Directory of Open Access Journals (Sweden)
Moreno-Pérez José A.
2005-01-01
Full Text Available In this paper we discuss the application of a met heuristic approach based on the Scatter Search to deal with robust optimization of the planning problem in the deploying of the Dense Wavelength Division Multiplexing (DWDM technology on an existing optical fiber network taking into account, in addition to the forecasted demands, the uncertainty in the survivability requirements.
Heuristic Search Theory and Applications
Edelkamp, Stefan
2011-01-01
Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed. Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constra
Directory of Open Access Journals (Sweden)
Chao-Chih Lin
2017-10-01
Full Text Available A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA and the symbiotic organism search (SOS to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN, are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS. Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.
A Graph Search Heuristic for Shortest Distance Paths
Energy Technology Data Exchange (ETDEWEB)
Chow, E
2005-03-24
This paper presents a heuristic for guiding A* search for finding the shortest distance path between two vertices in a connected, undirected, and explicitly stored graph. The heuristic requires a small amount of data to be stored at each vertex. The heuristic has application to quickly detecting relationships between two vertices in a large information or knowledge network. We compare the performance of this heuristic with breadth-first search on graphs with various topological properties. The results show that one or more orders of magnitude improvement in the number of vertices expanded is possible for large graphs, including Poisson random graphs.
Finding Multi-step Attacks in Computer Networks using Heuristic Search and Mobile Ambients
Nunes Leal Franqueira, V.
2009-01-01
An important aspect of IT security governance is the proactive and continuous identification of possible attacks in computer networks. This is complicated due to the complexity and size of networks, and due to the fact that usually network attacks are performed in several steps. This thesis proposes
Efficient heuristics for maximum common substructure search.
Englert, Péter; Kovács, Péter
2015-05-26
Maximum common substructure search is a computationally hard optimization problem with diverse applications in the field of cheminformatics, including similarity search, lead optimization, molecule alignment, and clustering. Most of these applications have strict constraints on running time, so heuristic methods are often preferred. However, the development of an algorithm that is both fast enough and accurate enough for most practical purposes is still a challenge. Moreover, in some applications, the quality of a common substructure depends not only on its size but also on various topological features of the one-to-one atom correspondence it defines. Two state-of-the-art heuristic algorithms for finding maximum common substructures have been implemented at ChemAxon Ltd., and effective heuristics have been developed to improve both their efficiency and the relevance of the atom mappings they provide. The implementations have been thoroughly evaluated and compared with existing solutions (KCOMBU and Indigo). The heuristics have been found to greatly improve the performance and applicability of the algorithms. The purpose of this paper is to introduce the applied methods and present the experimental results.
New Heuristic Algorithm for Dynamic Traffic in WDM Optical Networks
Directory of Open Access Journals (Sweden)
Arturo Benito Rodríguez Garcia
2015-12-01
Full Text Available The results and comparison of the simulation of a new heuristic algorithm called Snake One are presented. The comparison is made with three heuristic algorithms, Genetic Algorithms, Simulated Annealing, and Tabu Search, using blocking probability and network utilization as standard indicators. The simulation was made on the WDM NSFNET under dynamic traffic conditions. The results show a substantial decrease of blocking, but this causes a relative growth of network utilization. There are also load intervals at which its performance improves, decreasing the number of blocked requests.
Using Tabu Search Heuristics in Solving the Vehicle Routing ...
African Journals Online (AJOL)
Nafiisah
according to Glover, is a meta-heuristic that guides a local heuristic to explore the solution space beyond local optimality. Tabu Search starts just as an ordinary local search, proceeding iteratively from one solution to the next until some stopping criteria is satisfied while making use of some strategies to avoid getting trapped ...
A Heuristic Hierarchical Scheme for Academic Search and Retrieval
DEFF Research Database (Denmark)
Amolochitis, Emmanouil; Christou, Ioannis T.; Tan, Zheng-Hua
2013-01-01
We present PubSearch, a hybrid heuristic scheme for re-ranking academic papers retrieved from standard digital libraries such as the ACM Portal. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score...
Solving Large Clustering Problems with Meta-Heuristic Search
DEFF Research Database (Denmark)
Turkensteen, Marcel; Andersen, Kim Allan; Bang-Jensen, Jørgen
In Clustering Problems, groups of similar subjects are to be retrieved from data sets. In this paper, Clustering Problems with the frequently used Minimum Sum-of-Squares Criterion are solved using meta-heuristic search. Tabu search has proved to be a successful methodology for solving optimization...... problems, but applications to large clustering problems are rare. The simulated annealing heuristic has mainly been applied to relatively small instances. In this paper, we implement tabu search and simulated annealing approaches and compare them to the commonly used k-means approach. We find that the meta-heuristic...
Bayesian networks: a combined tuning heuristic
Bolt, J.H.
2016-01-01
One of the issues in tuning an output probability of a Bayesian network by changing multiple parameters is the relative amount of the individual parameter changes. In an existing heuristic parameters are tied such that their changes induce locally a maximal change of the tuned probability. This
Combined Heuristic Attack Strategy on Complex Networks
Directory of Open Access Journals (Sweden)
Marek Šimon
2017-01-01
Full Text Available Usually, the existence of a complex network is considered an advantage feature and efforts are made to increase its robustness against an attack. However, there exist also harmful and/or malicious networks, from social ones like spreading hoax, corruption, phishing, extremist ideology, and terrorist support up to computer networks spreading computer viruses or DDoS attack software or even biological networks of carriers or transport centers spreading disease among the population. New attack strategy can be therefore used against malicious networks, as well as in a worst-case scenario test for robustness of a useful network. A common measure of robustness of networks is their disintegration level after removal of a fraction of nodes. This robustness can be calculated as a ratio of the number of nodes of the greatest remaining network component against the number of nodes in the original network. Our paper presents a combination of heuristics optimized for an attack on a complex network to achieve its greatest disintegration. Nodes are deleted sequentially based on a heuristic criterion. Efficiency of classical attack approaches is compared to the proposed approach on Barabási-Albert, scale-free with tunable power-law exponent, and Erdős-Rényi models of complex networks and on real-world networks. Our attack strategy results in a faster disintegration, which is counterbalanced by its slightly increased computational demands.
Theory of Randomized Search Heuristics in Combinatorial Optimization
DEFF Research Database (Denmark)
such as time, money, or knowledge to obtain good specific algorithms. It is widely acknowledged that a solid mathematical foundation for such heuristics is needed. Most designers of RSHs, however, rather focused on mimicking processes in nature (such as evolution) rather than making the heuristics amenable......The rigorous mathematical analysis of randomized search heuristics(RSHs) with respect to their expected runtime is a growing research area where many results have been obtained in recent years. This class of heuristics includes well-known approaches such as Randomized Local Search (RLS...... to a mathematical analysis. This is different to the classical design of (randomized) algorithms which are developed with their theoretical analysis of runtime (and proof of correctness) in mind. Despite these obstacles, research from the last about 15 years has shown how to apply the methods for the probabilistic...
Heuristic Concentration and Tabu Search: A Nose to Nose Comparison
K.E. Rosing (Kenneth); C.S. ReVelle
1997-01-01
textabstractIn 1997 two papers applying the metaheuristics Tabu Search (TS) and Heuristic Concentration (HC) to the p-median problem were published in consecutive volumes of the European Journal of Operational Research. Here we apply the method of HC some of the data sets which were used for
Heuristics for Relevancy Ranking of Earth Dataset Search Results
Lynnes, C.; Quinn, P.; Norton, J.
2016-12-01
As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.
Concentrated Hitting Times of Randomized Search Heuristics with Variable Drift
DEFF Research Database (Denmark)
Lehre, Per Kristian; Witt, Carsten
2014-01-01
Drift analysis is one of the state-of-the-art techniques for the runtime analysis of randomized search heuristics (RSHs) such as evolutionary algorithms (EAs), simulated annealing etc. The vast majority of existing drift theorems yield bounds on the expected value of the hitting time for a target...
Heuristics for Relevancy Ranking of Earth Dataset Search Results
Lynnes, Christopher; Quinn, Patrick; Norton, James
2016-01-01
As the Variety of Earth science datasets increases, science researchers find it more challenging to discover and select the datasets that best fit their needs. The most common way of search providers to address this problem is to rank the datasets returned for a query by their likely relevance to the user. Large web page search engines typically use text matching supplemented with reverse link counts, semantic annotations and user intent modeling. However, this produces uneven results when applied to dataset metadata records simply externalized as a web page. Fortunately, data and search provides have decades of experience in serving data user communities, allowing them to form heuristics that leverage the structure in the metadata together with knowledge about the user community. Some of these heuristics include specific ways of matching the user input to the essential measurements in the dataset and determining overlaps of time range and spatial areas. Heuristics based on the novelty of the datasets can prioritize later, better versions of data over similar predecessors. And knowledge of how different user types and communities use data can be brought to bear in cases where characteristics of the user (discipline, expertise) or their intent (applications, research) can be divined. The Earth Observing System Data and Information System has begun implementing some of these heuristics in the relevancy algorithm of its Common Metadata Repository search engine.
Complex Sequencing Problems and Local Search Heuristics
Brucker, P.; Hurink, Johann L.; Osman, I.H.; Kelly, J.P.
1996-01-01
Many problems can be formulated as complex sequencing problems. We will present problems in flexible manufacturing that have such a formulation and apply local search methods like iterative improvement, simulated annealing and tabu search to solve these problems. Computational results are reported.
Heuristic approach to the passive optical network with fibre duct ...
African Journals Online (AJOL)
Integer programming, network flow optimisation, passive optical network, ... algorithm before providing a greedy planning heuristic [11]. The multi- ... A wide range of meta-heuristics have also been employed to solve PONPP, with genetic ... In the case of PONPP, the objective is to find a subset of open facilities F, with every.
Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks
Directory of Open Access Journals (Sweden)
Yuquan Guo
2017-01-01
Full Text Available Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.
Efficient Heuristics for Simulating Population Overflow in Parallel Networks
Zaburnenko, T.S.; Nicola, V.F.
2006-01-01
In this paper we propose a state-dependent importance sampling heuristic to estimate the probability of population overflow in networks of parallel queues. This heuristic approximates the “optimal��? state-dependent change of measure without the need for costly optimization involved in other
Local search heuristics for the probabilistic dial-a-ride problem
DEFF Research Database (Denmark)
Ho, Sin C.; Haugland, Dag
2011-01-01
evaluation procedure in a pure local search heuristic and in a tabu search heuristic. The quality of the solutions obtained by the two heuristics have been compared experimentally. Computational results confirm that our neighborhood evaluation technique is much faster than the straightforward one...
Impact of heuristics in clustering large biological networks.
Shafin, Md Kishwar; Kabir, Kazi Lutful; Ridwan, Iffatur; Anannya, Tasmiah Tamzid; Karim, Rashid Saadman; Hoque, Mohammad Mozammel; Rahman, M Sohel
2015-12-01
Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising. Copyright © 2015 Elsevier Ltd. All rights reserved.
Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.
Rappoport, Dmitrij; Galvin, Cooper J; Zubarev, Dmitry Yu; Aspuru-Guzik, Alán
2014-03-11
While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.
A comparative study of the A* heuristic search algorithm used to solve efficiently a puzzle game
Iordan, A. E.
2018-01-01
The puzzle game presented in this paper consists in polyhedra (prisms, pyramids or pyramidal frustums) which can be moved using the free available spaces. The problem requires to be found the minimum number of movements in order the game reaches to a goal configuration starting from an initial configuration. Because the problem is enough complex, the principal difficulty in solving it is given by dimension of search space, that leads to necessity of a heuristic search. The improving of the search method consists into determination of a strong estimation by the heuristic function which will guide the search process to the most promising side of the search tree. The comparative study is realized among Manhattan heuristic and the Hamming heuristic using A* search algorithm implemented in Java. This paper also presents the necessary stages in object oriented development of a software used to solve efficiently this puzzle game. The modelling of the software is achieved through specific UML diagrams representing the phases of analysis, design and implementation, the system thus being described in a clear and practical manner. With the purpose to confirm the theoretical results which demonstrates that Manhattan heuristic is more efficient was used space complexity criterion. The space complexity was measured by the number of generated nodes from the search tree, by the number of the expanded nodes and by the effective branching factor. From the experimental results obtained by using the Manhattan heuristic, improvements were observed regarding space complexity of A* algorithm versus Hamming heuristic.
A hybrid adaptive large neighborhood search heuristic for lot-sizing with setup times
DEFF Research Database (Denmark)
Muller, Laurent Flindt; Spoorendonk, Simon; Pisinger, David
2012-01-01
This paper presents a hybrid of a general heuristic framework and a general purpose mixed-integer programming (MIP) solver. The framework is based on local search and an adaptive procedure which chooses between a set of large neighborhoods to be searched. A mixed integer programming solver and its...... built-in feasibility heuristics is used to search a neighborhood for improving solutions. The general reoptimization approach used for repairing solutions is specifically suited for combinatorial problems where it may be hard to otherwise design suitable repair neighborhoods. The hybrid heuristic...... framework is applied to the multi-item capacitated lot sizing problem with setup times, where experiments have been conducted on a series of instances from the literature and a newly generated extension of these. On average the presented heuristic outperforms the best heuristics from the literature...
Proximity search heuristics for wind farm optimal layout
DEFF Research Database (Denmark)
Fischetti, Martina; Monaci, Michele
2016-01-01
A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming. In our framework, large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently...
Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem.
Nallaperuma, Samadhi; Neumann, Frank; Sudholt, Dirk
2017-01-01
Randomized search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to our theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed-time budget. We follow this approach and present a fixed-budget analysis for an NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson Problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed-time budget. In particular, we analyze Manhattan and Euclidean TSP instances and Randomized Local Search (RLS), (1+1) EA and (1+[Formula: see text]) EA algorithms for the TSP in a smoothed complexity setting, and derive the lower bounds of the expected fitness gain for a specified number of generations.
Analysis of complex network performance and heuristic node removal strategies
Jahanpour, Ehsan; Chen, Xin
2013-12-01
Removing important nodes from complex networks is a great challenge in fighting against criminal organizations and preventing disease outbreaks. Six network performance metrics, including four new metrics, are applied to quantify networks' diffusion speed, diffusion scale, homogeneity, and diameter. In order to efficiently identify nodes whose removal maximally destroys a network, i.e., minimizes network performance, ten structured heuristic node removal strategies are designed using different node centrality metrics including degree, betweenness, reciprocal closeness, complement-derived closeness, and eigenvector centrality. These strategies are applied to remove nodes from the September 11, 2001 hijackers' network, and their performance are compared to that of a random strategy, which removes randomly selected nodes, and the locally optimal solution (LOS), which removes nodes to minimize network performance at each step. The computational complexity of the 11 strategies and LOS is also analyzed. Results show that the node removal strategies using degree and betweenness centralities are more efficient than other strategies.
Deterministic oscillatory search: a new meta-heuristic optimization ...
Indian Academy of Sciences (India)
heuristic optimization; power system problem. Abstract. The paper proposes a new optimization algorithm that is extremely robust in solving mathematical and engineering problems. The algorithm combines the deterministic nature of classical ...
Benchmarking Heuristic Search and Optimisation Algorithms in Matlab
Luo, Wuqiao; Li, Yun
2016-01-01
With the proliferating development of heuristic methods, it has become challenging to choose the most suitable ones for an application at hand. This paper evaluates the performance of these algorithms available in Matlab, as it is problem dependent and parameter sensitive. Further, the paper attempts to address the challenge that there exists no satisfied benchmarks to evaluation all the algorithms at the same standard. The paper tests five heuristic algorithms in Matlab, the Nelder-Mead simp...
Heuristic urban transportation network design method, a multilayer coevolution approach
Ding, Rui; Ujang, Norsidah; Hamid, Hussain bin; Manan, Mohd Shahrudin Abd; Li, Rong; Wu, Jianjun
2017-08-01
The design of urban transportation networks plays a key role in the urban planning process, and the coevolution of urban networks has recently garnered significant attention in literature. However, most of these recent articles are based on networks that are essentially planar. In this research, we propose a heuristic multilayer urban network coevolution model with lower layer network and upper layer network that are associated with growth and stimulate one another. We first use the relative neighbourhood graph and the Gabriel graph to simulate the structure of rail and road networks, respectively. With simulation we find that when a specific number of nodes are added, the total travel cost ratio between an expanded network and the initial lower layer network has the lowest value. The cooperation strength Λ and the changeable parameter average operation speed ratio Θ show that transit users' route choices change dramatically through the coevolution process and that their decisions, in turn, affect the multilayer network structure. We also note that the simulated relation between the Gini coefficient of the betweenness centrality, Θ and Λ have an optimal point for network design. This research could inspire the analysis of urban network topology features and the assessment of urban growth trends.
An Approach to Optical Network Design using General Heuristic Optimization Framework
Directory of Open Access Journals (Sweden)
Marko Lacković
2010-12-01
Full Text Available The article tackles the problem of optimization methods in optical network design process, based on optimal traffic routing with the goal to minimize the utilized network resources for given topology and traffic demands. An optimization framework Nyx has been developed with the focus on flexibility in solving optimization problems by implementing general heuristic search techniques. Nyx modular organization has been described, including coding types for solutions and genetic algorithm as the optimization method. Optimal routing has been implemented to demonstrate the use of Nyx in the optical network design process. Optimal routing procedure has been applied to Pan-European optical network with variations of routing procedures and the number of wavelengths. The analysis included no protection scenario, 1+1 protection and path restoration. The routing was performed using shortest path routing and optimal routing which minimizes the use of optical network resources, being network multiplexers, amplifiers and fibers.
Heuristic Strategies for Persuader Selection in Contagions on Complex Networks.
Wang, Peng; Zhang, Li-Jie; Xu, Xin-Jian; Xiao, Gaoxi
2017-01-01
Individual decision to accept a new idea or product is often driven by both self-adoption and others' persuasion, which has been simulated using a double threshold model [Huang et al., Scientific Reports 6, 23766 (2016)]. We extend the study to consider the case with limited persuasion. That is, a set of individuals is chosen from the population to be equipped with persuasion capabilities, who may succeed in persuading their friends to take the new entity when certain conditions are satisfied. Network node centrality is adopted to characterize each node's influence, based on which three heuristic strategies are applied to pick out persuaders. We compare these strategies for persuader selection on both homogeneous and heterogeneous networks. Two regimes of the underline networks are identified in which the system exhibits distinct behaviors: when networks are sufficiently sparse, selecting persuader nodes in descending order of node centrality achieves the best performance; when networks are sufficiently dense, however, selecting nodes with medium centralities to serve as the persuaders performs the best. Under respective optimal strategies for different types of networks, we further probe which centrality measure is most suitable for persuader selection. It turns out that for the first regime, degree centrality offers the best measure for picking out persuaders from homogeneous networks; while in heterogeneous networks, betweenness centrality takes its place. In the second regime, there is no significant difference caused by centrality measures in persuader selection for homogeneous network; while for heterogeneous networks, closeness centrality offers the best measure.
Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks
Lee, Charles H.; Cheung, Kar-Ming
2012-01-01
In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.
A hybrid heuristic ordering and variable neighbourhood search for the nurse rostering problem
Burke, E.K.; Curtois, T.; Post, Gerhard F.; Qu, R.; Veltman, Bart
This paper is concerned with the development of intelligent decision support methodologies for nurse rostering problems in large modern hospital environments. We present an approach which hybridises heuristic ordering with variable neighbourhood search. We show that the search can be extended and
A hybrid heuristic ordering and Variable Neighbourhood Search for the nurse rostering problem
Burke, Edmund; Curtois, Timothy; De Causmaecker, P.; Post, Gerhard F.; van den Berghe, G.; Trick, M.A.; Burke, E.K.
2004-01-01
This paper is concerned with the development of intelligent decision support methodologies for nurse rostering problems in large modern hospital environments. We present an approach which hybridises heuristic ordering with variable neighbourhood search. We show that the search can be extended and
The Impact of Parametrization on Randomized Search Heuristics
DEFF Research Database (Denmark)
Gießen, Christian
optimization algorithms. We consider the following topics. Escaping local optima using local search. We analyze memetic algorithms, i.e. evolutionary algorithms equipped with a local search after mutation. To this end we consider the (1+1) EA equipped with Standard Local Search (SLS) and Variable-Depth Search...
Blumenthal-Barby, J S; Krieger, Heather
2015-05-01
The role of cognitive biases and heuristics in medical decision making is of growing interest. The purpose of this study was to determine whether studies on cognitive biases and heuristics in medical decision making are based on actual or hypothetical decisions and are conducted with populations that are representative of those who typically make the medical decision; to categorize the types of cognitive biases and heuristics found and whether they are found in patients or in medical personnel; and to critically review the studies based on standard methodological quality criteria. Data sources were original, peer-reviewed, empirical studies on cognitive biases and heuristics in medical decision making found in Ovid Medline, PsycINFO, and the CINAHL databases published in 1980-2013. Predefined exclusion criteria were used to identify 213 studies. During data extraction, information was collected on type of bias or heuristic studied, respondent population, decision type, study type (actual or hypothetical), study method, and study conclusion. Of the 213 studies analyzed, 164 (77%) were based on hypothetical vignettes, and 175 (82%) were conducted with representative populations. Nineteen types of cognitive biases and heuristics were found. Only 34% of studies (n = 73) investigated medical personnel, and 68% (n = 145) confirmed the presence of a bias or heuristic. Each methodological quality criterion was satisfied by more than 50% of the studies, except for sample size and validated instruments/questions. Limitations are that existing terms were used to inform search terms, and study inclusion criteria focused strictly on decision making. Most of the studies on biases and heuristics in medical decision making are based on hypothetical vignettes, raising concerns about applicability of these findings to actual decision making. Biases and heuristics have been underinvestigated in medical personnel compared with patients. © The Author(s) 2014.
Heuristic approach to the passive optical network with fibre duct ...
African Journals Online (AJOL)
PON) planning problem necessitates the search for a subset of deployed facilities (splitters) and their allocated demand points (optical network units) to minimise the overall deployment cost. A mixed integer linear programming formulation ...
A hybrid shifting bottleneck-tabu search heuristic for the job shop total weighted tardiness problem
Bülbül, Kerem; Bulbul, Kerem
2010-01-01
In this paper, we study the job shop scheduling problem with the objective of minimizing the total weighted tardiness. We propose a hybrid shifting bottleneck - tabu search (SB-TS) algorithm by replacing the reoptimization step in the shifting bottleneck (SB) algorithm by a tabu search (TS). In terms of the shifting bottleneck heuristic, the proposed tabu search optimizes the total weighted tardiness for partial schedules in which some machines are currently assumed to have infinite capacity...
Effective speeding up strategies for tabu search heuristics
Lai, David S.W.; Dullaert, Wout
2015-01-01
Tabu search metaheuristics have been developed for decades, making them one of the most widely applied metaheuristic frameworks. Although a standard tabu search framework can offer interesting performance, the effectiveness of a tabu search metaheuristic can be strongly improved by specific
A reduced-cost iterated local search heuristic for the fixed-charge transportation problem
Buson, Erika; Roberti, Roberto; Toth, Paolo
2014-01-01
The fixed-charge transportation problem (FCTP) is a generalization of the transportation problem where an additional fixed cost is paid for sending a flow from an origin to a destination. We propose an iterated local search heuristic based on the utilization of reduced costs for guiding the restart
Fitness levels with tail bounds for the analysis of randomized search heuristics
DEFF Research Database (Denmark)
Witt, Carsten
2014-01-01
The fitness-level method, also called the method of f-based partitions, is an intuitive and widely used technique for the running time analysis of randomized search heuristics. It was originally defined to prove upper and lower bounds on the expected running time. Recently, upper tail bounds were...
Optimizing Linear Functions with Randomized Search Heuristics - The Robustness of Mutation
DEFF Research Database (Denmark)
Witt, Carsten
2012-01-01
The analysis of randomized search heuristics on classes of functions is fundamental for the understanding of the underlying stochastic process and the development of suitable proof techniques. Recently, remarkable progress has been made in bounding the expected optimization time of the simple (1...
Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions
DEFF Research Database (Denmark)
Witt, Carsten
2013-01-01
The analysis of randomized search heuristics on classes of functions is fundamental to the understanding of the underlying stochastic process and the development of suitable proof techniques. Recently, remarkable progress has been made in bounding the expected optimization time of a simple...
DEFF Research Database (Denmark)
Wen, Min; Krapper, Emil; Larsen, Jesper
2011-01-01
. The objective is to minimize the total delivery cost that is a weighted sum of two kinds of delivery costs. A multilevel variable neighborhood search heuristic is proposed for the problem. In a preprocessing step, the problem size is reduced through an aggregation procedure. Thereafter, the aggregated weekly...
Using Tabu Search Heuristics in Solving the Vehicle Routing ...
African Journals Online (AJOL)
The Tabu Search algorithm has been applied to a real life situation for a Mauritian company. The optimal routes of the delivery of frozen chicken were found and it was then possible to minimize the cost of transport within the required the time constraint. Keywords: Vehicle routing Problem, Tabu Search method ...
A greedy construction heuristic for the liner service network design problem
DEFF Research Database (Denmark)
Brouer, Berit Dangaard
is challenging due to the size of a global liner shipping operation and due to the hub-and-spoke network design, where a high percentage of the total cargo is transshipped. We present the first construction heuristic for large scale instances of the LSN-DP. The heuristic is able to find a solution for a real...
Nicola, V.F.; Zaburnenko, T.S.
2006-01-01
In this paper we propose a state-dependent importance sampling heuristic to estimate the probability of population overï¬‚ow in feed-forward networks. This heuristic attempts to approximate the â€œoptimalï¿½ï¿½? state-dependent change of measure without the need for difficult analysis or costly
Directory of Open Access Journals (Sweden)
Alexander Safatli
2015-06-01
Full Text Available Summary. Pylogeny is a cross-platform library for the Python programming language that provides an object-oriented application programming interface for phylogenetic heuristic searches. Its primary function is to permit both heuristic search and analysis of the phylogenetic tree search space, as well as to enable the design of novel algorithms to search this space. To this end, the framework supports the structural manipulation of phylogenetic trees, in particular using rearrangement operators such as NNI, SPR, and TBR, the scoring of trees using parsimony and likelihood methods, the construction of a tree search space graph, and the programmatic execution of a few existing heuristic programs. The library supports a range of common phylogenetic file formats and can be used for both nucleotide and protein data. Furthermore, it is also capable of supporting GPU likelihood calculation on nucleotide character data through the BEAGLE library.Availability. Existing development and source code is available for contribution and for download by the public from GitHub (http://github.com/AlexSafatli/Pylogeny. A stable release of this framework is available for download through PyPi (Python Package Index at http://pypi.python.org/pypi/pylogeny.
Deterministic oscillatory search: a new meta-heuristic optimization ...
Indian Academy of Sciences (India)
N Archana
comb =find all possible combinations (pCk), where comb is the array to store the combinations, p is the number of dimensions of the search space for c=1: total number of combinations. Change parameter positions of the particle Xi as high values as per the combinations, so that all positions are unique. i++ end for k ++.
Tabu search heuristic for university course timetabling problem ...
African Journals Online (AJOL)
In this study we have addressed the NP-Hard problem of academic course timetabling. This is the problem of assigning resources such as lecturers, rooms and courses to a fixed time period normally a week, while satisfying a number of problem-specific constraints. This paper describes a Tabu Search algorithm that creates ...
Searching social networks for subgraph patterns
Ogaard, Kirk; Kase, Sue; Roy, Heather; Nagi, Rakesh; Sambhoos, Kedar; Sudit, Moises
2013-06-01
Software tools for Social Network Analysis (SNA) are being developed which support various types of analysis of social networks extracted from social media websites (e.g., Twitter). Once extracted and stored in a database such social networks are amenable to analysis by SNA software. This data analysis often involves searching for occurrences of various subgraph patterns (i.e., graphical representations of entities and relationships). The authors have developed the Graph Matching Toolkit (GMT) which provides an intuitive Graphical User Interface (GUI) for a heuristic graph matching algorithm called the Truncated Search Tree (TruST) algorithm. GMT is a visual interface for graph matching algorithms processing large social networks. GMT enables an analyst to draw a subgraph pattern by using a mouse to select categories and labels for nodes and links from drop-down menus. GMT then executes the TruST algorithm to find the top five occurrences of the subgraph pattern within the social network stored in the database. GMT was tested using a simulated counter-insurgency dataset consisting of cellular phone communications within a populated area of operations in Iraq. The results indicated GMT (when executing the TruST graph matching algorithm) is a time-efficient approach to searching large social networks. GMT's visual interface to a graph matching algorithm enables intelligence analysts to quickly analyze and summarize the large amounts of data necessary to produce actionable intelligence.
The Use of Parallelism to Implement a Heuristic Search.
1981-03-01
ut li~mm’v. k’ or at conventionail weijuential colputcr. I hie reinder of this pitper consists ofa discussion of’ theC piohlcin being sol ’.ed and...plain. T he parallel sol ut ion supplies "insu ra nce" ai ist picking bad branches in the search space. If the *sequenC~tial solution1 hlappened to...Algorithms, Carnegie-Mellon ,epoit CM -_"- M’- 124, May 1979. 141 lewitt , Carl, Design of tMe AIIAR Y for Actor ,sti:ms, Proceedings of the 1980 LIsp
Local search-based heuristics for the multiobjective multidimensional knapsack problem
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Dalessandro Soares Vianna
2012-01-01
Full Text Available In real optimization problems it is generally desirable to optimize more than one performance criterion (or objective at the same time. The goal of the multiobjective combinatorial optimization (MOCO is to optimize simultaneously r > 1 objectives. As in the single-objective case, the use of heuristic/metaheuristic techniques seems to be the most promising approach to MOCO problems because of their efficiency, generality and relative simplicity of implementation. In this work, we develop algorithms based on Greedy Randomized Adaptive Search Procedure (GRASP and Iterated Local Search (ILS metaheuristics for the multiobjective knapsack problem. Computational experiments on benchmark instances show that the proposed algorithms are very robust and outperform other heuristics in terms of solution quality and running times.
A Library of Local Search Heuristics for the Vehicle Routing Problem
Energy Technology Data Exchange (ETDEWEB)
Groer, Christopher S [ORNL; Golden, Bruce [University of Maryland; Edward, Wasil [American University
2010-01-01
The vehicle routing problem (VRP) is a difficult and well-studied combinatorial optimization problem. Real-world instances of the VRP can contain hundreds and even thousands of customer locations and can involve many complicating constraints, necessitating the use of heuristic methods. We present a software library of local search heuristics that allow one to quickly generate good solutions to VRP instances. The code has a logical, object-oriented design and uses efficient data structures to store and modify solutions. The core of the library is the implementation of seven local search operators that share a similar interface and are designed to be extended to handle additional options with minimal code change. The code is well-documented, is straightforward to compile, and is freely available for download at http://sites.google.com/site/vrphlibrary/ . The distribution of the code contains several applications that can be used to generate solutions to instances of the capacitated VRP.
A tabu-search heuristic for solving the multi-depot vehicle scheduling problem
Directory of Open Access Journals (Sweden)
Gilmar D'Agostini Oliveira Casalinho
2014-08-01
Full Text Available Currently the logistical problems are relying quite significantly on Operational Research in order to achieve greater efficiency in their operations. Among the problems related to the vehicles scheduling in a logistics system, the Multiple Depot Vehicle Scheduling Problem (MDVSP has been addressed in several studies. The MDVSP presupposes the existence of depots that affect the planning of sequences to which travel must be performed. Often, exact methods cannot solve large instances encountered in practice and in order to take them into account, several heuristic approaches are being developed. The aim of this study was thus to solve the MDVSP using a meta-heuristic based on tabu-search method. The main motivation for this work came from the indication that only recently the use of meta-heuristics is being applied to MDVSP context (Pepin et al. 2008 and, also, the limitations listed by Rohde (2008 in his study, which used the branch-and-bound in one of the steps of the heuristic presented to solve the problem, which has increased the time resolution. The research method for solving this problem was based on adaptations of traditional techniques of Operational Research, and provided resolutions presenting very competitive results for the MDVSP such as the cost of the objective function, number of vehicles used and computational time.
A hop count based heuristic routing protocol for mobile delay tolerant networks.
You, Lei; Li, Jianbo; Wei, Changjiang; Dai, Chenqu; Xu, Jixing; Hu, Lejuan
2014-01-01
Routing in delay tolerant networks (DTNs) is a challenge since it must handle network partitioning, long delays, and dynamic topology. Meanwhile, routing protocols of the traditional mobile ad hoc networks (MANETs) cannot work well due to the failure of its assumption that most network connections are available. In this paper, we propose a hop count based heuristic routing protocol by utilizing the information carried by the peripatetic packets in the network. A heuristic function is defined to help in making the routing decision. We formally define a custom operation for square matrices so as to transform the heuristic value calculation into matrix manipulation. Finally, the performance of our proposed algorithm is evaluated by the simulation results, which show the advantage of such self-adaptive routing protocol in the diverse circumstance of DTNs.
Energy Technology Data Exchange (ETDEWEB)
Brandt, Christopher; Fieg, Georg [Hamburg University of Technology, Institute of Process and Plant Engineering, Hamburg (Germany); Luo, Xing [Helmut Schmidt University, Institute of Thermodynamics, Hamburg (Germany); University of Shanghai for Science and Technology, Institute of Thermal Engineering, Shanghai (China)
2011-08-15
In this work an innovative method for heat exchanger network (HEN) synthesis is introduced and examined. It combines a genetic algorithm (GA) with a heuristic based optimization procedure. The novel algorithm removes appearing heat load loops from the HEN structures when profitable, throughout the evolution. Two examples were examined with the new HEN synthesis method and for both better results were obtained. Thus, a positive effect of heuristic based optimization methods on the HEN synthesis with GA could be located. (orig.)
Xu, Huafeng; Agrafiotis, Dimitris K
2003-01-01
We present a new algorithm for nearest neighbor search in general metric spaces. The algorithm organizes the database into recursively partitioned Voronoi regions and represents these partitions in a tree. The separations between the Voronoi regions as well as the radius of each region are used with triangular inequality to derive the minimum possible distance between any point in a region and the query and to discard the region from further search if a smaller distance has already been found. The algorithm also orders the search sequence of the tree branches using the estimate of the minimum possible distance. This simple heuristic proves to considerably enhance the pruning of the search tree. The efficiency of the algorithm is demonstrated on several artificial data sets and real problems in computational chemistry.
Directory of Open Access Journals (Sweden)
Qiang Zhu
2015-01-01
Full Text Available The reliable mapping of virtual networks is one of the hot issues in network virtualization researches. Unlike the traditional protection mechanisms based on redundancy and recovery mechanisms, we take the solution of the survivable virtual topology routing problem for reference to ensure that the rest of the mapped virtual networks keeps connected under a single node failure condition in the substrate network, which guarantees the completeness of the virtual network and continuity of services. In order to reduce the cost of the substrate network, a hybrid reliable heuristic mapping method based on survivable virtual networks (Hybrid-RHM-SVN is proposed. In Hybrid-RHM-SVN, we formulate the reliable mapping problem as an integer linear program. Firstly, we calculate the primary-cut set of the virtual network subgraph where the failed node has been removed. Then, we use the ant colony optimization algorithm to achieve the approximate optimal mapping. The links in primary-cut set should select a substrate path that does not pass through the substrate node corresponding to the virtual node that has been removed first. The simulation results show that the acceptance rate of virtual networks, the average revenue of mapping, and the recovery rate of virtual networks are increased compared with the existing reliable mapping algorithms, respectively.
A dichotomous search-based heuristic for the three-dimensional sphere packing problem
Directory of Open Access Journals (Sweden)
Mhand Hifi
2015-12-01
Full Text Available In this paper, the three-dimensional sphere packing problem is solved by using a dichotomous search-based heuristic. An instance of the problem is defined by a set of $ n $ unequal spheres and an object of fixed width and height and, unlimited length. Each sphere is characterized by its radius and the aim of the problem is to optimize the length of the object containing all spheres without overlapping. The proposed method is based upon beam search, in which three complementary phases are combined: (i a greedy selection phase which determines a series of eligible search subspace, (ii a truncated tree search, using a width-beam search, that explores some promising paths, and (iii a dichotomous search that diversifies the search. The performance of the proposed method is evaluated on benchmark instances taken from the literature where its obtained results are compared to those reached by some recent methods of the literature. The proposed method is competitive and it yields promising results.
Hierarchical heuristic search using a Gaussian mixture model for UAV coverage planning.
Lin, Lanny; Goodrich, Michael A
2014-12-01
During unmanned aerial vehicle (UAV) search missions, efficient use of UAV flight time requires flight paths that maximize the probability of finding the desired subject. The probability of detecting the desired subject based on UAV sensor information can vary in different search areas due to environment elements like varying vegetation density or lighting conditions, making it likely that the UAV can only partially detect the subject. This adds another dimension of complexity to the already difficult (NP-Hard) problem of finding an optimal search path. We present a new class of algorithms that account for partial detection in the form of a task difficulty map and produce paths that approximate the payoff of optimal solutions. The algorithms use the mode goodness ratio heuristic that uses a Gaussian mixture model to prioritize search subregions. The algorithms search for effective paths through the parameter space at different levels of resolution. We compare the performance of the new algorithms against two published algorithms (Bourgault's algorithm and LHC-GW-CONV algorithm) in simulated searches with three real search and rescue scenarios, and show that the new algorithms outperform existing algorithms significantly and can yield efficient paths that yield payoffs near the optimal.
Analysis of utility-theoretic heuristics for intelligent adaptive network routing
Energy Technology Data Exchange (ETDEWEB)
Mikler, A.R.; Honavar, V.; Wong, J.S.K. [Iowa State Univ., Ames, IA (United States)
1996-12-31
Utility theory offers an elegant and powerful theoretical framework for design and analysis of autonomous adaptive communication networks. Routing of messages in such networks presents a real-time instance of a multi-criterion optimization problem in a dynamic and uncertain environment. In this paper, we incrementally develop a set of heuristic decision functions that can be used to guide messages along a near-optimal (e.g., minimum delay) path in a large network. We present an analysis of properties of such heuristics under a set of simplifying assumptions about the network topology and load dynamics and identify the conditions under which they are guaranteed to route messages along an optimal path. The paper concludes with a discussion of the relevance of the theoretical results presented in the paper to the design of intelligent autonomous adaptive communication networks and an outline of some directions of future research.
Searching association networks for nurturers
Mohan, BK
2005-01-01
Studying the evolution of association networks offers insights that researchers can use to develop new forms of Web information retrieval and improve searches. In addition to finding nurturers, this work can be applied to targeted recommendations, human resource management, and social network analysis.
Eberbach, Andreas; Becker, Annette; Rochon, Justine; Finkemeler, Holger; Wagner, Achim; Donner-Banzhoff, Norbert
2016-01-01
General practitioners (GPs) are confronted with a wide variety of clinical questions, many of which remain unanswered. In order to assist GPs in finding quick, evidence-based answers, we developed a learning program (LP) with a short interactive workshop based on a simple three-step-heuristic to improve their search and appraisal competence (SAC). We evaluated the LP effectiveness with a randomized controlled trial (RCT). Participants (intervention group [IG] n=20; control group [CG] n=31) rated acceptance and satisfaction and also answered 39 knowledge questions to assess their SAC. We controlled for previous knowledge in content areas covered by the test. Main outcome - SAC: within both groups, the pre-post test shows significant (P=0.00) improvements in correctness (IG 15% vs CG 11%) and confidence (32% vs 26%) to find evidence-based answers. However, the SAC difference was not significant in the RCT. Most workshop participants rated "learning atmosphere" (90%), "skills acquired" (90%), and "relevancy to my practice" (86%) as good or very good. The LP-recommendations were implemented by 67% of the IG, whereas 15% of the CG already conformed to LP recommendations spontaneously (odds ratio 9.6, P=0.00). After literature search, the IG showed a (not significantly) higher satisfaction regarding "time spent" (IG 80% vs CG 65%), "quality of information" (65% vs 54%), and "amount of information" (53% vs 47%). Long-standing established GPs have a good SAC. Despite high acceptance, strong learning effects, positive search experience, and significant increase of SAC in the pre-post test, the RCT of our LP showed no significant difference in SAC between IG and CG. However, we suggest that our simple decision heuristic merits further investigation.
FocusHeuristics - expression-data-driven network optimization and disease gene prediction.
Ernst, Mathias; Du, Yang; Warsow, Gregor; Hamed, Mohamed; Endlich, Nicole; Endlich, Karlhans; Murua Escobar, Hugo; Sklarz, Lisa-Madeleine; Sender, Sina; Junghanß, Christian; Möller, Steffen; Fuellen, Georg; Struckmann, Stephan
2017-02-16
To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.
Sun, Jian
2010-09-01
A new methodology for adapting rigorous simulation programs to optimal supervisory control of a central chilled water plant is proposed in this article, which solves plant operation mode optimization and set points optimization by combining heuristic search with sequential quadratic programming. The mathematical basis of this algorithm is developed. A new derivative calculation strategy is introduced in set points optimization. This approach is applied to a central chilled water plant which consists of three chillers, two 3-cell cooling towers, three chilled water pumps and three condenser water pumps. Model verification study is performed. The optimal sequence of operation, set points of the decision variables at given load demand and weather condition are calculated. The plant performance and optimal control results are discussed.
Mignon, David; Simonson, Thomas
2016-07-15
Computational protein design depends on an energy function and an algorithm to search the sequence/conformation space. We compare three stochastic search algorithms: a heuristic, Monte Carlo (MC), and a Replica Exchange Monte Carlo method (REMC). The heuristic performs a steepest-descent minimization starting from thousands of random starting points. The methods are applied to nine test proteins from three structural families, with a fixed backbone structure, a molecular mechanics energy function, and with 1, 5, 10, 20, 30, or all amino acids allowed to mutate. Results are compared to an exact, "Cost Function Network" method that identifies the global minimum energy conformation (GMEC) in favorable cases. The designed sequences accurately reproduce experimental sequences in the hydrophobic core. The heuristic and REMC agree closely and reproduce the GMEC when it is known, with a few exceptions. Plain MC performs well for most cases, occasionally departing from the GMEC by 3-4 kcal/mol. With REMC, the diversity of the sequences sampled agrees with exact enumeration where the latter is possible: up to 2 kcal/mol above the GMEC. Beyond, room temperature replicas sample sequences up to 10 kcal/mol above the GMEC, providing thermal averages and a solution to the inverse protein folding problem. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.
A Decentralized Heuristic Approach towards Resource Allocation in Femtocell Networks
Directory of Open Access Journals (Sweden)
Kyung-Geun Lee
2013-06-01
Full Text Available Femtocells represent a novel configuration for existing cellular communication, contributing towards the improvement of coverage and throughput. The dense deployment of these femtocells causes significant femto-macro and femto-femto interference, consequently deteriorating the throughput of femtocells. In this study, we compare two heuristic approaches, i.e., particle swarm optimization (PSO and genetic algorithm (GA, for joint power assignment and resource allocation, within the context of the femtocell environment. The supposition made in this joint optimization is that the discrete power levels are available for the assignment. Furthermore, we have employed two variants of each PSO and GA: inertia weight and constriction factor model for PSO, and twopoint and uniform crossover for GA. The two proposed algorithms are in a decentralized manner, with no involvement of any centralized entity. The comparison is carried out between the two proposed algorithms for the aforementioned joint optimization problem. The contrast includes the performance metrics: including average objective function, min–max throughput of the femtocells, average throughput of the femto users, outage rate and time complexity. The results demonstrate that the decentralized PSO constriction factor outperforms the others in terms of the aforementioned performance metrics.
Qin, Junping; Sun, Shiwen; Deng, Qingxu; Liu, Limin; Tian, Yonghong
2017-06-02
Object tracking and detection is one of the most significant research areas for wireless sensor networks. Existing indoor trajectory tracking schemes in wireless sensor networks are based on continuous localization and moving object data mining. Indoor trajectory tracking based on the received signal strength indicator (RSSI) has received increased attention because it has low cost and requires no special infrastructure. However, RSSI tracking introduces uncertainty because of the inaccuracies of measurement instruments and the irregularities (unstable, multipath, diffraction) of wireless signal transmissions in indoor environments. Heuristic information includes some key factors for trajectory tracking procedures. This paper proposes a novel trajectory tracking scheme based on Delaunay triangulation and heuristic information (TTDH). In this scheme, the entire field is divided into a series of triangular regions. The common side of adjacent triangular regions is regarded as a regional boundary. Our scheme detects heuristic information related to a moving object's trajectory, including boundaries and triangular regions. Then, the trajectory is formed by means of a dynamic time-warping position-fingerprint-matching algorithm with heuristic information constraints. Field experiments show that the average error distance of our scheme is less than 1.5 m, and that error does not accumulate among the regions.
Heuristic approach to the passive optical network with fibre duct ...
African Journals Online (AJOL)
PON) plan- ning problem ... This paper uses concepts from network flow optimisation to incorporate fibre duct shar- ing into a ...... 682–686. [31] Yen JY, 1971, Finding the k shortest loopless paths in a network, Management Science, 17(11), pp.
A simple heuristic for Internet-based evidence search in primary care: a randomized controlled trial
Directory of Open Access Journals (Sweden)
Eberbach A
2016-08-01
Full Text Available Andreas Eberbach,1 Annette Becker,1 Justine Rochon,2 Holger Finkemeler,1Achim Wagner,3 Norbert Donner-Banzhoff1 1Department of Family and Community Medicine, Philipp University of Marburg, Marburg, Germany; 2Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany; 3Department of Sport Medicine, Justus-Liebig-University of Giessen, Giessen, Germany Background: General practitioners (GPs are confronted with a wide variety of clinical questions, many of which remain unanswered. Methods: In order to assist GPs in finding quick, evidence-based answers, we developed a learning program (LP with a short interactive workshop based on a simple three-step-heuristic to improve their search and appraisal competence (SAC. We evaluated the LP effectiveness with a randomized controlled trial (RCT. Participants (intervention group [IG] n=20; control group [CG] n=31 rated acceptance and satisfaction and also answered 39 knowledge questions to assess their SAC. We controlled for previous knowledge in content areas covered by the test. Results: Main outcome – SAC: within both groups, the pre–post test shows significant (P=0.00 improvements in correctness (IG 15% vs CG 11% and confidence (32% vs 26% to find evidence-based answers. However, the SAC difference was not significant in the RCT. Other measures: Most workshop participants rated “learning atmosphere” (90%, “skills acquired” (90%, and “relevancy to my practice” (86% as good or very good. The LP-recommendations were implemented by 67% of the IG, whereas 15% of the CG already conformed to LP recommendations spontaneously (odds ratio 9.6, P=0.00. After literature search, the IG showed a (not significantly higher satisfaction regarding “time spent” (IG 80% vs CG 65%, “quality of information” (65% vs 54%, and “amount of information” (53% vs 47%.Conclusion: Long-standing established GPs have a good SAC. Despite high acceptance, strong
A heuristic algorithm based on tabu search for vehicle routing problems with backhauls
Directory of Open Access Journals (Sweden)
Jhon Jairo Santa Chávez
2017-07-01
Full Text Available In this paper, a heuristic algorithm based on Tabu Search Approach for solving the Vehicle Routing Problem with Backhauls (VRPB is proposed. The problem considers a set of customers divided in two subsets: Linehaul and Backhaul customers. Each Linehaul customer requires the delivery of a given quantity of product from the depot, whereas a given quantity of product must be picked up from each Backhaul customer and transported to the depot. In the proposed algorithm, each route consists of one sub-route in which only the delivery task is done, and one sub-route in which only the collection process is performed. The search process allows obtaining a correct order to visit all the customers on each sub-route. In addition, the proposed algorithm determines the best connections among the sub-routes in order to obtain a global solution with the minimum traveling cost. The efficiency of the algorithm is evaluated on a set of benchmark instances taken from the literature. The results show that the computing times are greatly reduced with a high quality of solutions. Finally, conclusions and suggestions for future works are presented.
Heuristic Search for Planning with Different Forced Goal-Ordering Constraints
Directory of Open Access Journals (Sweden)
Jiangfeng Luo
2013-01-01
Full Text Available Planning with forced goal-ordering (FGO constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. In certain planning domains, all the FGOs exist in the initial state. No matter which approach is adopted to achieve a subgoal, all the subgoals should be achieved in a given sequence from the initial state. Otherwise, the planning may arrive at a deadlock. For some other planning domains, there is no FGO in the initial state. However, FGO may occur during the planning process if certain subgoal is achieved by an inappropriate approach. This paper contributes to illustrate that it is the excludable constraints among the goal achievement operations (GAO of different subgoals that introduce the FGOs into the planning problem, and planning with FGO is still a challenge for the heuristic search based planners. Then, a novel multistep forward search algorithm is proposed which can solve the planning problem with different FGOs efficiently.
Parsing heuristic and forward search in first-graders' game-play behavior.
Paz, Luciano; Goldin, Andrea P; Diuk, Carlos; Sigman, Mariano
2015-07-01
Seventy-three children between 6 and 7 years of age were presented with a problem having ambiguous subgoal ordering. Performance in this task showed reliable fingerprints: (a) a non-monotonic dependence of performance as a function of the distance between the beginning and the end-states of the problem, (b) very high levels of performance when the first move was correct, and (c) states in which accuracy of the first move was significantly below chance. These features are consistent with a non-Markov planning agent, with an inherently inertial decision process, and that uses heuristics and partial problem knowledge to plan its actions. We applied a statistical framework to fit and test the quality of a proposed planning model (Monte Carlo Tree Search). Our framework allows us to parse out independent contributions to problem-solving based on the construction of the value function and on general mechanisms of the search process in the tree of solutions. We show that the latter are correlated with children's performance on an independent measure of planning, while the former is highly domain specific. Copyright © 2014 Cognitive Science Society, Inc.
Amoeba-Inspired Heuristic Search Dynamics for Exploring Chemical Reaction Paths.
Aono, Masashi; Wakabayashi, Masamitsu
2015-09-01
We propose a nature-inspired model for simulating chemical reactions in a computationally resource-saving manner. The model was developed by extending our previously proposed heuristic search algorithm, called "AmoebaSAT [Aono et al. 2013]," which was inspired by the spatiotemporal dynamics of a single-celled amoeboid organism that exhibits sophisticated computing capabilities in adapting to its environment efficiently [Zhu et al. 2013]. AmoebaSAT is used for solving an NP-complete combinatorial optimization problem [Garey and Johnson 1979], "the satisfiability problem," and finds a constraint-satisfying solution at a speed that is dramatically faster than one of the conventionally known fastest stochastic local search methods [Iwama and Tamaki 2004] for a class of randomly generated problem instances [ http://www.cs.ubc.ca/~hoos/5/benchm.html ]. In cases where the problem has more than one solution, AmoebaSAT exhibits dynamic transition behavior among a variety of the solutions. Inheriting these features of AmoebaSAT, we formulate "AmoebaChem," which explores a variety of metastable molecules in which several constraints determined by input atoms are satisfied and generates dynamic transition processes among the metastable molecules. AmoebaChem and its developed forms will be applied to the study of the origins of life, to discover reaction paths for which expected or unexpected organic compounds may be formed via unknown unstable intermediates and to estimate the likelihood of each of the discovered paths.
Adabor, Emmanuel S; Acquaah-Mensah, George K; Oduro, Francis T
2015-02-01
Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks. Copyright © 2014 Elsevier Inc. All rights reserved.
Heuristic Scheme for Home Circuit Grouping andWavelength Assignment in LOBS-HC Networks
Yang, Ximin; Yi, Bo; Tang, Wan; Li, Jingcong
2014-09-01
Grouping the Home Circuits (HCs) with the same source is a critical issue of Labeled Optical Burst Switching with Home Circuit (LOBS-HC). To maximize the wavelength utilization in LOBS-HC ring networks, an HC grouping and wavelength assignment scheme, called Longest Path Matching and Graph Coloring (LPMGC), is proposed. LPMGC consists of two phases: grouping and wavelength assignment. First, a heuristic algorithm, named Longest Path Matching (LPM), is proposed to group the HCs according to the longest common path matching between HCs and to make each group as large as possible. And then, Graph Coloring (GC) is introduced to assign wavelengths for HC groups. The numerical results show that the proposed scheme performances better than Complementary HC Assignment (CHA) and some other heuristics in both unidirectional and bidirectional LOBS-HC ring networks in terms of wavelength utilization.
Neural model of gene regulatory network: a survey on supportive meta-heuristics.
Biswas, Surama; Acharyya, Sriyankar
2016-06-01
Gene regulatory network (GRN) is produced as a result of regulatory interactions between different genes through their coded proteins in cellular context. Having immense importance in disease detection and drug finding, GRN has been modelled through various mathematical and computational schemes and reported in survey articles. Neural and neuro-fuzzy models have been the focus of attraction in bioinformatics. Predominant use of meta-heuristic algorithms in training neural models has proved its excellence. Considering these facts, this paper is organized to survey neural modelling schemes of GRN and the efficacy of meta-heuristic algorithms towards parameter learning (i.e. weighting connections) within the model. This survey paper renders two different structure-related approaches to infer GRN which are global structure approach and substructure approach. It also describes two neural modelling schemes, such as artificial neural network/recurrent neural network based modelling and neuro-fuzzy modelling. The meta-heuristic algorithms applied so far to learn the structure and parameters of neutrally modelled GRN have been reviewed here.
Directory of Open Access Journals (Sweden)
Mário Mestria
2013-08-01
Full Text Available The Clustered Traveling Salesman Problem (CTSP is a generalization of the Traveling Salesman Problem (TSP in which the set of vertices is partitioned into disjoint clusters and objective is to find a minimum cost Hamiltonian cycle such that the vertices of each cluster are visited contiguously. The CTSP is NP-hard and, in this context, we are proposed heuristic methods for the CTSP using GRASP, Path Relinking and Variable Neighborhood Descent (VND. The heuristic methods were tested using Euclidean instances with up to 2000 vertices and clusters varying between 4 to 150 vertices. The computational tests were performed to compare the performance of the heuristic methods with an exact algorithm using the Parallel CPLEX software. The computational results showed that the hybrid heuristic method using VND outperforms other heuristic methods.
Parameter identification of robot manipulators: a heuristic particle swarm search approach.
Directory of Open Access Journals (Sweden)
Danping Yan
Full Text Available Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO algorithm, which we call the elitist learning strategy (ELS and proportional integral derivative (PID controller hybridized PSO approach (ELPIDSO. A specified PID controller is designed to improve particles' local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS method, genetic algorithm (GA, and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.
Index Fund Optimization Using a Genetic Algorithm and a Heuristic Local Search
Orito, Yukiko; Inoguchi, Manabu; Yamamoto, Hisashi
It is well known that index funds are popular passively managed portfolios and have been used very extensively for the hedge trading. Index funds consist of a certain number of stocks of listed companies on a stock market such that the fund's return rates follow a similar path to the changing rates of the market indices. However it is hard to make a perfect index fund consisting of all companies included in the given market index. Thus, the index fund optimization can be viewed as a combinatorial optimization for portfolio managements. In this paper, we propose an optimization method that consists of a genetic algorithm and a heuristic local search algorithm to make strong linear association between the fund's return rates and the changing rates of market index. We apply the method to the Tokyo Stock Exchange and make index funds whose return rates follow a similar path to the changing rates of Tokyo Stock Price Index (TOPIX). The results show that our proposal method makes the index funds with strong linear association to the market index by small computing time.
Parameter identification of robot manipulators: a heuristic particle swarm search approach.
Yan, Danping; Lu, Yongzhong; Levy, David
2015-01-01
Parameter identification of robot manipulators is an indispensable pivotal process of achieving accurate dynamic robot models. Since these kinetic models are highly nonlinear, it is not easy to tackle the matter of identifying their parameters. To solve the difficulty effectively, we herewith present an intelligent approach, namely, a heuristic particle swarm optimization (PSO) algorithm, which we call the elitist learning strategy (ELS) and proportional integral derivative (PID) controller hybridized PSO approach (ELPIDSO). A specified PID controller is designed to improve particles' local and global positions information together with ELS. Parameter identification of robot manipulators is conducted for performance evaluation of our proposed approach. Experimental results clearly indicate the following findings: Compared with standard PSO (SPSO) algorithm, ELPIDSO has improved a lot. It not only enhances the diversity of the swarm, but also features better search effectiveness and efficiency in solving practical optimization problems. Accordingly, ELPIDSO is superior to least squares (LS) method, genetic algorithm (GA), and SPSO algorithm in estimating the parameters of the kinetic models of robot manipulators.
A Hybrid Tabu Search Heuristic for a Bilevel Competitive Facility Location Model
Küçükaydın, Hande; Aras, Necati; Altınel, I. Kuban
We consider a problem in which a firm or franchise enters a market by locating new facilities where there are existing facilities belonging to a competitor. The firm aims at finding the location and attractiveness of each facility to be opened so as to maximize its profit. The competitor, on the other hand, can react by adjusting the attractiveness of its existing facilities, opening new facilities and/or closing existing ones with the objective of maximizing its own profit. The demand is assumed to be aggregated at certain points in the plane and the facilities of the firm can be located at prespecified candidate sites. We employ Huff's gravity-based rule in modeling the behavior of the customers where the fraction of customers at a demand point that visit a certain facility is proportional to the facility attractiveness and inversely proportional to the distance between the facility site and demand point. We formulate a bilevel mixed-integer nonlinear programming model where the firm entering the market is the leader and the competitor is the follower. In order to find a feasible solution of this model, we develop a hybrid tabu search heuristic which makes use of two exact methods as subroutines: a gradient ascent method and a branch-and-bound algorithm with nonlinear programming relaxation.
The Dynamical Recollection of Interconnected Neural Networks Using Meta-heuristics
Kuremoto, Takashi; Watanabe, Shun; Kobayashi, Kunikazu; Feng, Laing-Bing; Obayashi, Masanao
The interconnected recurrent neural networks are well-known with their abilities of associative memory of characteristic patterns. For example, the traditional Hopfield network (HN) can recall stored pattern stably, meanwhile, Aihara's chaotic neural network (CNN) is able to realize dynamical recollection of a sequence of patterns. In this paper, we propose to use meta-heuristic (MH) methods such as the particle swarm optimization (PSO) and the genetic algorithm (GA) to improve traditional associative memory systems. Using PSO or GA, for CNN, optimal parameters are found to accelerate the recollection process and raise the rate of successful recollection, and for HN, optimized bias current is calculated to improve the network with dynamical association of a series of patterns. Simulation results of binary pattern association showed effectiveness of the proposed methods.
Brusco, Michael; Stolze, Hannah J; Hoffman, Michaela; Steinley, Douglas
2017-01-01
A popular objective criterion for partitioning a set of actors into core and periphery subsets is the maximization of the correlation between an ideal and observed structure associated with intra-core and intra-periphery ties. The resulting optimization problem has commonly been tackled using heuristic procedures such as relocation algorithms, genetic algorithms, and simulated annealing. In this paper, we present a computationally efficient simulated annealing algorithm for maximum correlation core/periphery partitioning of binary networks. The algorithm is evaluated using simulated networks consisting of up to 2000 actors and spanning a variety of densities for the intra-core, intra-periphery, and inter-core-periphery components of the network. Core/periphery analyses of problem solving, trust, and information sharing networks for the frontline employees and managers of a consumer packaged goods manufacturer are provided to illustrate the use of the model.
Directory of Open Access Journals (Sweden)
Jeng-Fung Chen
2014-10-01
Full Text Available Predicting student academic performance with a high accuracy facilitates admission decisions and enhances educational services at educational institutions. This raises the need to propose a model that predicts student performance, based on the results of standardized exams, including university entrance exams, high school graduation exams, and other influential factors. In this study, an approach to the problem based on the artificial neural network (ANN with the two meta-heuristic algorithms inspired by cuckoo birds and their lifestyle, namely, Cuckoo Search (CS and Cuckoo Optimization Algorithm (COA is proposed. In particular, we used previous exam results and other factors, such as the location of the student’s high school and the student’s gender as input variables, and predicted the student academic performance. The standard CS and standard COA were separately utilized to train the feed-forward network for prediction. The algorithms optimized the weights between layers and biases of the neuron network. The simulation results were then discussed and analyzed to investigate the prediction ability of the neural network trained by these two algorithms. The findings demonstrated that both CS and COA have potential in training ANN and ANN-COA obtained slightly better results for predicting student academic performance in this case. It is expected that this work may be used to support student admission procedures and strengthen the service system in educational institutions.
Energy Technology Data Exchange (ETDEWEB)
Delbem, Alexandre C.B.; Bretas, Newton G. [Sao Paulo Univ., Sao Carlos, SP (Brazil). Dept. de Engenharia Eletrica; Carvalho, Andre C.P.L.F. [Sao Paulo Univ., Sao Carlos, SP (Brazil). Dept. de Ciencias de Computacao e Estatistica
1996-11-01
A search approach using fuzzy heuristics and a neural network parameter was developed for service restoration of a distribution system. The goal was to restore energy for an un-faulted zone after a fault had been identified and isolated. The restoration plan must be carried out in a very short period. However, the combinatorial feature of the problem constrained the application of automatic energy restoration planners. To overcome this problem, an heuristic search approach using fuzzy heuristics was proposed. As a result, a genetic algorithm approach was developed to achieve the optimal energy restoration plan. The effectiveness of these approaches were tested in a simplified distribution system based on the complex distribution system of Sao Carlos city, Sao Paulo State - southeast Brazil. It was noticed that the genetic algorithm provided better performance than the fuzzy heuristic search in this problem. 11 refs., 10 figs.
Directory of Open Access Journals (Sweden)
Orhan TÜRKBEY
2002-02-01
Full Text Available Memetic algorithms, which use local search techniques, are hybrid structured algorithms like genetic algorithms among evolutionary algorithms. In this study, for Quadratic Assignment Problem (QAP, a memetic structured algorithm using a local search heuristic like 2-opt is developed. Developed in the algorithm, a crossover operator that has not been used before for QAP is applied whereas, Eshelman procedure is used in order to increase thesolution variability. The developed memetic algorithm is applied on test problems taken from QAP-LIB, the results are compared with the present techniques in the literature.
Mengshoel, Ole J.; Roth, Dan; Wilkins, David C.
2001-01-01
Portfolio methods support the combination of different algorithms and heuristics, including stochastic local search (SLS) heuristics, and have been identified as a promising approach to solve computationally hard problems. While successful in experiments, theoretical foundations and analytical results for portfolio-based SLS heuristics are less developed. This article aims to improve the understanding of the role of portfolios of heuristics in SLS. We emphasize the problem of computing most probable explanations (MPEs) in Bayesian networks (BNs). Algorithmically, we discuss a portfolio-based SLS algorithm for MPE computation, Stochastic Greedy Search (SGS). SGS supports the integration of different initialization operators (or initialization heuristics) and different search operators (greedy and noisy heuristics), thereby enabling new analytical and experimental results. Analytically, we introduce a novel Markov chain model tailored to portfolio-based SLS algorithms including SGS, thereby enabling us to analytically form expected hitting time results that explain empirical run time results. For a specific BN, we show the benefit of using a homogenous initialization portfolio. To further illustrate the portfolio approach, we consider novel additive search heuristics for handling determinism in the form of zero entries in conditional probability tables in BNs. Our additive approach adds rather than multiplies probabilities when computing the utility of an explanation. We motivate the additive measure by studying the dramatic impact of zero entries in conditional probability tables on the number of zero-probability explanations, which again complicates the search process. We consider the relationship between MAXSAT and MPE, and show that additive utility (or gain) is a generalization, to the probabilistic setting, of MAXSAT utility (or gain) used in the celebrated GSAT and WalkSAT algorithms and their descendants. Utilizing our Markov chain framework, we show that
Directory of Open Access Journals (Sweden)
Mário Mestria
2014-11-01
Full Text Available In this paper, we propose new heuristic methods for solver the Clustered Traveling Salesman Problem (CTSP. The CTSP is a generalization of the Traveling Salesman Problem (TSP in which the set of vertices is partitioned into disjoint clusters and objective is to find a minimum cost Hamiltonian cycle such that the vertices of each cluster are visited contiguously. We develop two Variable Neighborhood Random Descent with Iterated Local for solver the CTSP. The heuristic methods proposed were tested in types of instances with data at different level of granularity for the number of vertices and clusters. The computational results showed that the heuristic methods outperform recent existing methods in the literature and they are competitive with an exact algorithm using the Parallel CPLEX software.
DEFF Research Database (Denmark)
Iris, Cagatay; Pacino, Dario; Røpke, Stefan
2017-01-01
This paper focuses on the integrated berth allocation and quay crane assignment problem in container terminals. We consider the decrease in the marginal productivity of quay cranes and the increase in handling time due to deviation from the desired position. We consider a continuous berth......, discretized in small equal-sized sections. A number of enhancements over the state-of-the-art formulation and an Adaptive Large Neighborhood Search (ALNS) heuristic are presented. Computational results reveal that the enhancements improve many of the best-known bounds, and the ALNS outperforms the state-of-the-art...
Complexity, Heuristic, and Search Analysis for the Games of Crossings and Epaminondas
2014-03-27
research in Artifical Intelligence (Section 2.1) and why games are studied (Section 2.2). Section 2.3 discusses how games are played and solved. An...5 2.1 Games in Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Game Study...Artificial Intelligence UCT Upper Confidence Bounds applied to Trees HUCT Heuristic Guided UCT LOA Lines of Action UCB Upper Confidence Bound RAVE Rapid
AM: An Artificial Intelligence Approach to Discovery in Mathematics as Heuristic Search
1976-07-01
could lead to a single discovery like " Fibonacci series" but never lead to any other discoveries. The reasons for demanding generality are not oily...done in this general area is to emulate some of the reasonably simple activities of mathematicians, which is finding consequences from given assumptions...Illustration. "Fill in Generalizations of Equality’ The Ratings Game 4.3. Heuristics Create New Concepts ..................................... 42 An
Directory of Open Access Journals (Sweden)
Zhenghai Liao
2017-02-01
Full Text Available This paper proposes a heuristic triple layered particle swarm optimization–back-propagation (PSO-BP neural network method for improving the convergence and prediction accuracy of the fault diagnosis system of the photovoltaic (PV array. The parameters, open-circuit voltage (Voc, short-circuit current (Isc, maximum power (Pm and voltage at maximum power point (Vm are extracted from the output curve of the PV array as identification parameters for the fault diagnosis system. This study compares performances of two methods, the back-propagation neural network method, which is widely used, and the heuristic method with MATLAB. In the training phase, the back-propagation method takes about 425 steps to convergence, while the heuristic method needs only 312 steps. In the fault diagnosis phase, the prediction accuracy of the heuristic method is 93.33%, while the back-propagation method scores 86.67%. It is concluded that the heuristic method can not only improve the convergence of the simulation but also significantly improve the prediction accuracy of the fault diagnosis system.
Groundwater Monitoring Network Design Using a Space-Filling/ Bias-Reduction Heuristic
Yan, T.; Singh, A.; Kelley, V.; Deeds, N.
2012-12-01
Groundwater monitoring network design is one of the primary goals of groundwater management. In this study, a heuristic method for selecting wells to monitor groundwater flow is developed. The approach selects wells to a) maximize spread within the monitoring area (space-filling objective), b) reduce bias in estimate of groundwater level (drawdown objective) by selecting pairs of well proximal and distant from pumping areas. By selecting pairs of monitoring wells, this method is able to capture the largest and smallest drawdown in the study area while ensuring the newly added monitoring wells are at the greatest distance from existing monitoring wells. One of the advantages of this method is that it does not require water level information, obtained either from field measurements or groundwater model runs, which might be unavailable at the time of the monitoring network design; instead, this method utilizes pumping rates and locations thus can take future planning into consideration. If water level data is available then that may be included by considering it in the drawdown objective. A FORTRAN code is developed to implement this method. By changing the weighting factors, users have the flexibility on deciding the importance of pumping and spatial information to their network designs. The method has been successfully applied to monitoring network design in Upper Trinity County Groundwater Conservation District in Texas. Monitoring wells were selected from thousands of existing wells and added to the current monitoring network. The results support the decision maker on the number and distribution of a new groundwater network using existing wells. The study can be extended to improve the application of desired future condition (DFC) for Groundwater Conservation Districts in Texas.
An adaptive random search for short term generation scheduling with network constraints.
Directory of Open Access Journals (Sweden)
J A Marmolejo
Full Text Available This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach.
Heuristic space diversity control for improved meta-hyper-heuristic performance
CSIR Research Space (South Africa)
Grobler, J
2015-04-01
Full Text Available This paper expands on the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm in search of greater performance benefits...
Heuristic approaches for energy-efficient shared restoration in WDM networks
Alilou, Shahab
In recent years, there has been ongoing research on the design of energy-efficient Wavelength Division Multiplexing (WDM) networks. The explosive growth of Internet traffic has led to increased power consumption of network components. Network survivability has also been a relevant research topic, as it plays a crucial role in assuring continuity of service with no disruption, regardless of network component failure. Network survivability mechanisms tend to utilize considerable resources such as spare capacity in order to protect and restore information. This thesis investigates techniques for reducing energy demand and enhancing energy efficiency in the context of network survivability. We propose two novel heuristic energy-efficient shared protection approaches for WDM networks. These approaches intend to save energy by setting on sleep mode devices that are not being used while providing shared backup paths to satisfy network survivability. The first approach exploits properties of a math series in order to assign weight to the network links. It aims at reducing power consumption at the network indirectly by aggregating traffic on a set of nodes and links with high traffic load level. Routing traffic on links and nodes that are already under utilization makes it possible for the links and nodes with no load to be set on sleep mode. The second approach is intended to dynamically route traffic through nodes and links with high traffic load level. Similar to the first approach, this approach computes a pair of paths for every newly arrived demand. It computes these paths for every new demand by comparing the power consumption of nodes and links in the network before the demand arrives with their potential power consumption if they are chosen along the paths of this demand. Simulations of two different networks were used to compare the total network power consumption obtained using the proposed techniques against a standard shared-path restoration scheme. Shared
Directory of Open Access Journals (Sweden)
Jinggang Chu
2015-05-01
Full Text Available River basin simulation and multi-reservoir optimal operation have been critical for river basin management. Due to the intense interaction between human activities and river basin systems, the river basin model and multi-reservoir operation model are complicated with a large number of parameters. Therefore, fast and stable optimization algorithms are required for river basin management under the changing conditions of climate and current human activities. This study presents a new global optimization algorithm, named as heuristic dynamically dimensioned search with sensitivity information (HDDS-S, to effectively perform river basin simulation and multi-reservoir optimal operation during river basin management. The HDDS-S algorithm is built on the dynamically dimensioned search (DDS algorithm; and has an improved computational efficiency while maintaining its search capacity compared to the original DDS algorithm. This is mainly due to the non-uniform probability assigned to each decision variable on the basis of its changing sensitivity to the optimization objectives during the adaptive change from global to local search with dimensionality reduced. This study evaluates the new algorithm by comparing its performance with the DDS algorithm on a river basin model calibration problem and a multi-reservoir optimal operation problem. The results obtained indicate that the HDDS-S algorithm outperforms the DDS algorithm in terms of search ability and computational efficiency in the two specific problems. In addition; similar to the DDS algorithm; the HDDS-S algorithm is easy to use as it does not require any parameter tuning and automatically adjusts its search to find good solutions given an available computational budget.
Realistic searches on stretched exponential networks
Indian Academy of Sciences (India)
In addition, the linking probability is taken to be dependent on social distances and is governed by a parameter . The searches are realistic in the sense that not all search chains can be completed. An estimate of = ρ/d, where is the success rate and d the dynamic path length, shows that for a network of nodes, ...
Search Engine Competition with Network Externalities
Argenton, C.; Prüfer, J.
2011-01-01
The market for Internet search is not only economically and socially important, it is also highly concentrated. Is this a problem? We study the question whether "competition is only a free click away". We argue that the market for Internet search is characterized by indirect network externalities
Search engine competition with network externalities
Argenton, C.; Prüfer, J.
2012-01-01
The market for Internet search is not only economically and socially important, it is also highly concentrated. Is this a problem? We study the question of whether “competition is only a free click away.” We argue that the market for Internet search is characterized by indirect network externalities and construct a simple model of search engine competition, which produces a market share development that fits well the empirically observed developments since 2003. We find that there is a strong...
Edge-ratio network clustering by Variable Neighborhood Search
Cafieri, Sonia; Hansen, Pierre; Mladenović, Nenad
2014-05-01
The analysis of networks and in particular the identification of communities, or clusters, is a topic of active research with applications arising in many domains. Several models were proposed for this problem. In reference [S. Cafieri, P. Hansen, L. Liberti, Phys. Rev. E 81, 026105 (2010)], a criterion is proposed for a graph bipartition to be optimal: one seeks to maximize the minimum for both classes of the bipartition of the ratio of inner edges to cut edges ( edge-ratio), and it is used in a hierarchical divisive algorithm for community identification in networks. In this paper, we develop a VNS-based heuristic for hierarchical divisive edge-ratio network clustering. A k-neighborhood is defined as move of k entities, i.e., k entities change their membership from one to another cluster. A local search is based on 1-changes and k-changes are used for shaking the incumbent solution. Computational results on datasets from the literature validate the proposed approach.
A Column Generation based Heuristic to extend Lifetime in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Karine DESCHINKEL
2012-03-01
Full Text Available Lifetime Optimization has received a lot of interest in wireless sensor networks. In our study we propose an energy-aware centralized method by organizing the nodes in non-disjoint cover sets where each cover set is capable of monitoring all the targets of the region of interest and by activate these cover sets successively. We first model the problem with a linear programming (LP formulation for non-disjoint cover sets where the objective is to maximize the total work time of all cover sets, with respect the sensor’s battery lifetime. As the number of cover sets may be huge, exponential to the number of sensors and targets, we develop a resolution method based on a column generation (CG process. This method requires the resolution of an auxiliary problem formulated as an integer programming (IP problem. We propose a heuristic for addressing the auxiliary problem which produces very good solutions in lower computational times compared to an exact resolution as shown in the simulation results.
Hemmelmayr, Vera C.; Cordeau, Jean-François; Crainic, Teodor Gabriel
2012-01-01
In this paper, we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP). The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final customers. The LRP can be seen as a special case of the 2E-VRP in which vehicle routing is performed only at the second level. We have developed new neighborhood search operators by exploiting the structure of the two problem classes considered and have also adapted existing operators from the literature. The operators are used in a hierarchical scheme reflecting the multi-level nature of the problem. Computational experiments conducted on several sets of instances from the literature show that our algorithm outperforms existing solution methods for the 2E-VRP and achieves excellent results on the LRP. PMID:23483764
Hemmelmayr, Vera C; Cordeau, Jean-François; Crainic, Teodor Gabriel
2012-12-01
In this paper, we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP). The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final customers. The LRP can be seen as a special case of the 2E-VRP in which vehicle routing is performed only at the second level. We have developed new neighborhood search operators by exploiting the structure of the two problem classes considered and have also adapted existing operators from the literature. The operators are used in a hierarchical scheme reflecting the multi-level nature of the problem. Computational experiments conducted on several sets of instances from the literature show that our algorithm outperforms existing solution methods for the 2E-VRP and achieves excellent results on the LRP.
Energy Technology Data Exchange (ETDEWEB)
Cruz Castrejon, J. A; Islas Perez, E; Espinosa Reza, A; Garcia Mendoza, R [Instituto de Investigaciones Electricas, Cuernavaca, Morelos (Mexico)]. E-mails: adrian.cruz@iie.org.mx; eislas@iie.org.mx; aer@iie.org.mx; rgarcia@iie.org.mx
2013-03-15
In this paper we present a proposed solution to the problem of finding alternatives to reset faults in radial distribution networks power systems. This solution uses a deterministic method based on the definition of heuristics and whose main objectives are to improve execution time and solution quality. This search is based on the alternate repetition of two stages: a stage that attempts to reset the unconnected areas and other areas trying ballasting overloaded. [Spanish] En este articulo se presenta una propuesta de solucion al problema de busqueda de alternativas de restablecimiento para fallas en redes de distribucion radiales en sistemas electricos de potencia. Esta solucion utiliza un metodo deterministico basado en la definicion de heuristicas y cuyos objetivos principales son: mejorar el tiempo de ejecucion y calidad de la solucion. Esta busqueda se basa en la repeticion alternada de dos etapas: una etapa que intenta restablecer las areas desconectadas y otra que intenta deslastrar las areas sobrecargadas.
Utilization of Tabu search heuristic rules in sampling-based motion planning
Khaksar, Weria; Hong, Tang Sai; Sahari, Khairul Salleh Mohamed; Khaksar, Mansoor
2015-05-01
Path planning in unknown environments is one of the most challenging research areas in robotics. In this class of path planning, the robot acquires the information from its sensory system. Sampling-based path planning is one of the famous approaches with low memory and computational requirements that has been studied by many researchers during the past few decades. We propose a sampling-based algorithm for path planning in unknown environments using Tabu search. The Tabu search component of the proposed method guides the sampling to find the samples in the most promising areas and makes the sampling procedure more intelligent. The simulation results show the efficient performance of the proposed approach in different types of environments. We also compare the performance of the algorithm with some of the well-known path planning approaches, including Bug1, Bug2, PRM, RRT and the Visibility Graph. The comparison results support the claim of superiority of the proposed algorithm.
[Case finding in early prevention networks - a heuristic for ambulatory care settings].
Barth, Michael; Belzer, Florian
2016-06-01
One goal of early prevention is the support of families with small children up to three years who are exposed to psychosocial risks. The identification of these cases is often complex and not well-directed, especially in the ambulatory care setting. Development of a model of a feasible and empirical based strategy for case finding in ambulatory care. Based on the risk factors of postpartal depression, lack of maternal responsiveness, parental stress with regulation disorders and poverty a lexicographic and non-compensatory heuristic model with simple decision rules, will be constructed and empirically tested. Therefore the original data set from an evaluation of the pediatric documentary form on psychosocial issues of families with small children in well-child visits will be used and reanalyzed. The first diagnostic step in the non-compensatory and hierarchical classification process is the assessment of postpartal depression followed by maternal responsiveness, parental stress and poverty. The classification model identifies 89.0 % cases from the original study. Compared to the original study the decision process becomes clearer and more concise. The evidence-based and data-driven model exemplifies a strategy for the assessment of psychosocial risk factors in ambulatory care settings. It is based on four evidence-based risk factors and offers a quick and reliable classification. A further advantage of this model is that after a risk factor is identified the diagnostic procedure will be stopped and the counselling process can commence. For further validation of the model studies, in well suited early prevention networks are needed.
Directory of Open Access Journals (Sweden)
A A Eicher
2012-09-01
Full Text Available Magnetic Resonance Imaging provides a non-invasive means to study the neural correlates of Fetal Alcohol Spectrum Disorder (FASD - the most common form of preventable mental retardation worldwide. One approach aims to detect brain abnormalities through an assessment of volume and shape of two sub-cortical structures, the caudate nucleus and hippocampus. We present a method for automatically segmenting these structures from high-resolution MR images captured as part of an ongoing study into the neural correlates of FASD. Our method incorporates an Active Shape Model, which is used to learn shape variation from manually segmented training data. A modified discrete Geometrically Deformable Model is used to generate point correspondence between training models. An ASM is then created from the landmark points. Experiments were conducted on the image search phase of ASM segmentation, in order to find the technique best suited to segmentation of the hippocampus and caudate nucleus. Various popular image search techniques were tested, including an edge detection method and a method based on grey profile Mahalanobis distance measurement. A novel heuristic image search method was also developed and tested. This heuristic method improves image segmentation by taking advantage of characteristics specific to the target data, such as a relatively homogeneous tissue colour in target structures. Results show that ASMs that use the heuristic image search technique produce the most accurate segmentations. An ASM constructed using this technique will enable researchers to quickly, reliably, and automatically segment test data for use in the FASD study.
PISA: Federated Search in P2P Networks with Uncooperative Peers
Ren, Zujie; Shou, Lidan; Chen, Gang; Chen, Chun; Bei, Yijun
Recently, federated search in P2P networks has received much attention. Most of the previous work assumed a cooperative environment where each peer can actively participate in information publishing and distributed document indexing. However, little work has addressed the problem of incorporating uncooperative peers, which do not publish their own corpus statistics, into a network. This paper presents a P2P-based federated search framework called PISA which incorporates uncooperative peers as well as the normal ones. In order to address the indexing needs for uncooperative peers, we propose a novel heuristic query-based sampling approach which can obtain high-quality resource descriptions from uncooperative peers at relatively low communication cost. We also propose an effective method called RISE to merge the results returned by uncooperative peers. Our experimental results indicate that PISA can provide quality search results, while utilizing the uncooperative peers at a low cost.
Meta-Heuristic Cuckoo Search Algorithm for the Correction of Faulty Array Antenna
Directory of Open Access Journals (Sweden)
Shafqatullah Khan
2015-10-01
Full Text Available When the distribution system is disconnected from the transmission system, the islanded portion of the network comprising DG (Distributed Generation units forms a MG (Micro Grid. It is essential either to shut down the DG units or ensure the stable and the controlled operation of the islanded MG. The frequency and the voltage of the islanded MG vary when it is isolated from the main transmission grid. The voltage and the frequency of the islanded MG can be controlled to the permissible limits by providing the required amount of the active and reactive power by the local available sources in the MG. The main focus of this paper is about the control of the network frequency in the islanded MG by employing PI controllers based STATCOM (Static Compensator and BESS-STATCOM (Battery Energy Storage System Equipped devices. The study is done by using DIgSILENT power factory software version 15.0
Chebbo, Hind Munzer
2000-01-01
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University. The goal of transmission planning in electric power systems is a robust network which is economical, reliable, and in harmony with its environment taking into account the inherent uncertainties. For reasons of practicality, transmission planners have normally taken an incremental approach and tended to evaluate a relatively small number of expansion alternatives over a relatively short time h...
A local search method for graph clustering heuristics based on partitional distribution learning
Manjarres D.; Landa-Torres I.; Del Ser J.
2017-01-01
The community structure of complex networks reveals hidden relationships in the organization of their constituent nodes. Indeed, many practical problems stemming from different fields of knowledge such as Biology, Sociology, Chemistry and Computer Science can be modeled as a graph. Therefore, graph analysis and community detection have become a key component for understanding the inherent relational characteristics underlying different systems and processes. In this regard, distinct unsupervi...
Zhao, Wanqing; Meng, Qinggang; Chung, Paul W H
2016-04-01
Using distributed task allocation methods for cooperating multivehicle systems is becoming increasingly attractive. However, most effort is placed on various specific experimental work and little has been done to systematically analyze the problem of interest and the existing methods. In this paper, a general scenario description and a system configuration are first presented according to search and rescue scenario. The objective of the problem is then analyzed together with its mathematical formulation extracted from the scenario. Considering the requirement of distributed computing, this paper then proposes a novel heuristic distributed task allocation method for multivehicle multitask assignment problems. The proposed method is simple and effective. It directly aims at optimizing the mathematical objective defined for the problem. A new concept of significance is defined for every task and is measured by the contribution to the local cost generated by a vehicle, which underlies the key idea of the algorithm. The whole algorithm iterates between a task inclusion phase, and a consensus and task removal phase, running concurrently on all the vehicles where local communication exists between them. The former phase is used to include tasks into a vehicle's task list for optimizing the overall objective, while the latter is to reach consensus on the significance value of tasks for each vehicle and to remove the tasks that have been assigned to other vehicles. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution and can achieve outstanding performance in comparison with the consensus-based bundle algorithm.
Directory of Open Access Journals (Sweden)
Hakan ASLAN
2008-03-01
Full Text Available The safe movement of hazardous materials is receiving increased attention due to growing environmental awareness of the potential health affects of a release causing incident. A novel approach developed in this paper through a game theory interpretation provides a risk-averse solution to the hazardous materials transportation problem. The dispatcher minimizes the expected maximum disutility subject to worst possible set of link failure probabilities, assuming that one link in the network fails. The expected cost at the Nash equilibrium is a useful measure to evaluate the routing strategies for the safe movement of hazardous materials.
Multimodal Logistics Network Design over Planning Horizon through a Hybrid Meta-Heuristic Approach
Shimizu, Yoshiaki; Yamazaki, Yoshihiro; Wada, Takeshi
Logistics has been acknowledged increasingly as a key issue of supply chain management to improve business efficiency under global competition and diversified customer demands. This study aims at improving a quality of strategic decision making associated with dynamic natures in logistics network optimization. Especially, noticing an importance to concern with a multimodal logistics under multiterms, we have extended a previous approach termed hybrid tabu search (HybTS). The attempt intends to deploy a strategic planning more concretely so that the strategic plan can link to an operational decision making. The idea refers to a smart extension of the HybTS to solve a dynamic mixed integer programming problem. It is a two-level iterative method composed of a sophisticated tabu search for the location problem at the upper level and a graph algorithm for the route selection at the lower level. To keep efficiency while coping with the resulting extremely large-scale problem, we invented a systematic procedure to transform the original linear program at the lower-level into a minimum cost flow problem solvable by the graph algorithm. Through numerical experiments, we verified the proposed method outperformed the commercial software. The results indicate the proposed approach can make the conventional strategic decision much more practical and is promising for real world applications.
Soto-Ferrari, Milton; Prieto, Diana; Munene, Gitonga
2017-06-28
Breast-conservation surgery with radiotherapy is a treatment highly recommended by the guidelines from the National Comprehensive Cancer Network. However, several variables influence the final receipt of radiotherapy and it might not be administered to breast cancer patients. Our objective is to propose a systematic framework to identify the clinical and non-clinical variables that influence the receipt of unexpected radiotherapy treatment by means of Bayesian networks and a proposed heuristic approach. We used cancer registry data of Detroit, San Francisco-Oakland, and Atlanta from years 2007-2012 downloaded from the Surveillance, Epidemiology, and End Results Program. The samples had patients diagnosed with in situ and early invasive cancer with 14 clinical and non-clinical variables. Bayesian networks were fitted to the data of each region and systematically analyzed through the proposed Zoom-in heuristic. A comparative analysis with logistic regressions is also presented. For Detroit, patients under stage 0, grade undetermined, histology lobular carcinoma in situ, and age between 26-50 were found more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients with age between 51-75, and grade II were found to be more likely to receive breast-conservation surgery with radiotherapy. For San Francisco-Oakland, patients under stage 0, grade undetermined, and age >75 are more likely to receive BCS. For stages I, IIA, and IIB patients with age >75 are more likely to receive breast-conservation surgery without radiotherapy. For Atlanta, patients under stage 0, grade undetermined, year 2011, and primary site C509 are more likely to receive breast-conservation surgery without radiotherapy. For stages I, IIA, and IIB patients in year 2011, and grade III are more likely to receive breast-conservation surgery without radiotherapy. For in situ breast cancer and early invasive breast cancer, the results are in accordance with the
Improved Degree Search Algorithms in Unstructured P2P Networks
Directory of Open Access Journals (Sweden)
Guole Liu
2012-01-01
Full Text Available Searching and retrieving the demanded correct information is one important problem in networks; especially, designing an efficient search algorithm is a key challenge in unstructured peer-to-peer (P2P networks. Breadth-first search (BFS and depth-first search (DFS are the current two typical search methods. BFS-based algorithms show the perfect performance in the aspect of search success rate of network resources, while bringing the huge search messages. On the contrary, DFS-based algorithms reduce the search message quantity and also cause the dropping of search success ratio. To address the problem that only one of performances is excellent, we propose two memory function degree search algorithms: memory function maximum degree algorithm (MD and memory function preference degree algorithm (PD. We study their performance including the search success rate and the search message quantity in different networks, which are scale-free networks, random graph networks, and small-world networks. Simulations show that the two performances are both excellent at the same time, and the performances are improved at least 10 times.
A survey on bio inspired meta heuristic based clustering protocols for wireless sensor networks
Datta, A.; Nandakumar, S.
2017-11-01
Recent studies have shown that utilizing a mobile sink to harvest and carry data from a Wireless Sensor Network (WSN) can improve network operational efficiency as well as maintain uniform energy consumption by the sensor nodes in the network. Due to Sink mobility, the path between two sensor nodes continuously changes and this has a profound effect on the operational longevity of the network and a need arises for a protocol which utilizes minimal resources in maintaining routes between the mobile sink and the sensor nodes. Swarm Intelligence based techniques inspired by the foraging behavior of ants, termites and honey bees can be artificially simulated and utilized to solve real wireless network problems. The author presents a brief survey on various bio inspired swarm intelligence based protocols used in routing data in wireless sensor networks while outlining their general principle and operation.
Solving the RWA Problem in WDM Optical Networks Using the BCO Meta-Heuristic
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V. S. Aćimović-Raspopović
2010-06-01
Full Text Available This paper researches the routing and wavelength assignment (RWA problem in wavelength routed optical WDM networks with the wavelength conversion capability at different network nodes. We studied the static case in which all connection requests are known in advance, thus a routing decision can be made based on the complete knowledge of the traffic to be served by the network. The Bee Colony Optimization (BCO metaheuristic is applied to solve the RWA problem. We carried out a comprehensive simulation study of the performance of the proposed BCORWA algorithm for different network topologies and traffic scenarios. The obtained simulation results are analyzed and compared.
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Cheng-Yu Yeh
2012-01-01
Full Text Available With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set. The comparisons of our method with other existing methods on known yeast MAPK pathways in terms of precision and recall show that we can find maximum number of the proteins and perform comparably well. On the other hand, our method is more efficient than previous ones and detects the paths of length 10 within 40 seconds using CPU Intel 1.73GHz and 1GB main memory running under windows operating system.
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M. Barnard
2010-07-01
Full Text Available In this article it is demonstrated why and how liminality has developed into a key concept in Practical Theology, in particular in Liturgical Studies. Liminality began its voyage at the beginning of the 20th century as indication of the phase “betwixt and between” distinguished social and spatial stages in rites of passage (Van Gennep, 1960. Among its defining qualities were autonomy and in-stability. In the sixties it developed into a more permanent state, in which “communitas” could come into being as a marginal form of human interrelatedness (Turner, 1995. In the network society of the 21st century liminality has accomplished its journey by moving to the centre of society, pushing structured human interrelatedness to the “margin”, or more precisely to the local, regional, national or categorical (religious, gender, sexual preference, etc. domain (Castells, 2000a; 2004; 2000b. Hu-man society is built around a centre of the stability of the unstable. This also holds for Christian faith and for liturgy. Christian ritual is performed across (worldwide networks and in independent groups and churches by anyone who chooses to do so. There is no liturgical elite anymore; it is principally a popular move-ment characterised by “plural authority structures”. The acade-mic heuristic power of liminality is finally demonstrated in two liturgical cases.
A Computational Investigation of Heuristic Algorithms for 2-Edge-Connectivity Augmentation
DEFF Research Database (Denmark)
Bang-Jensen, Jørgen; Chiarandini, Marco; Morling, Peter
2010-01-01
programming, simple construction heuristics and metaheuristics. As part of the design of heuristics, we consider different neighborhood structures for local search, among which is a very large scale neighborhood. In all cases, we exploit approaches through the graph formulation as well as through...... an equivalent set covering formulation. The results indicate that exact solutions by means of a basic integer programming model can be obtained in reasonably short time even on networks with 800 vertices and around 287,000 edges. Alternatively, an advanced heuristic algorithm based on subgradient...
A new heuristic for the quadratic assignment problem
Zvi Drezner
2002-01-01
We propose a new heuristic for the solution of the quadratic assignment problem. The heuristic combines ideas from tabu search and genetic algorithms. Run times are very short compared with other heuristic procedures. The heuristic performed very well on a set of test problems.
Sunstein, Cass R
2005-08-01
With respect to questions of fact, people use heuristics--mental short-cuts, or rules of thumb, that generally work well, but that also lead to systematic errors. People use moral heuristics too--moral short-cuts, or rules of thumb, that lead to mistaken and even absurd moral judgments. These judgments are highly relevant not only to morality, but to law and politics as well. examples are given from a number of domains, including risk regulation, punishment, reproduction and sexuality, and the act/omission distinction. in all of these contexts, rapid, intuitive judgments make a great deal of sense, but sometimes produce moral mistakes that are replicated in law and policy. One implication is that moral assessments ought not to be made by appealing to intuitions about exotic cases and problems; those intuitions are particularly unlikely to be reliable. Another implication is that some deeply held moral judgments are unsound if they are products of moral heuristics. The idea of error-prone heuristics is especially controversial in the moral domain, where agreement on the correct answer may be hard to elicit; but in many contexts, heuristics are at work and they do real damage. Moral framing effects, including those in the context of obligations to future generations, are also discussed.
Energy Technology Data Exchange (ETDEWEB)
Ortiz, J. J.; Castillo, J. A.; Montes, J. L.; Hernandez, J. L., E-mail: juanjose.ortiz@inin.gob.m [ININ, Carretera Mexico-Toluca s/n, 52750 Ocoyoacac, Estado de Mexico (Mexico)
2009-10-15
This work approaches the study of one of the heuristic rules of fuel cells design for boiling water nuclear reactors. This rule requires that the minor uranium enrichment is placed in the corners of the fuel cell. Also the search greedy is applied for the fuel cells design where explicitly does not take in count this rule, allowing the possibility to place any uranium enrichment with the condition that it does not contain gadolinium. Results are shown in the quality of the obtained cell by search greedy when it considers the rule and when not. The cell quality is measured with the value of the power pick factor obtained, as well as of the neutrons multiplication factor in an infinite medium. Cells were analyzed with 1 and 2 gadolinium concentrations low operation conditions at 120% of the nominal power of the reactors of the nuclear power plant of Laguna Verde. The results show that not to consider the rule in cells with a single gadolinium concentration, it has as repercussion that the greedy search has a minor performance. On the other hand with cells of two gadolinium concentrations, the performance of the greedy search was better. (Author)
Melles, S. J.; Jones, N. E.; Schmidt, B. J.
2014-03-01
Conservation and management of fresh flowing waters involves evaluating and managing effects of cumulative impacts on the aquatic environment from disturbances such as: land use change, point and nonpoint source pollution, the creation of dams and reservoirs, mining, and fishing. To assess effects of these changes on associated biotic communities it is necessary to monitor and report on the status of lotic ecosystems. A variety of stream classification methods are available to assist with these tasks, and such methods attempt to provide a systematic approach to modeling and understanding complex aquatic systems at various spatial and temporal scales. Of the vast number of approaches that exist, it is useful to group them into three main types. The first involves modeling longitudinal species turnover patterns within large drainage basins and relating these patterns to environmental predictors collected at reach and upstream catchment scales; the second uses regionalized hierarchical classification to create multi-scale, spatially homogenous aquatic ecoregions by grouping adjacent catchments together based on environmental similarities; and the third approach groups sites together on the basis of similarities in their environmental conditions both within and between catchments, independent of their geographic location. We review the literature with a focus on more recent classifications to examine the strengths and weaknesses of the different approaches. We identify gaps or problems with the current approaches, and we propose an eight-step heuristic process that may assist with development of more flexible and integrated aquatic classifications based on the current understanding, network thinking, and theoretical underpinnings.
A Search Model with a Quasi-Network
DEFF Research Database (Denmark)
Ejarque, Joao Miguel
This paper adds a quasi-network to a search model of the labor market. Fitting the model to an average unemployment rate and to other moments in the data implies the presence of the network is not noticeable in the basic properties of the unemployment and job finding rates. However, the network c...
Heuristic Methods for Security Protocols
Directory of Open Access Journals (Sweden)
Qurat ul Ain Nizamani
2009-10-01
Full Text Available Model checking is an automatic verification technique to verify hardware and software systems. However it suffers from state-space explosion problem. In this paper we address this problem in the context of cryptographic protocols by proposing a security property-dependent heuristic. The heuristic weights the state space by exploiting the security formulae; the weights may then be used to explore the state space when searching for attacks.
Directory of Open Access Journals (Sweden)
José Manuel Corujeira Gómez
2014-10-01
Full Text Available The current definition of creativity gives importance to interpersonal communication in innovation strategies, and allows us to question the profiles of professionals –innovation partners– communication skills in the practice session in which they are applied. This text shows shallow results on the application of some of their tactics with a group of students. We tested structural/procedural descriptions of hypothetical effects of communication using indicators proposed by Network Theory in terms topologies provided by the group. Without a conclusive result, we expect this paper helps to the creativity's investigation in the innovation sessions.
A Geographical Heuristic Routing Protocol for VANETs
Directory of Open Access Journals (Sweden)
Luis Urquiza-Aguiar
2016-09-01
Full Text Available Vehicular ad hoc networks (VANETs leverage the communication system of Intelligent Transportation Systems (ITS. Recently, Delay-Tolerant Network (DTN routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols use geographical and local information to make forwarding decisions. However, current proposals only consider the selection of the best candidate based on a local-search. In this paper, we propose a generic Geographical Heuristic Routing (GHR protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions hop by hop. GHR includes in its operation adaptations simulated annealing and Tabu-search meta-heuristics, which have largely been used to improve local-search results in discrete optimization. We include a complete performance evaluation of GHR in a multi-hop VANET simulation scenario for a reporting service. Our study analyzes all of the meaningful configurations of GHR and offers a statistical analysis of our findings by means of MANOVA tests. Our results indicate that the use of a Tabu list contributes to improving the packet delivery ratio by around 5% to 10%. Moreover, if Tabu is used, then the simulated annealing routing strategy gets a better performance than the selection of the best node used with carry and forwarding (default operation.
Cooperative and heuristic learning in the international network e-Culturas
Directory of Open Access Journals (Sweden)
Antonio PANTOJA VALLEJO
2011-12-01
Full Text Available 0 0 1 89 495 Instituto Universitario de Ciencias de la Educación 4 1 583 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} Virtual learning environments, converted to digital platforms, encourage collaboration and interaction between users, while allowing the creative capacity will be increased considerably. On this basis the Intercultural Program e-Culturas is based, being part of the International e-Culturas Network (http://www.e-culturas.org. This is a collaborative networking project that aims to twin children of different nationalities to work a series of cross-cultural materials. In the present paper is explained the basic methodology underlying it.
Directory of Open Access Journals (Sweden)
Juan Reca
2017-11-01
Full Text Available This work presents a new approach to increase the efficiency of the heuristics methods applied to the optimal design of water distribution systems. The approach is based on reducing the search space by bounding the diameters that can be used for every network pipe. To reduce the search space, two opposite extreme flow distribution scenarios are analyzed and velocity restrictions to the pipe flow are then applied. The first scenario produces the most uniform flow distribution in the network. The opposite scenario is represented by the network with the maximum flow accumulation. Both extreme flow distributions are calculated by solving a quadratic programming problem, which is a very robust and efficient procedure. This approach has been coupled to a Genetic Algorithm (GA. The GA has an integer coding scheme and variable number of alleles depending on the number of diameters comprised within the velocity restrictions. The methodology has been applied to several benchmark networks and its performance has been compared to a classic GA formulation with a non-bounded search space. It considerably reduced the search space and provided a much faster and more accurate convergence than the GA formulation. This approach can also be coupled to other metaheuristics.
Climate Literacy and Energy Awareness Network releases search widget
Showstack, Randy
2011-11-01
The Climate Literacy and Energy Awareness Network (CLEAN) has launched a widget that can be embedded in any Web site to search the network's catalog of online resources relating to climate and energy topics for students in grades 6-12 and for general audiences. The catalog includes more than 300 high-quality existing digital resources, including learning activities, videos, visualizations, and short investigations that have been reviewed and annotated for scientific accuracy and pedagogical potential. The widget allows users to search keywords and then access the full catalog record of resources from the search. The CLEAN Web site includes a section on teaching climate and energy topics.
A neural network application to search and rescue satellite aided tracking (SARSAT)
Taylor, Ivan W.; Vigneault, Michel O.
1992-04-01
The Search and Rescue Satellite Aided Tracking (SARSAT) system uses polar orbiting satellites to monitor the earth's surface for emergency locator transmitters (ELT's). A detected ELT signal is retransmitted to ground facilities for processing. The ELT location is determined by fitting a Doppler curve to the signal, and this location estimate is then sent for transmission to a rescue unit for action. The heuristic based approach to predict the location accuracy category of SARSAT hits based on the parameters of the Doppler curve fit work reasonably well but are far from ideal. They consistently have difficulty identifying very good solutions when they occur and very poor solutions when they occur. Normal data fitting techniques based on multiple linear regression do not work well because SARSAT data are inherently nonlinear. Neural networks were thus applied to SARSAT data as a nonlinear data fitting technique. A neural network program was trained on a sample of 173 observations and tested on a sample of 172 observations. Results are compared to the current method. Although the results so far have been promising, they are still far from ideal. Other artificial intelligence technologies such as fuzzy logic, expert systems, and inductive learning are being investigated to enhance the neural network approach.
A local search heuristic for the Multi-Commodity k-splittable Maximum Flow Problem
DEFF Research Database (Denmark)
Gamst, Mette
2014-01-01
The Multi-Commodity k-splittable Maximum Flow Problem consists of maximizing the amount of flow routed through a network such that each commodity uses at most k paths and such that edge capacities are satisfied. The problem is NP -hard and has application in a.o. telecommunications. In this paper...
Directory of Open Access Journals (Sweden)
Eric Z. Chen
2015-01-01
Full Text Available Error control codes have been widely used in data communications and storage systems. One central problem in coding theory is to optimize the parameters of a linear code and construct codes with best possible parameters. There are tables of best-known linear codes over finite fields of sizes up to 9. Recently, there has been a growing interest in codes over $\\mathbb{F}_{13}$ and other fields of size greater than 9. The main purpose of this work is to present a database of best-known linear codes over the field $\\mathbb{F}_{13}$ together with upper bounds on the minimum distances. To find good linear codes to establish lower bounds on minimum distances, an iterative heuristic computer search algorithm is employed to construct quasi-twisted (QT codes over the field $\\mathbb{F}_{13}$ with high minimum distances. A large number of new linear codes have been found, improving previously best-known results. Tables of $[pm, m]$ QT codes over $\\mathbb{F}_{13}$ with best-known minimum distances as well as a table of lower and upper bounds on the minimum distances for linear codes of length up to 150 and dimension up to 6 are presented.
An Improved Harmony Search Algorithm for Power Distribution Network Planning
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Wei Sun
2015-01-01
Full Text Available Distribution network planning because of involving many variables and constraints is a multiobjective, discrete, nonlinear, and large-scale optimization problem. Harmony search (HS algorithm is a metaheuristic algorithm inspired by the improvisation process of music players. HS algorithm has several impressive advantages, such as easy implementation, less adjustable parameters, and quick convergence. But HS algorithm still has some defects such as premature convergence and slow convergence speed. According to the defects of the standard algorithm and characteristics of distribution network planning, an improved harmony search (IHS algorithm is proposed in this paper. We set up a mathematical model of distribution network structure planning, whose optimal objective function is to get the minimum annual cost and constraint conditions are overload and radial network. IHS algorithm is applied to solve the complex optimization mathematical model. The empirical results strongly indicate that IHS algorithm can effectively provide better results for solving the distribution network planning problem compared to other optimization algorithms.
Realistic searches on stretched exponential networks
Indian Academy of Sciences (India)
experimental study by Milgram et al [4], in which it was shown that any two persons. (in the USA) can be connected ... tremendous interest in the study of networks, new experiments have been done to verify this property in ... The source nodes have to use a strategy to make the signal reach the target through connected. 313 ...
Graph animals, subgraph sampling and motif search in large networks
Baskerville, Kim; Paczuski, Maya
2007-01-01
We generalize a sampling algorithm for lattice animals (connected clusters on a regular lattice) to a Monte Carlo algorithm for `graph animals', i.e. connected subgraphs in arbitrary networks. As with the algorithm in [N. Kashtan et al., Bioinformatics 20, 1746 (2004)], it provides a weighted sample, but the computation of the weights is much faster (linear in the size of subgraphs, instead of super-exponential). This allows subgraphs with up to ten or more nodes to be sampled with very high statistics, from arbitrarily large networks. Using this together with a heuristic algorithm for rapidly classifying isomorphic graphs, we present results for two protein interaction networks obtained using the TAP high throughput method: one of Escherichia coli with 230 nodes and 695 links, and one for yeast (Saccharomyces cerevisiae) with roughly ten times more nodes and links. We find in both cases that most connected subgraphs are strong motifs (Z-scores >10) or anti-motifs (Z-scores <-10) when the null model is the...
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S. K. Chaharsooghi
2017-04-01
Full Text Available The hub location problem (HLP is one of the strategic planning problems encountered in different contexts such as supply chain management, passenger and cargo transportation industries, and telecommunications. In this paper, we consider a reliable uncapacitated multiple allocation hub location problem under hub disruptions. It is assumed that every open hub facility can fail during its use and in such a case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service in which case a penalty must be paid. The problem is modeled as two-stage stochastic program and a metaheuristic algorithm based on the adaptive large neighborhood search (ALNS is proposed. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.
Electroencephalography epilepsy classifications using hybrid cuckoo search and neural network
Pratiwi, A. B.; Damayanti, A.; Miswanto
2017-07-01
Epilepsy is a condition that affects the brain and causes repeated seizures. This seizure is episodes that can vary and nearly undetectable to long periods of vigorous shaking or brain contractions. Epilepsy often can be confirmed with an electrocephalography (EEG). Neural Networks has been used in biomedic signal analysis, it has successfully classified the biomedic signal, such as EEG signal. In this paper, a hybrid cuckoo search and neural network are used to recognize EEG signal for epilepsy classifications. The weight of the multilayer perceptron is optimized by the cuckoo search algorithm based on its error. The aim of this methods is making the network faster to obtained the local or global optimal then the process of classification become more accurate. Based on the comparison results with the traditional multilayer perceptron, the hybrid cuckoo search and multilayer perceptron provides better performance in term of error convergence and accuracy. The purpose methods give MSE 0.001 and accuracy 90.0 %.
2015-01-01
How can we advance knowledge? Which methods do we need in order to make new discoveries? How can we rationally evaluate, reconstruct and offer discoveries as a means of improving the ‘method’ of discovery itself? And how can we use findings about scientific discovery to boost funding policies, thus fostering a deeper impact of scientific discovery itself? The respective chapters in this book provide readers with answers to these questions. They focus on a set of issues that are essential to the development of types of reasoning for advancing knowledge, such as models for both revolutionary findings and paradigm shifts; ways of rationally addressing scientific disagreement, e.g. when a revolutionary discovery sparks considerable disagreement inside the scientific community; frameworks for both discovery and inference methods; and heuristics for economics and the social sciences.
Understanding crowd-powered search groups: a social network perspective.
Zhang, Qingpeng; Wang, Fei-Yue; Zeng, Daniel; Wang, Tao
2012-01-01
Crowd-powered search is a new form of search and problem solving scheme that involves collaboration among a potentially large number of voluntary Web users. Human flesh search (HFS), a particular form of crowd-powered search originated in China, has seen tremendous growth since its inception in 2001. HFS presents a valuable test-bed for scientists to validate existing and new theories in social computing, sociology, behavioral sciences, and so forth. In this research, we construct an aggregated HFS group, consisting of the participants and their relationships in a comprehensive set of identified HFS episodes. We study the topological properties and the evolution of the aggregated network and different sub-groups in the network. We also identify the key HFS participants according to a variety of measures. We found that, as compared with other online social networks, HFS participant network shares the power-law degree distribution and small-world property, but with a looser and more distributed organizational structure, leading to the diversity, decentralization, and independence of HFS participants. In addition, the HFS group has been becoming increasingly decentralized. The comparisons of different HFS sub-groups reveal that HFS participants collaborated more often when they conducted the searches in local platforms or the searches requiring a certain level of professional knowledge background. On the contrary, HFS participants did not collaborate much when they performed the search task in national platforms or the searches with general topics that did not require specific information and learning. We also observed that the key HFS information contributors, carriers, and transmitters came from different groups of HFS participants.
Understanding crowd-powered search groups: a social network perspective.
Directory of Open Access Journals (Sweden)
Qingpeng Zhang
Full Text Available BACKGROUND: Crowd-powered search is a new form of search and problem solving scheme that involves collaboration among a potentially large number of voluntary Web users. Human flesh search (HFS, a particular form of crowd-powered search originated in China, has seen tremendous growth since its inception in 2001. HFS presents a valuable test-bed for scientists to validate existing and new theories in social computing, sociology, behavioral sciences, and so forth. METHODOLOGY: In this research, we construct an aggregated HFS group, consisting of the participants and their relationships in a comprehensive set of identified HFS episodes. We study the topological properties and the evolution of the aggregated network and different sub-groups in the network. We also identify the key HFS participants according to a variety of measures. CONCLUSIONS: We found that, as compared with other online social networks, HFS participant network shares the power-law degree distribution and small-world property, but with a looser and more distributed organizational structure, leading to the diversity, decentralization, and independence of HFS participants. In addition, the HFS group has been becoming increasingly decentralized. The comparisons of different HFS sub-groups reveal that HFS participants collaborated more often when they conducted the searches in local platforms or the searches requiring a certain level of professional knowledge background. On the contrary, HFS participants did not collaborate much when they performed the search task in national platforms or the searches with general topics that did not require specific information and learning. We also observed that the key HFS information contributors, carriers, and transmitters came from different groups of HFS participants.
Heuristics for Multidimensional Packing Problems
DEFF Research Database (Denmark)
Egeblad, Jens
for a three-dimensional knapsack packing problem involving furniture is presented in the fourth paper. The heuristic is based on a variety of techniques including tree-search, wall-building, and sequential placement. The solution process includes considerations regarding stability and load bearing strength...
DEFF Research Database (Denmark)
Pisinger, David; Røpke, Stefan
2010-01-01
Heuristics based on large neighborhood search have recently shown outstanding results in solving various transportation and scheduling problems. Large neighborhood search methods explore a complex neighborhood by use of heuristics. Using large neighborhoods makes it possible to find better candid...
Lee, Aaron Y; Lee, Cecilia S; Van Gelder, Russell N
2016-07-28
Next generation sequencing technology has enabled characterization of metagenomics through massively parallel genomic DNA sequencing. The complexity and diversity of environmental samples such as the human gut microflora, combined with the sustained exponential growth in sequencing capacity, has led to the challenge of identifying microbial organisms by DNA sequence. We sought to validate a Scalable Metagenomics Alignment Research Tool (SMART), a novel searching heuristic for shotgun metagenomics sequencing results. After retrieving all genomic DNA sequences from the NCBI GenBank, over 1 × 10(11) base pairs of 3.3 × 10(6) sequences from 9.25 × 10(5) species were indexed using 4 base pair hashtable shards. A MapReduce searching strategy was used to distribute the search workload in a computing cluster environment. In addition, a one base pair permutation algorithm was used to account for single nucleotide polymorphisms and sequencing errors. Simulated datasets used to evaluate Kraken, a similar metagenomics classification tool, were used to measure and compare precision and accuracy. Finally using a same set of training sequences we compared Kraken, CLARK, and SMART within the same computing environment. Utilizing 12 computational nodes, we completed the classification of all datasets in under 10 min each using exact matching with an average throughput of over 1.95 × 10(6) reads classified per minute. With permutation matching, we achieved sensitivity greater than 83 % and precision greater than 94 % with simulated datasets at the species classification level. We demonstrated the application of this technique applied to conjunctival and gut microbiome metagenomics sequencing results. In our head to head comparison, SMART and CLARK had similar accuracy gains over Kraken at the species classification level, but SMART required approximately half the amount of RAM of CLARK. SMART is the first scalable, efficient, and rapid metagenomics classification algorithm
A heuristic algorithm for a multi-product four-layer capacitated location-routing problem
Directory of Open Access Journals (Sweden)
Mohsen Hamidi
2014-01-01
Full Text Available The purpose of this study is to solve a complex multi-product four-layer capacitated location-routing problem (LRP in which two specific constraints are taken into account: 1 plants have limited production capacity, and 2 central depots have limited capacity for storing and transshipping products. The LRP represents a multi-product four-layer distribution network that consists of plants, central depots, regional depots, and customers. A heuristic algorithm is developed to solve the four-layer LRP. The heuristic uses GRASP (Greedy Randomized Adaptive Search Procedure and two probabilistic tabu search strategies of intensification and diversification to tackle the problem. Results show that the heuristic solves the problem effectively.
Early detection network design and search strategy issues
We conducted a series of field and related modeling studies (2005-2012) to evaluate search strategies for Great Lakes coastal ecosystems that are at risk of invasion by non-native aquatic species. In developing a network, we should design to achieve an acceptable limit of detect...
Exhaustive Search for Fuzzy Gene Networks from Microarray Data
Energy Technology Data Exchange (ETDEWEB)
Sokhansanj, B A; Fitch, J P; Quong, J N; Quong, A A
2003-07-07
Recent technological advances in high-throughput data collection allow for the study of increasingly complex systems on the scale of the whole cellular genome and proteome. Gene network models are required to interpret large and complex data sets. Rationally designed system perturbations (e.g. gene knock-outs, metabolite removal, etc) can be used to iteratively refine hypothetical models, leading to a modeling-experiment cycle for high-throughput biological system analysis. We use fuzzy logic gene network models because they have greater resolution than Boolean logic models and do not require the precise parameter measurement needed for chemical kinetics-based modeling. The fuzzy gene network approach is tested by exhaustive search for network models describing cyclin gene interactions in yeast cell cycle microarray data, with preliminary success in recovering interactions predicted by previous biological knowledge and other analysis techniques. Our goal is to further develop this method in combination with experiments we are performing on bacterial regulatory networks.
An information search model for online social Networks - MOBIRSE
Directory of Open Access Journals (Sweden)
J. A. Astaiza
2015-12-01
Full Text Available Online Social Networks (OSNs have been gaining great importance among Internet users in recent years. These are sites where it is possible to meet people, publish, and share content in a way that is both easy and free of charge. As a result, the volume of information contained in these websites has grown exponentially, and web search has consequently become an important tool for users to easily find information relevant to their social networking objectives. Making use of ontologies and user profiles can make these searches more effective. This article presents a model for Information Retrieval in OSNs (MOBIRSE based on user profile and ontologies which aims to improve the relevance of retrieved information on these websites. The social network Facebook was chosen for a case study and as the instance for the proposed model. The model was validated using measures such as At-k Precision and Kappa statistics, to assess its efficiency.
Performance of selection hyper-heuristics on the extended HyFlex domains
Almutairi, Alhanof; Özcan, Ender; Kheiri, Ahmed; Jackson, Warren G.
2016-01-01
Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected to be applicable to multiple problem domains, hence performing well in cross-domain search. HyFlex is a general purpose heuristic search API which separates the high level search control from the domain details enabling rapid development and perfo...
Modeling reproductive decisions with simple heuristics
Directory of Open Access Journals (Sweden)
Peter Todd
2013-10-01
Full Text Available BACKGROUND Many of the reproductive decisions that humans make happen without much planning or forethought, arising instead through the use of simple choice rules or heuristics that involve relatively little information and processing. Nonetheless, these heuristic-guided decisions are typically beneficial, owing to humans' ecological rationality - the evolved fit between our constrained decision mechanisms and the adaptive problems we face. OBJECTIVE This paper reviews research on the ecological rationality of human decision making in the domain of reproduction, showing how fertility-related decisions are commonly made using various simple heuristics matched to the structure of the environment in which they are applied, rather than being made with information-hungry mechanisms based on optimization or rational economic choice. METHODS First, heuristics for sequential mate search are covered; these heuristics determine when to stop the process of mate search by deciding that a good-enough mate who is also mutually interested has been found, using a process of aspiration-level setting and assessing. These models are tested via computer simulation and comparison to demographic age-at-first-marriage data. Next, a heuristic process of feature-based mate comparison and choice is discussed, in which mate choices are determined by a simple process of feature-matching with relaxing standards over time. Parental investment heuristics used to divide resources among offspring are summarized. Finally, methods for testing the use of such mate choice heuristics in a specific population over time are then described.
An LP-based heuristic for the fixed charge transportation problem
DEFF Research Database (Denmark)
Klose, Andreas
2007-01-01
The fixed charge transportation problem consists in finding a minimum cost network flow from a set of suppliers to a set of customers. Beside costs proportional to quantities transported, transportation costs also include a fixed charge. The paper describes a linear programming based heuristic...... approach for computing lower and upper bounds on the minimal cost. To this end, the LP relaxation is iteratively strengthened by means of adding cuts; in each iteration the current LP solution is then used to guide a local search heuristic. In addition to standard polyhedral cuts as lifted cover...
Directory of Open Access Journals (Sweden)
Mohammad Taghi Ameli
2012-01-01
Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.
Directory of Open Access Journals (Sweden)
Mohammad Dreidy
2017-01-01
Full Text Available Recently, several environmental problems are beginning to affect all aspects of life. For this reason, many governments and international agencies have expressed great interest in using more renewable energy sources (RESs. However, integrating more RESs with distribution networks resulted in several critical problems vis-à-vis the frequency stability, which might lead to a complete blackout if not properly treated. Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP, Binary genetic algorithm (BGA, and Binary particle swarm optimization (BPSO, to determine the optimal combination of loads that needs to be shed from the islanded distribution network. Compared with existing UFLS schemes using fixed priority loads, the proposed scheme has the ability to restore the network frequency without any overshooting. Furthermore, in terms of execution time, the simulation results show that the BEP technique is fast enough to shed the optimal combination of loads compared with BGA and BPSO techniques.
Complete local search with memory
Ghosh, D.; Sierksma, G.
2000-01-01
Neighborhood search heuristics like local search and its variants are some of the most popular approaches to solve discrete optimization problems of moderate to large size. Apart from tabu search, most of these heuristics are memoryless. In this paper we introduce a new neighborhood search heuristic
Complete local search with memory
Ghosh, D.; Sierksma, G.
2002-01-01
Neighborhood search heuristics like local search and its variants are some of the most popular approaches to solve discrete optimization problems of moderate to large size. Apart from tabu search, most of these heuristics are memoryless. In this paper we introduce a new neighborhood search heuristic
de Jong, Menno D.T.; van der Geest, Thea
2000-01-01
This article is intended to make Web designers more aware of the qualities of heuristics by presenting a framework for analyzing the characteristics of heuristics. The framework is meant to support Web designers in choosing among alternative heuristics. We hope that better knowledge of the
A HYBRID HEURISTIC ALGORITHM FOR THE CLUSTERED TRAVELING SALESMAN PROBLEM
Directory of Open Access Journals (Sweden)
Mário Mestria
2016-04-01
Full Text Available ABSTRACT This paper proposes a hybrid heuristic algorithm, based on the metaheuristics Greedy Randomized Adaptive Search Procedure, Iterated Local Search and Variable Neighborhood Descent, to solve the Clustered Traveling Salesman Problem (CTSP. Hybrid Heuristic algorithm uses several variable neighborhood structures combining the intensification (using local search operators and diversification (constructive heuristic and perturbation routine. In the CTSP, the vertices are partitioned into clusters and all vertices of each cluster have to be visited contiguously. The CTSP is -hard since it includes the well-known Traveling Salesman Problem (TSP as a special case. Our hybrid heuristic is compared with three heuristics from the literature and an exact method. Computational experiments are reported for different classes of instances. Experimental results show that the proposed hybrid heuristic obtains competitive results within reasonable computational time.
Intelligent System Design Using Hyper-Heuristics
Directory of Open Access Journals (Sweden)
Nelishia Pillay
2015-07-01
Full Text Available Determining the most appropriate search method or artificial intelligence technique to solve a problem is not always evident and usually requires implementation of the different approaches to ascertain this. In some instances a single approach may not be sufficient and hybridization of methods may be needed to find a solution. This process can be time consuming. The paper proposes the use of hyper-heuristics as a means of identifying which method or combination of approaches is needed to solve a problem. The research presented forms part of a larger initiative aimed at using hyper-heuristics to develop intelligent hybrid systems. As an initial step in this direction, this paper investigates this for classical artificial intelligence uninformed and informed search methods, namely depth first search, breadth first search, best first search, hill-climbing and the A* algorithm. The hyper-heuristic determines the search or combination of searches to use to solve the problem. An evolutionary algorithm hyper-heuristic is implemented for this purpose and its performance is evaluated in solving the 8-Puzzle, Towers of Hanoi and Blocks World problems. The hyper-heuristic employs a generational evolutionary algorithm which iteratively refines an initial population using tournament selection to select parents, which the mutation and crossover operators are applied to for regeneration. The hyper-heuristic was able to identify a search or combination of searches to produce solutions for the twenty 8-Puzzle, five Towers of Hanoi and five Blocks World problems. Furthermore, admissible solutions were produced for all problem instances.
Social biases determine spatiotemporal sparseness of ciliate mating heuristics.
Clark, Kevin B
2012-01-01
Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present
Searching for planetary transits around M dwarfs with telescope networks
Directory of Open Access Journals (Sweden)
Casal Estefanía
2013-04-01
Full Text Available As of today, hundreds of extrasolar planets have been discoverd, and thousands of candidates are waiting for confirmation. Although we already have discovered dozens of the least massive planets (the so called super-Earths, only a few have been found to be orbiting low mass stars, and only one that actually transist a bright M star. Here, we propose to carry out a photometric search for transiting super-Earths in the Habitable Zone of a large sample of very low-mass stars (M < 0.3 M⊙. These planets have a higher transit detection probability, shorter orbital periods and produce larger radial velocity amplitudes than those orbiting Solar type stars. The observing strategy proposed for such a search would require a robotic network of 1-m class telescopes, such as the LCOGT will provide.
Information Spread of Emergency Events: Path Searching on Social Networks
Hu, Hongzhi; Wu, Tunan
2014-01-01
Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323
Information spread of emergency events: path searching on social networks.
Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui
2014-01-01
Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.
6th International Conference on Network Analysis
Nikolaev, Alexey; Pardalos, Panos; Prokopyev, Oleg
2017-01-01
This valuable source for graduate students and researchers provides a comprehensive introduction to current theories and applications in optimization methods and network models. Contributions to this book are focused on new efficient algorithms and rigorous mathematical theories, which can be used to optimize and analyze mathematical graph structures with massive size and high density induced by natural or artificial complex networks. Applications to social networks, power transmission grids, telecommunication networks, stock market networks, and human brain networks are presented. Chapters in this book cover the following topics: Linear max min fairness Heuristic approaches for high-quality solutions Efficient approaches for complex multi-criteria optimization problems Comparison of heuristic algorithms New heuristic iterative local search Power in network structures Clustering nodes in random graphs Power transmission grid structure Network decomposition problems Homogeneity hypothesis testing Network analy...
Virtual Network Embedding via Monte Carlo Tree Search.
Haeri, Soroush; Trajkovic, Ljiljana
2018-02-01
Network virtualization helps overcome shortcomings of the current Internet architecture. The virtualized network architecture enables coexistence of multiple virtual networks (VNs) on an existing physical infrastructure. VN embedding (VNE) problem, which deals with the embedding of VN components onto a physical network, is known to be -hard. In this paper, we propose two VNE algorithms: MaVEn-M and MaVEn-S. MaVEn-M employs the multicommodity flow algorithm for virtual link mapping while MaVEn-S uses the shortest-path algorithm. They formalize the virtual node mapping problem by using the Markov decision process (MDP) framework and devise action policies (node mappings) for the proposed MDP using the Monte Carlo tree search algorithm. Service providers may adjust the execution time of the MaVEn algorithms based on the traffic load of VN requests. The objective of the algorithms is to maximize the profit of infrastructure providers. We develop a discrete event VNE simulator to implement and evaluate performance of MaVEn-M, MaVEn-S, and several recently proposed VNE algorithms. We introduce profitability as a new performance metric that captures both acceptance and revenue to cost ratios. Simulation results show that the proposed algorithms find more profitable solutions than the existing algorithms. Given additional computation time, they further improve embedding solutions.
Directory of Open Access Journals (Sweden)
Markowski Marcin
2017-09-01
Full Text Available In recent years elastic optical networks have been perceived as a prospective choice for future optical networks due to better adjustment and utilization of optical resources than is the case with traditional wavelength division multiplexing networks. In the paper we investigate the elastic architecture as the communication network for distributed data centers. We address the problems of optimization of routing and spectrum assignment for large-scale computing systems based on an elastic optical architecture; particularly, we concentrate on anycast user to data center traffic optimization. We assume that computational resources of data centers are limited. For this offline problems we formulate the integer linear programming model and propose a few heuristics, including a meta-heuristic algorithm based on a tabu search method. We report computational results, presenting the quality of approximate solutions and efficiency of the proposed heuristics, and we also analyze and compare some data center allocation scenarios.
Intelligent process mapping through systematic improvement of heuristics
Ieumwananonthachai, Arthur; Aizawa, Akiko N.; Schwartz, Steven R.; Wah, Benjamin W.; Yan, Jerry C.
1992-01-01
The present system for automatic learning/evaluation of novel heuristic methods applicable to the mapping of communication-process sets on a computer network has its basis in the testing of a population of competing heuristic methods within a fixed time-constraint. The TEACHER 4.1 prototype learning system implemented or learning new postgame analysis heuristic methods iteratively generates and refines the mappings of a set of communicating processes on a computer network. A systematic exploration of the space of possible heuristic methods is shown to promise significant improvement.
Archer, Charles J [Rochester, MN; Blocksome, Michael A [Rochester, MN; Heidelberger, Philip [Cortlandt Manor, NY; Kumar, Sameer [White Plains, NY; Parker, Jeffrey J [Rochester, MN; Ratterman, Joseph D [Rochester, MN
2011-06-07
Methods, compute nodes, and computer program products are provided for heuristic status polling of a component in a computing system. Embodiments include receiving, by a polling module from a requesting application, a status request requesting status of a component; determining, by the polling module, whether an activity history for the component satisfies heuristic polling criteria; polling, by the polling module, the component for status if the activity history for the component satisfies the heuristic polling criteria; and not polling, by the polling module, the component for status if the activity history for the component does not satisfy the heuristic criteria.
A review of parameters and heuristics for guiding metabolic pathfinding.
Kim, Sarah M; Peña, Matthew I; Moll, Mark; Bennett, George N; Kavraki, Lydia E
2017-09-15
Recent developments in metabolic engineering have led to the successful biosynthesis of valuable products, such as the precursor of the antimalarial compound, artemisinin, and opioid precursor, thebaine. Synthesizing these traditionally plant-derived compounds in genetically modified yeast cells introduces the possibility of significantly reducing the total time and resources required for their production, and in turn, allows these valuable compounds to become cheaper and more readily available. Most biosynthesis pathways used in metabolic engineering applications have been discovered manually, requiring a tedious search of existing literature and metabolic databases. However, the recent rapid development of available metabolic information has enabled the development of automated approaches for identifying novel pathways. Computer-assisted pathfinding has the potential to save biochemists time in the initial discovery steps of metabolic engineering. In this paper, we review the parameters and heuristics used to guide the search in recent pathfinding algorithms. These parameters and heuristics capture information on the metabolic network structure, compound structures, reaction features, and organism-specificity of pathways. No one metabolic pathfinding algorithm or search parameter stands out as the best to use broadly for solving the pathfinding problem, as each method and parameter has its own strengths and shortcomings. As assisted pathfinding approaches continue to become more sophisticated, the development of better methods for visualizing pathway results and integrating these results into existing metabolic engineering practices is also important for encouraging wider use of these pathfinding methods.
Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm.
Wu, Haizhou; Zhou, Yongquan; Luo, Qifang; Basset, Mohamed Abdel
2016-01-01
Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem. In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs). In this paper, SOS is employed as a new method for training FNNs. To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms. The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed. It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.
Adaptive Large Neighbourhood Search
DEFF Research Database (Denmark)
Røpke, Stefan
Large neighborhood search is a metaheuristic that has gained popularity in recent years. The heuristic repeatedly moves from solution to solution by first partially destroying the solution and then repairing it. The best solution observed during this search is presented as the final solution....... This tutorial introduces the large neighborhood search metaheuristic and the variant adaptive large neighborhood search that dynamically tunes parameters of the heuristic while it is running. Both heuristics belong to a broader class of heuristics that are searching a solution space using very large...
Heuristic Inquiry: A Personal Journey of Acculturation and Identity Reconstruction
Djuraskovic, Ivana; Arthur, Nancy
2010-01-01
Heuristic methodology attempts to discover the nature and meaning of phenomenon through internal self-search, exploration, and discovery. Heuristic methodology encourages the researcher to explore and pursue the creative journey that begins inside one's being and ultimately uncovers its direction and meaning through internal discovery (Douglass &…
A genetic algorithm selection perturbative hyper-heuristic for solving ...
African Journals Online (AJOL)
Hyper-heuristics, on the other hand, search a heuristic space with the aim of providing a more generalized solution to the particular optimisation problem. This is a fairly new technique that has proven to be successful in solving various combinatorial optimisation problems. There has not been much research into the use of ...
A Heuristic Bioinspired for 8-Piece Puzzle
Machado, M. O.; Fabres, P. A.; Melo, J. C. L.
2017-10-01
This paper investigates a mathematical model inspired by nature, and presents a Meta-Heuristic that is efficient in improving the performance of an informed search, when using strategy A * using a General Search Tree as data structure. The work hypothesis suggests that the investigated meta-heuristic is optimal in nature and may be promising in minimizing the computational resources required by an objective-based agent in solving high computational complexity problems (n-part puzzle) as well as In the optimization of objective functions for local search agents. The objective of this work is to describe qualitatively the characteristics and properties of the mathematical model investigated, correlating the main concepts of the A * function with the significant variables of the metaheuristic used. The article shows that the amount of memory required to perform this search when using the metaheuristic is less than using the A * function to evaluate the nodes of a general search tree for the eight-piece puzzle. It is concluded that the meta-heuristic must be parameterized according to the chosen heuristic and the level of the tree that contains the possible solutions to the chosen problem.
Case-Based Reasoning as a Heuristic Selector in a Hyper-Heuristic for Course Timetabling Problems
Petrovic, Sanja; Qu, Rong
2002-01-01
This paper studies Knowledge Discovery (KD) using Tabu Search and Hill Climbing within Case-Based Reasoning (CBR) as a hyper-heuristic method for course timetabling problems. The aim of the hyper-heuristic is to choose the best heuristic(s) for given timetabling problems according to the knowledge stored in the case base. KD in CBR is a 2-stage iterative process on both case representation and the case base. Experimental results are analysed and related research issues for future work are dis...
Mandal, Sudip; Saha, Goutam; Pal, Rajat Kumar
2017-08-01
Correct inference of genetic regulations inside a cell from the biological database like time series microarray data is one of the greatest challenges in post genomic era for biologists and researchers. Recurrent Neural Network (RNN) is one of the most popular and simple approach to model the dynamics as well as to infer correct dependencies among genes. Inspired by the behavior of social elephants, we propose a new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN). This algorithm is mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques. Initially, the algorithm is tested against benchmark small and medium scale artificial genetic networks without and with presence of different noise levels and the efficiency was observed in term of parametric error, minimum fitness value, execution time, accuracy of prediction of true regulation, etc. Next, the proposed algorithm is tested against the real time gene expression data of Escherichia Coli SOS Network and results were also compared with others state of the art optimization methods. The experimental results suggest that ESWSA is very efficient for GRN inference problem and performs better than other methods in many ways.
Using tree diversity to compare phylogenetic heuristics.
Sul, Seung-Jin; Matthews, Suzanne; Williams, Tiffani L
2009-04-29
Evolutionary trees are family trees that represent the relationships between a group of organisms. Phylogenetic heuristics are used to search stochastically for the best-scoring trees in tree space. Given that better tree scores are believed to be better approximations of the true phylogeny, traditional evaluation techniques have used tree scores to determine the heuristics that find the best scores in the fastest time. We develop new techniques to evaluate phylogenetic heuristics based on both tree scores and topologies to compare Pauprat and Rec-I-DCM3, two popular Maximum Parsimony search algorithms. Our results show that although Pauprat and Rec-I-DCM3 find the trees with the same best scores, topologically these trees are quite different. Furthermore, the Rec-I-DCM3 trees cluster distinctly from the Pauprat trees. In addition to our heatmap visualizations of using parsimony scores and the Robinson-Foulds distance to compare best-scoring trees found by the two heuristics, we also develop entropy-based methods to show the diversity of the trees found. Overall, Pauprat identifies more diverse trees than Rec-I-DCM3. Overall, our work shows that there is value to comparing heuristics beyond the parsimony scores that they find. Pauprat is a slower heuristic than Rec-I-DCM3. However, our work shows that there is tremendous value in using Pauprat to reconstruct trees-especially since it finds identical scoring but topologically distinct trees. Hence, instead of discounting Pauprat, effort should go in improving its implementation. Ultimately, improved performance measures lead to better phylogenetic heuristics and will result in better approximations of the true evolutionary history of the organisms of interest.
Recursive heuristic classification
Wilkins, David C.
1994-01-01
The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.
Heuristics Considering UX and Quality Criteria for Heuristics
Directory of Open Access Journals (Sweden)
Frederik Bader
2017-12-01
Full Text Available Heuristic evaluation is a cheap tool with which one can take qualitative measures of a product’s usability. However, since the methodology was first presented, the User Experience (UX has become more popular but the heuristics have remained the same. In this paper, we analyse the current state of heuristic evaluation in terms of heuristics for measuring the UX. To do so, we carried out a literature review. In addition, we had a look at different heuristics and mapped them with the UX dimensions of the User Experience Questionnaire (UEQ. Moreover, we proposed a quality model for heuristic evaluation and a list of quality criteria for heuristics.
Mendyk, Aleksander; Güres, Sinan; Jachowicz, Renata; Szlęk, Jakub; Polak, Sebastian; Wiśniowska, Barbara; Kleinebudde, Peter
2015-01-01
The purpose of this work was to develop a mathematical model of the drug dissolution (Q) from the solid lipid extrudates based on the empirical approach. Artificial neural networks (ANNs) and genetic programming (GP) tools were used. Sensitivity analysis of ANNs provided reduction of the original input vector. GP allowed creation of the mathematical equation in two major approaches: (1) direct modeling of Q versus extrudate diameter (d) and the time variable (t) and (2) indirect modeling through Weibull equation. ANNs provided also information about minimum achievable generalization error and the way to enhance the original dataset used for adjustment of the equations' parameters. Two inputs were found important for the drug dissolution: d and t. The extrudates length (L) was found not important. Both GP modeling approaches allowed creation of relatively simple equations with their predictive performance comparable to the ANNs (root mean squared error (RMSE) from 2.19 to 2.33). The direct mode of GP modeling of Q versus d and t resulted in the most robust model. The idea of how to combine ANNs and GP in order to escape ANNs' black-box drawback without losing their superior predictive performance was demonstrated. Open Source software was used to deliver the state-of-the-art models and modeling strategies.
In search of an adequate yet affordable tutor in online learning networks
Sloep, Peter; Van Rosmalen, Peter; Kester, Liesbeth; Brouns, Francis; Koper, Rob
2006-01-01
Sloep, P., van Rosmalen, P., Kester, L., Brouns, F. M. R., & Koper, E. J. R. (2006). In search of an adequate yet affordable tutor in online learning networks. In search of an adequate yet affordable tutor in online learning networks. Presentation at the 6th IEEE International Conference on Advanced
Pitfalls in Teaching Judgment Heuristics
Shepperd, James A.; Koch, Erika J.
2005-01-01
Demonstrations of judgment heuristics typically focus on how heuristics can lead to poor judgments. However, exclusive focus on the negative consequences of heuristics can prove problematic. We illustrate the problem with the representativeness heuristic and present a study (N = 45) that examined how examples influence understanding of the…
Directory of Open Access Journals (Sweden)
De-Xin Yu
2013-01-01
Full Text Available Combined with improved Pallottino parallel algorithm, this paper proposes a large-scale route search method, which considers travelers’ route choice preferences. And urban road network is decomposed into multilayers effectively. Utilizing generalized travel time as road impedance function, the method builds a new multilayer and multitasking road network data storage structure with object-oriented class definition. Then, the proposed path search algorithm is verified by using the real road network of Guangzhou city as an example. By the sensitive experiments, we make a comparative analysis of the proposed path search method with the current advanced optimal path algorithms. The results demonstrate that the proposed method can increase the road network search efficiency by more than 16% under different search proportion requests, node numbers, and computing process numbers, respectively. Therefore, this method is a great breakthrough in the guidance field of urban road network.
Search Result Caching in Peer-to-Peer Information Retrieval Networks
Tigelaar, A.S.; Hiemstra, Djoerd; Trieschnigg, Rudolf Berend
2011-01-01
For peer-to-peer web search engines it is important to quickly process queries and return search results. How to keep the perceived latency low is an open challenge. In this paper we explore the solution potential of search result caching in large-scale peer-to-peer information retrieval networks by
Srinivas, B; Kulick, S N; Doran, Christine; Kulick, Seth
1995-01-01
There are currently two philosophies for building grammars and parsers -- Statistically induced grammars and Wide-coverage grammars. One way to combine the strengths of both approaches is to have a wide-coverage grammar with a heuristic component which is domain independent but whose contribution is tuned to particular domains. In this paper, we discuss a three-stage approach to disambiguation in the context of a lexicalized grammar, using a variety of domain independent heuristic techniques. We present a training algorithm which uses hand-bracketed treebank parses to set the weights of these heuristics. We compare the performance of our grammar against the performance of the IBM statistical grammar, using both untrained and trained weights for the heuristics.
Multi-Objective Optimization for Trustworthy Tactical Networks: A Survey and Insights
2013-06-01
metaheuristics , high-level algorithmic strategies that direct other heuristics or algorithms while searching through the feasible solution space in...Networks Using Metaheuristics , Auerbach Publications, Taylors and Francis Group, 2007. [10] N. Edalat, C. Than, and W. Xiao, “An auction-based strategy...Categorized as metaheuristics , high-level algorithmic strategies that direct other heuristics or algorithms • Search through the feasible
Hyper-heuristics with low level parameter adaptation.
Ren, Zhilei; Jiang, He; Xuan, Jifeng; Luo, Zhongxuan
2012-01-01
Recent years have witnessed the great success of hyper-heuristics applying to numerous real-world applications. Hyper-heuristics raise the generality of search methodologies by manipulating a set of low level heuristics (LLHs) to solve problems, and aim to automate the algorithm design process. However, those LLHs are usually parameterized, which may contradict the domain independent motivation of hyper-heuristics. In this paper, we show how to automatically maintain low level parameters (LLPs) using a hyper-heuristic with LLP adaptation (AD-HH), and exemplify the feasibility of AD-HH by adaptively maintaining the LLPs for two hyper-heuristic models. Furthermore, aiming at tackling the search space expansion due to the LLP adaptation, we apply a heuristic space reduction (SAR) mechanism to improve the AD-HH framework. The integration of the LLP adaptation and the SAR mechanism is able to explore the heuristic space more effectively and efficiently. To evaluate the performance of the proposed algorithms, we choose the p-median problem as a case study. The empirical results show that with the adaptation of the LLPs and the SAR mechanism, the proposed algorithms are able to achieve competitive results over the three heterogeneous classes of benchmark instances.
Learning Bayesian network classifiers for credit scoring using Markov Chain Monte Carlo search
Baesens, B.; Egmont-Petersen, M.; Castelo, R.; Vanthienen, J.
2001-01-01
In this paper, we will evaluate the power and usefulness of Bayesian network classifiers for credit scoring. Various types of Bayesian network classifiers will be evaluated and contrasted including unrestricted Bayesian network classifiers learnt using Markov Chain Monte Carlo (MCMC) search.
Nearest Neighbor Search in the Metric Space of a Complex Network for Community Detection
Directory of Open Access Journals (Sweden)
Suman Saha
2016-03-01
Full Text Available The objective of this article is to bridge the gap between two important research directions: (1 nearest neighbor search, which is a fundamental computational tool for large data analysis; and (2 complex network analysis, which deals with large real graphs but is generally studied via graph theoretic analysis or spectral analysis. In this article, we have studied the nearest neighbor search problem in a complex network by the development of a suitable notion of nearness. The computation of efficient nearest neighbor search among the nodes of a complex network using the metric tree and locality sensitive hashing (LSH are also studied and experimented. For evaluation of the proposed nearest neighbor search in a complex network, we applied it to a network community detection problem. Experiments are performed to verify the usefulness of nearness measures for the complex networks, the role of metric tree and LSH to compute fast and approximate node nearness and the the efficiency of community detection using nearest neighbor search. We observed that nearest neighbor between network nodes is a very efficient tool to explore better the community structure of the real networks. Several efficient approximation schemes are very useful for large networks, which hardly made any degradation of results, whereas they save lot of computational times, and nearest neighbor based community detection approach is very competitive in terms of efficiency and time.
Gene selection heuristic algorithm for nutrigenomics studies.
Valour, D; Hue, I; Grimard, B; Valour, B
2013-07-15
Large datasets from -omics studies need to be deeply investigated. The aim of this paper is to provide a new method (LEM method) for the search of transcriptome and metabolome connections. The heuristic algorithm here described extends the classical canonical correlation analysis (CCA) to a high number of variables (without regularization) and combines well-conditioning and fast-computing in "R." Reduced CCA models are summarized in PageRank matrices, the product of which gives a stochastic matrix that resumes the self-avoiding walk covered by the algorithm. Then, a homogeneous Markov process applied to this stochastic matrix converges the probabilities of interconnection between genes, providing a selection of disjointed subsets of genes. This is an alternative to regularized generalized CCA for the determination of blocks within the structure matrix. Each gene subset is thus linked to the whole metabolic or clinical dataset that represents the biological phenotype of interest. Moreover, this selection process reaches the aim of biologists who often need small sets of genes for further validation or extended phenotyping. The algorithm is shown to work efficiently on three published datasets, resulting in meaningfully broadened gene networks.
An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics
Baluja, Shumeet
1995-01-01
This report is a repository of the results obtained from a large scale empirical comparison of seven iterative and evolution-based optimization heuristics. Twenty-seven static optimization problems, spanning six sets of problem classes which are commonly explored in genetic algorithm literature, are examined. The problem sets include job-shop scheduling, traveling salesman, knapsack, binpacking, neural network weight optimization, and standard numerical optimization. The search spaces in these problems range from 2368 to 22040. The results indicate that using genetic algorithms for the optimization of static functions does not yield a benefit, in terms of the final answer obtained, over simpler optimization heuristics. Descriptions of the algorithms tested and the encodings of the problems are described in detail for reproducibility.
Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K
2016-01-01
The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.
Directory of Open Access Journals (Sweden)
Sudip Mandal
2016-01-01
Full Text Available The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.
Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems.
Moreno-Scott, Jorge Humberto; Ortiz-Bayliss, José Carlos; Terashima-Marín, Hugo; Conant-Pablos, Santiago Enrique
2016-01-01
Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems.
How Will Online Affiliate Marketing Networks Impact Search Engine Rankings?
D. Janssen (David); H.W.G.M. van Heck (Eric)
2007-01-01
textabstractIn online affiliate marketing networks advertising web sites offer their affiliates revenues based on provided web site traffic and associated leads and sales. Advertising web sites can have a network of thousands of affiliates providing them with web site traffic through hyperlinks on
In search of stars: network formation among heterogeneous agents
Goeree, J.K.; Riedl, A.; Ule, A.
2007-01-01
This paper reports results from a laboratory experiment on network formation among heterogeneous agents. The experimental design extends the Bala-Goyal (2000) model of network formation with decay and two-way flow of benefits by allowing for agents with lower linking costs or higher benefits to
A quantum heuristic algorithm for the traveling salesman problem
Bang, Jeongho; Ryu, Junghee; Lee, Changhyoup; Yoo, Seokwon; Lim, James; Lee, Jinhyoung
2012-12-01
We propose a quantum heuristic algorithm to solve the traveling salesman problem by generalizing the Grover search. Sufficient conditions are derived to greatly enhance the probability of finding the tours with the cheapest costs reaching almost to unity. These conditions are characterized by the statistical properties of tour costs and are shown to be automatically satisfied in the large-number limit of cities. In particular for a continuous distribution of the tours along the cost, we show that the quantum heuristic algorithm exhibits a quadratic speedup compared to its classical heuristic algorithm.
Models and Tabu Search Metaheuristics for Service Network Design with Asset-Balance Requirements
DEFF Research Database (Denmark)
Pedersen, Michael Berliner; Crainic, T.G.; Madsen, Oli B.G.
2009-01-01
design model, a generalization of the capacitated multicommodity network design model generally used in service network design applications. Both arc-and cycle-based formulations for the new model are presented. The paper also proposes a tabu search metaheuristic framework for the arc-based formulation...
Tensions in R&D networks : Implications for knowledge search and integration
Ritala, Paavo; Huizingh, Eelko; Almpanopoulou, Argyro; Wijbenga, Paul
R&D Networks comprise different actors with various goals and motivations. Thus, such networks are filled with tensions that emerge from simultaneously existing, competing or contradictory organizing elements and demands. In this study, we examine the knowledge search and integration behaviour of
Boussalis, Dhemetrios; Wang, Shyh J.
1992-01-01
This paper presents a method for utilizing artificial neural networks for direct adaptive control of dynamic systems with poorly known dynamics. The neural network weights (controller gains) are adapted in real time using state measurements and a random search optimization algorithm. The results are demonstrated via simulation using two highly nonlinear systems.
Heuristics for the economic dispatch problem
Energy Technology Data Exchange (ETDEWEB)
Flores, Benjamin Carpio [Centro Nacional de Controle de Energia (CENACE), Mexico, D.F. (Mexico). Dept. de Planificacion Economica de Largo Plazo], E-mail: benjamin.carpo@cfe.gob.mx; Laureano Cruces, A.L.; Lopez Bracho, R.; Ramirez Rodriguez, J. [Universidad Autonoma Metropolitana (UAM), Mexico, D.F. (Brazil). Dept. de Sistemas], Emails: clc@correo.azc.uam.mx, rlb@correo.azc.uam.mx, jararo@correo.azc.uam.mx
2009-07-01
This paper presents GRASP (Greedy Randomized Adaptive Search Procedure), Simulated Annealing (SAA), Genetic (GA), and Hybrid Genetic (HGA) Algorithms for the economic dispatch problem (EDP), considering non-convex cost functions and dead zones the only restrictions, showing the results obtained. We also present parameter settings that are specifically applicable to the EDP, and a comparative table of results for each heuristic. It is shown that these methods outperform the classical methods without the need to assume convexity of the target function. (author)
Information Spread of Emergency Events: Path Searching on Social Networks
National Research Council Canada - National Science Library
Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui
2014-01-01
.... In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events...
In search for a robust design of environmental sensor networks.
Budi, Setia; Susanto, Ferry; de Souza, Paulo; Timms, Greg; Malhotra, Vishv; Turner, Paul
2018-03-01
This paper presents an approach to the design of environmental sensor networks (ESN) which aims at providing a robust, fit-for-purpose network with minimum redundancy. A set of near optimum ESN designs is sought using an evolutionary algorithm, which incorporates redundancy and robustness as fitness functions. This work can assist the decision-making process when determining the number of sensor nodes and how the nodes are going to be deployed in the region of interest.
Heuristic decision making in medicine.
Marewski, Julian N; Gigerenzer, Gerd
2012-03-01
Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care.
Heuristic decision making in medicine
Marewski, Julian N.; Gigerenzer, Gerd
2012-01-01
Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care. PMID:22577307
On the Importance of Elimination Heuristics in Lazy Propagation
DEFF Research Database (Denmark)
Madsen, Anders Læsø; Butz, Cory J.
2012-01-01
elimination orders on-line. This paper considers the importance of elimination heuristics in LP when using Variable Elimination (VE) as the message and single marginal computation algorithm. It considers well-known cost measures for selecting the next variable to eliminate and a new cost measure....... The empirical evaluation examines dierent heuristics as well as sequences of cost measures, and was conducted on real-world and randomly generated Bayesian networks. The results show that for most cases performance is robust relative to the cost measure used and in some cases the elimination heuristic can have...
Heuristic of radiodiagnostic systems
Energy Technology Data Exchange (ETDEWEB)
Wackenheim, A.
1986-12-01
In the practice of creating expert systems, the radiologist and his team are considered as the expert who leads the job of the cognitian or cognitician. Different kinds of expert systems can be imagined. The author describes the main characteristics of heuristics in redefining semiology, semantics and rules of picture reading. Finally it is the experience of the couple expert and cognitician which will in the futur grant for the success of expert systems in radiology.
Time constrained liner shipping network design
DEFF Research Database (Denmark)
Karsten, Christian Vad; Brouer, Berit Dangaard; Desaulniers, Guy
2017-01-01
We present a mathematical model and a solution method for the liner shipping network design problem. The model takes into account coordination between vessels and transit time restrictions on the cargo flow. The solution method is an improvement heuristic, where an integer program is solved...... iteratively to perform moves in a large neighborhood search. Our improvement heuristic is applicable as a real-time decision support tool for a liner shipping company. It can be used to find improvements to the network when evaluating changes in operating conditions or testing different scenarios...
Mastering the game of Go with deep neural networks and tree search.
Silver, David; Huang, Aja; Maddison, Chris J; Guez, Arthur; Sifre, Laurent; van den Driessche, George; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal; Sutskever, Ilya; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore; Hassabis, Demis
2016-01-28
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
Efficient heuristics for the Rural Postman Problem
Directory of Open Access Journals (Sweden)
GW Groves
2005-06-01
Full Text Available A local search framework for the (undirected Rural Postman Problem (RPP is presented in this paper. The framework allows local search approaches that have been applied successfully to the well–known Travelling Salesman Problem also to be applied to the RPP. New heuristics for the RPP, based on this framework, are introduced and these are capable of solving significantly larger instances of the RPP than have been reported in the literature. Test results are presented for a number of benchmark RPP instances in a bid to compare efficiency and solution quality against known methods.
Heuristics for the Robust Coloring Problem
Directory of Open Access Journals (Sweden)
Miguel Ángel Gutiérrez Andrade
2011-03-01
Full Text Available Let $G$ and $\\bar{G}$ be complementary graphs. Given a penalty function defined on the edges of $G$, we will say that the rigidity of a $k$-coloring of $G$ is the sum of the penalties of the edges of G joining vertices of the same color. Based on the previous definition, the Robust Coloring Problem (RCP is stated as the search of the minimum rigidity $k$-coloring. In this work a comparison of heuristics based on simulated annealing, GRASP and scatter search is presented. These are the best results for the RCP that have been obtained.
Bashiri, Mahdi; Karimi, Hossein
2012-07-01
Quadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature. In this paper, we applied 2-opt, greedy 2-opt, 3-opt, greedy 3-opt, and VNZ as heuristic methods and tabu search (TS), simulated annealing, and particle swarm optimization as meta-heuristic methods for the QAP. This research is dedicated to compare the relative percentage deviation of these solution qualities from the best known solution which is introduced in QAPLIB. Furthermore, a tuning method is applied for meta-heuristic parameters. Results indicate that TS is the best in 31%of QAPs, and the IFLS method, which is in the literature, is the best in 58 % of QAPs; these two methods are the same in 11 % of test problems. Also, TS has a better computational time among heuristic and meta-heuristic methods.
Social Networks and their Importance in Job Searching of College Students
Directory of Open Access Journals (Sweden)
Marek Potkány
2015-03-01
Full Text Available At present, in every sphere of human activity, using modern ICT is considered as a matter of course. Several human resources management institutions are aware of the potential of social networks in estabilishing and building relationships with their target groups. It is a trend to create job portals in social networks. These are currently an integrated part of communication with target audience and therefore also an objects of attention and reflexions. It is also the topic of this paper. The goal of this research is to determine the level of use of social networks by college students in Slovakia. Part of the research is also to discover the effect of social networks on job searching of a selected sample of students. The research was conducted on a sample of 407 slovak students. A questioning method in form of an online questionnaire was used. The obtained results were processed using methods of descriptive and test statistics. We studied the significance of gender on the frequency of using the internet to search for job opportunities via social networks, as well as the ability to apprehend the importance of personal presentation on social networks. The statistical testing did not prove any significant difference between men and women in the question of frequency of using internet to search for job opportunities, nor in the ability to apprehend the importance of personal presentation on social networks. A statistically significant difference in gender was proven only in case of using social networks in job searching. Women tend to use social networks to find jobs more often than men. These statements are proved by the results of tests of significance of mean changes at the 5% significance level, which means that they are valid with 95% probability.
A System for Automatically Generating Scheduling Heuristics
Morris, Robert
1996-01-01
The goal of this research is to improve the performance of automated schedulers by designing and implementing an algorithm by automatically generating heuristics by selecting a schedule. The particular application selected by applying this method solves the problem of scheduling telescope observations, and is called the Associate Principal Astronomer. The input to the APA scheduler is a set of observation requests submitted by one or more astronomers. Each observation request specifies an observation program as well as scheduling constraints and preferences associated with the program. The scheduler employs greedy heuristic search to synthesize a schedule that satisfies all hard constraints of the domain and achieves a good score with respect to soft constraints expressed as an objective function established by an astronomer-user.
Multitarget search on complex networks: A logarithmic growth of global mean random cover time
Weng, Tongfeng; Zhang, Jie; Small, Michael; Yang, Ji; Bijarbooneh, Farshid Hassani; Hui, Pan
2017-09-01
We investigate multitarget search on complex networks and derive an exact expression for the mean random cover time that quantifies the expected time a walker needs to visit multiple targets. Based on this, we recover and extend some interesting results of multitarget search on networks. Specifically, we observe the logarithmic increase of the global mean random cover time with the target number for a broad range of random search processes, including generic random walks, biased random walks, and maximal entropy random walks. We show that the logarithmic growth pattern is a universal feature of multi-target search on networks by using the annealed network approach and the Sherman-Morrison formula. Moreover, we find that for biased random walks, the global mean random cover time can be minimized, and that the corresponding optimal parameter also minimizes the global mean first passage time, pointing towards its robustness. Our findings further confirm that the logarithmic growth pattern is a universal law governing multitarget search in confined media.
Batt, Gregory; Page, Michel; Cantone, Irene; Goessler, Gregor; Monteiro, Pedro; de Jong, Hidde
2010-09-15
Investigating the relation between the structure and behavior of complex biological networks often involves posing the question if the hypothesized structure of a regulatory network is consistent with the observed behavior, or if a proposed structure can generate a desired behavior. The above questions can be cast into a parameter search problem for qualitative models of regulatory networks. We develop a method based on symbolic model checking that avoids enumerating all possible parametrizations, and show that this method performs well on real biological problems, using the IRMA synthetic network and benchmark datasets. We test the consistency between IRMA and time-series expression profiles, and search for parameter modifications that would make the external control of the system behavior more robust. GNA and the IRMA model are available at http://ibis.inrialpes.fr/.
Harmony search optimization algorithm for a novel transportation problem in a consolidation network
Davod Hosseini, Seyed; Akbarpour Shirazi, Mohsen; Taghi Fatemi Ghomi, Seyed Mohammad
2014-11-01
This article presents a new harmony search optimization algorithm to solve a novel integer programming model developed for a consolidation network. In this network, a set of vehicles is used to transport goods from suppliers to their corresponding customers via two transportation systems: direct shipment and milk run logistics. The objective of this problem is to minimize the total shipping cost in the network, so it tries to reduce the number of required vehicles using an efficient vehicle routing strategy in the solution approach. Solving several numerical examples confirms that the proposed solution approach based on the harmony search algorithm performs much better than CPLEX in reducing both the shipping cost in the network and computational time requirement, especially for realistic size problem instances.
A Tutorial on Heuristic Methods
DEFF Research Database (Denmark)
Vidal, Rene Victor Valqui; Werra, D. de; Silver, E.
1980-01-01
In this paper we define a heuristic method as a procedure for solving a well-defined mathematical problem by an intuitive approach in which the structure of the problem can be interpreted and exploited intelligently to obtain a reasonable solution. Issues discussed include: (i) the measurement...... of the quality of a heuristic method, (ii) different types of heuristic procedures, (iii) the interactive role of human beings and (iv) factors that may influence the choice or testing of heuristic methods. A large number of references are included....
(AJST) TABU SEARCH HEURISTIC FOR UNIVERSITY COURSE ...
African Journals Online (AJOL)
Applications of Artificial Intelligence and Expert. Systems, Edinburgh. 4. Daskalaki S., Birbas T., Housos E., (2004), An Integer. Programming formulation for a case study in. University timetabling, European Journal of. Operational Research, Vol. 153, pp. 117-135. 5. ElMohamed M., Fox G. (1998), A Comparison of.
Numerical Algorithms for Personalized Search in Self-organizing Information Networks
Kamvar, Sep
2010-01-01
This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quad
Multi-tree Coding Method (MCM) for drainage networks supporting high-efficient search
Wang, Hao; Fu, Xudong; Wang, Guangqian
2013-03-01
River coding method for drainage networks plays an very important role in the physical simulation of river basins. In this study we developed a new river coding method named Multi-tree Coding Method (MCM), which has the following features: (1) it is established on a topological pattern reflecting the dendriform structure of drainage networks; (2) the multi-tree code can be effectively managed by the database to perform convenient topological search toward drainage networks using Structured Query Language (SQL); (3) the multi-tree code does not exhibit digital overflow problems in the computer, thus any resolution and scale drainage networks can easily be coded; and (4) it supports high-efficient search process. A river reach can be directly positioned in a drainage network under MCM, without the complex search process from all river reaches. This feature has great potential to improve the computational performance of basin models. We demonstrate here the efficiency and practicality of MCM by testing it in the Yalu Tsangpo river basin, Tibet. A drainage network with 140,745 digital reaches was extracted from the digital elevation model (DEM), and the multi-tree codes of all river reaches were obtained.
Meta-heuristic algorithms as tools for hydrological science
Yoo, Do Guen; Kim, Joong Hoon
2014-12-01
In this paper, meta-heuristic optimization techniques are introduced and their applications to water resources engineering, particularly in hydrological science are introduced. In recent years, meta-heuristic optimization techniques have been introduced that can overcome the problems inherent in iterative simulations. These methods are able to find good solutions and require limited computation time and memory use without requiring complex derivatives. Simulation-based meta-heuristic methods such as Genetic algorithms (GAs) and Harmony Search (HS) have powerful searching abilities, which can occasionally overcome the several drawbacks of traditional mathematical methods. For example, HS algorithms can be conceptualized from a musical performance process and used to achieve better harmony; such optimization algorithms seek a near global optimum determined by the value of an objective function, providing a more robust determination of musical performance than can be achieved through typical aesthetic estimation. In this paper, meta-heuristic algorithms and their applications (focus on GAs and HS) in hydrological science are discussed by subject, including a review of existing literature in the field. Then, recent trends in optimization are presented and a relatively new technique such as Smallest Small World Cellular Harmony Search (SSWCHS) is briefly introduced, with a summary of promising results obtained in previous studies. As a result, previous studies have demonstrated that meta-heuristic algorithms are effective tools for the development of hydrological models and the management of water resources.
2015-08-17
Control based on Heuristic Dynamic Programming for Nonlinear Continuous-Time Systems In this paper, a novel predictive event-triggered control...method based on heuristic dynamic programming (HDP) algorithm is developed for nonlinear continuous-time systems. A model network is used to estimate...College Road, Suite II Kingston, RI 02881 -1967 ABSTRACT Predictive Event-Triggered Control based on Heuristic Dynamic Programming for Nonlinear
Mixed Integer Programming and Heuristic Scheduling for Space Communication
Lee, Charles H.; Cheung, Kar-Ming
2013-01-01
Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.
Mastering the game of Go with deep neural networks and tree search
Silver, David; Huang, Aja; Maddison, Chris J.; Guez, Arthur; Sifre, Laurent; van den Driessche, George; Schrittwieser, Julian; Antonoglou, Ioannis; Panneershelvam, Veda; Lanctot, Marc; Dieleman, Sander; Grewe, Dominik; Nham, John; Kalchbrenner, Nal; Sutskever, Ilya; Lillicrap, Timothy; Leach, Madeleine; Kavukcuoglu, Koray; Graepel, Thore; Hassabis, Demis
2016-01-01
The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
Using biological networks to search for interacting loci in genomewide association studies
DEFF Research Database (Denmark)
Emily, Mathieu; Mailund, Thomas; Hein, Jotun
2009-01-01
mainly affect the phenotype in combination with other variants, termed epistasis. An exhaustive search for epistatic effects is computationally demanding, as several billions of SNP pairs exist for typical genotyping chips. In this study, the experimental knowledge on biological networks is used...
Villar, Esperanca; Juan, Jaume; Corominas, Enric; Capell, Dolors
2000-01-01
Investigates the relationship between social networking and job placement of university graduates. Results reveal two different approaches to the process of job entry-use of formal or informal contacts. Significant differences were found between use of formal and informal sources of help relating to graduates' job-search intention and confidence…
In search of a network theory of innovations: relations, positions, and perspectives
Leydesdorff, L.; Ahrweiler, P.
2014-01-01
As a complement to Nelson and Winter's (1977) article titled "In Search of a Useful Theory of Innovation," a sociological perspective on innovation networks can be elaborated using Luhmann's social systems theory, on the one hand, and Latour's "sociology of translations," on the other. Because of a
Reexamining Our Bias against Heuristics
McLaughlin, Kevin; Eva, Kevin W.; Norman, Geoff R.
2014-01-01
Using heuristics offers several cognitive advantages, such as increased speed and reduced effort when making decisions, in addition to allowing us to make decision in situations where missing data do not allow for formal reasoning. But the traditional view of heuristics is that they trade accuracy for efficiency. Here the authors discuss sources…
General k-opt submoves for the Lin-Kernighan TSP heuristic
DEFF Research Database (Denmark)
Helsgaun, Keld
2009-01-01
Local search with k-exchange neighborhoods, k-opt, is the most widely used heuristic method for the traveling salesman problem (TSP). This paper presents an effective implementation of k-opt in LKH-2, a variant of the Lin–Kernighan TSP heuristic. The effectiveness of the implementation...
An Effective Implementation of K-opt Moves for the Lin-Kernighan TSP Heuristic
DEFF Research Database (Denmark)
Helsgaun, Keld
Local search with k-change neighborhoods, k-opt, is the most widely used heuristic method for the traveling salesman problem (TSP). This report presents an effective implementation of k-opt for the Lin- Kernighan TSP heuristic. The effectiveness of the implementation is demonstrated with extensive...
Mengshoel, Ole J.; Wilkins, David C.; Roth, Dan
2010-01-01
For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.
Tabu search methods for multicommodity capcitated fixed charge network design problem
Energy Technology Data Exchange (ETDEWEB)
Crainic, T.; Farvolden, J.; Gendreau, M.; Soriano, P.
1994-12-31
We address the fixed charge capacitated multicommodity network flow problem with linear costs and no additional side constraints, and present two solution approaches based on the tabu search metaheuristic. In the first case, the search is conducted by exploring the space of the design (integer) variables, while neighbors are evaluated and moves are selected via a capacitated multicommodity minimum cost network flow subproblem. The second approach integrates simplex pivoting rules into a tabu search framework. Here, the search explores the space of flow path variables when all design arcs are open, by using column generation to obtain new variables, and pivoting to determine and evaluate the neighbors of any given solution. Adapting this idea within our tabu search framework represents an interesting challenge, since several of the standard assumptions upon which column generation schemes are based are no longer verified. In particular, the monotonic decrease of the objective function value is no longer ensured, and both variable and fixed costs characterize arcs. On the other hand, the precise definition and generation of the tabu search neighborhoods in a column generation context poses an additional challenge, linked particularly to the description and identification of path variables. We describe the various components, implementation challenges and behaviour of each of the two algorithms, and compare their computational and solution quality performance. Comparisons with known bounding procedures will be presented as well.
A Bayesian network approach to the database search problem in criminal proceedings
2012-01-01
Background The ‘database search problem’, that is, the strengthening of a case - in terms of probative value - against an individual who is found as a result of a database search, has been approached during the last two decades with substantial mathematical analyses, accompanied by lively debate and centrally opposing conclusions. This represents a challenging obstacle in teaching but also hinders a balanced and coherent discussion of the topic within the wider scientific and legal community. This paper revisits and tracks the associated mathematical analyses in terms of Bayesian networks. Their derivation and discussion for capturing probabilistic arguments that explain the database search problem are outlined in detail. The resulting Bayesian networks offer a distinct view on the main debated issues, along with further clarity. Methods As a general framework for representing and analyzing formal arguments in probabilistic reasoning about uncertain target propositions (that is, whether or not a given individual is the source of a crime stain), this paper relies on graphical probability models, in particular, Bayesian networks. This graphical probability modeling approach is used to capture, within a single model, a series of key variables, such as the number of individuals in a database, the size of the population of potential crime stain sources, and the rarity of the corresponding analytical characteristics in a relevant population. Results This paper demonstrates the feasibility of deriving Bayesian network structures for analyzing, representing, and tracking the database search problem. The output of the proposed models can be shown to agree with existing but exclusively formulaic approaches. Conclusions The proposed Bayesian networks allow one to capture and analyze the currently most well-supported but reputedly counter-intuitive and difficult solution to the database search problem in a way that goes beyond the traditional, purely formulaic expressions
Directory of Open Access Journals (Sweden)
Chih-Chiang Lin
2010-01-01
Full Text Available The broadcast scheduling problem (BSP in packet radio networks is a well-known NP-complete combinatorial optimization problem. The broadcast scheduling avoids packet collisions by allowing only one node transmission in each collision domain of a time division multiple access (TDMA network. It also improves the transmission utilization by assigning one transmission time slot to one or more nodes; thus, each node transmits at least once in each time frame. An optimum transmission schedule could minimize the length of a time frame while minimizing the number of idle nodes. In this paper, we propose a new iterated local search (ILS algorithm that consists of two special perturbation and local search operators to solve the BSPs. Computational experiments are applied to benchmark data sets and randomly generated problem instances. The experimental results show that our ILS approach is effective in solving the problems with only a few runtimes, even for very large networks with 2,500 nodes.
Directory of Open Access Journals (Sweden)
Jian Yang
2016-09-01
Full Text Available In this article, a tree search algorithm is proposed to find the near optimal conflict avoidance solutions for unmanned aerial vehicles. In the dynamic environment, the unmodeled elements, such as wind, would make UAVs deviate from nominal traces. It brings about difficulties for conflict detection and resolution. The back propagation neural networks are utilized to approximate the unmodeled dynamics of the environment. To satisfy the online planning requirement, the search length of the tree search algorithm would be limited. Therefore, the algorithm may not be able to reach the goal states in search process. The midterm reward function for assessing each node is devised, with consideration given to two factors, namely, the safe separation requirement and the mission of each unmanned aerial vehicle. The simulation examples and the comparisons with previous approaches are provided to illustrate the smooth and convincing behaviours of the proposed algorithm.
Robinson, Karen A; Dunn, Adam G; Tsafnat, Guy; Glasziou, Paul
2014-07-01
Reports of randomized controlled trials (RCTs) should set findings within the context of previous research. The resulting network of citations would also provide an alternative search method for clinicians, researchers, and systematic reviewers seeking to base decisions on all available evidence. We sought to determine the connectedness of citation networks of RCTs by examining direct (referenced trials) and indirect (through references of referenced trials, etc) citation of trials to one another. Meta-analyses were used to create citation networks of RCTs addressing the same clinical questions. The primary measure was the proportion of networks where following citation links between RCTs identifies the complete set of RCTs, forming a single connected citation group. Other measures included the number of disconnected groups (islands) within each network, the number of citations in the network relative to the maximum possible, and the maximum number of links in the path between two connected trials (a measure of indirectness of citations). We included 259 meta-analyses with a total of 2,413 and a median of seven RCTs each. For 46% (118 of 259) of networks, the RCTs formed a single connected citation group-one island. For the other 54% of networks, where at least one RCT group was not cited by others, 39% had two citation islands and 4% (10 of 257) had 10 or more islands. On average, the citation networks had 38% of the possible citations to other trials (if each trial had cited all earlier trials). The number of citation islands and the maximum number of citation links increased with increasing numbers of trials in the network. Available evidence to answer a clinical question may be identified by using network citations created with a small initial corpus of eligible trials. However, the number of islands means that citation networks cannot be relied on for evidence retrieval. Copyright © 2014 Elsevier Inc. All rights reserved.
Verhoef, E.T.
2002-01-01
This paper considers the second-best problem where not all links of a congested transportation network can be tolled. This paper builds on earlier work, in which the second-best tax rule for this problem was derived for general static networks, so that the solution presented is valid for any graph
Multiple random walks on complex networks: A harmonic law predicts search time
Weng, Tongfeng; Zhang, Jie; Small, Michael; Hui, Pan
2017-05-01
We investigate multiple random walks traversing independently and concurrently on complex networks and introduce the concept of mean first parallel passage time (MFPPT) to quantify their search efficiency. The mean first parallel passage time represents the expected time required to find a given target by one or some of the multiple walkers. We develop a general theory that allows us to calculate the MFPPT analytically. Interestingly, we find that the global MFPPT follows a harmonic law with respect to the global mean first passage times of the associated walkers. Remarkably, when the properties of multiple walkers are identical, the global MFPPT decays in a power law manner with an exponent of unity, irrespective of network structure. These findings are confirmed by numerical and theoretical results on various synthetic and real networks. The harmonic law reveals a universal principle governing multiple random walks on networks that uncovers the contribution and role of the combined walkers in a target search. Our paradigm is also applicable to a broad range of random search processes.
Design and Implementation of Sound Searching Robots in Wireless Sensor Networks
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Lianfu Han
2016-09-01
Full Text Available A sound target-searching robot system which includes a 4-channel microphone array for sound collection, magneto-resistive sensor for declination measurement, and a wireless sensor networks (WSN for exchanging information is described. It has an embedded sound signal enhancement, recognition and location method, and a sound searching strategy based on a digital signal processor (DSP. As the wireless network nodes, three robots comprise the WSN a personal computer (PC in order to search the three different sound targets in task-oriented collaboration. The improved spectral subtraction method is used for noise reduction. As the feature of audio signal, Mel-frequency cepstral coefficient (MFCC is extracted. Based on the K-nearest neighbor classification method, we match the trained feature template to recognize sound signal type. This paper utilizes the improved generalized cross correlation method to estimate time delay of arrival (TDOA, and then employs spherical-interpolation for sound location according to the TDOA and the geometrical position of the microphone array. A new mapping has been proposed to direct the motor to search sound targets flexibly. As the sink node, the PC receives and displays the result processed in the WSN, and it also has the ultimate power to make decision on the received results in order to improve their accuracy. The experiment results show that the designed three-robot system implements sound target searching function without collisions and performs well.
An implementation of modified scatter search algorithm to transmission expansion planning
MEYMAND, Majid ZEINADDINI; RASHIDINEJAD, Masoud; KHORASANI, Hamid
2011-01-01
Transmission network expansion planning (TNEP) is one of the most important tasks in the field of power systems, especially in deregulated power system environments. TNEP is a nonlinear mixed integer programming problem that can be solved via hybrid heuristic algorithms. This paper presents a modified scatter search algorithm (MSSA) to reinforce the ordinary scatter search algorithm (SSA) to be equipped for handling large scale transmission expansion planning (TEP) problems. The prop...
DEFF Research Database (Denmark)
Hu, Yimei
2013-01-01
of global innovation organization and management. For example, the dissertation expands the triple helix model by adding an internationalization dimension, and elaborates and expands the emerging concepts of relational competitive advantages and orchestration capabilities. The findings of the dissertation...... also suggest that in order to improve their innovation activities, transnational corporations need to have a network mindset and facilitate global innovation in three ways: searching for innovation resources in the global business environment, establishing technological partnerships, and designing...... corporations perceive/design a network organization to facilitate their global innovation? • To what extent and how can we manage a network organization? Research focus of the dissertation is on transnational corporations’ network organization for innovation. The first research question aims to clarify...
He, Jieyue; Li, Chaojun; Ye, Baoliu; Zhong, Wei
2012-06-25
Most computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures. In this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules. The proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms. Based on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the
deal: A Package for Learning Bayesian Networks
Directory of Open Access Journals (Sweden)
Susanne G. Boettcher
2003-12-01
Full Text Available deal is a software package for use with R. It includes several methods for analysing data using Bayesian networks with variables of discrete and/or continuous types but restricted to conditionally Gaussian networks. Construction of priors for network parameters is supported and their parameters can be learned from data using conjugate updating. The network score is used as a metric to learn the structure of the network and forms the basis of a heuristic search strategy. deal has an interface to Hugin.
Formative Research on the Heuristic Task Analysis.
Reigeluth, Charles M.; Lee, Ji-Yeon; Peterson, Bruce; Chavez, Michael
Corporate and educational settings increasingly require decision-making, problem-solving and other complex cognitive skills to handle ill-structured, or heuristic, tasks, but the growing need for heuristic task expertise has outpaced the refinement of task analysis methods for heuristic expertise. The Heuristic Task Analysis (HTA) Method was…
How do people judge risks: availability heuristic, affect heuristic, or both?
Pachur, Thorsten; Hertwig, Ralph; Steinmann, Florian
2012-09-01
How does the public reckon which risks to be concerned about? The availability heuristic and the affect heuristic are key accounts of how laypeople judge risks. Yet, these two accounts have never been systematically tested against each other, nor have their predictive powers been examined across different measures of the public's risk perception. In two studies, we gauged risk perception in student samples by employing three measures (frequency, value of a statistical life, and perceived risk) and by using a homogeneous (cancer) and a classic set of heterogeneous causes of death. Based on these judgments of risk, we tested precise models of the availability heuristic and the affect heuristic and different definitions of availability and affect. Overall, availability-by-recall, a heuristic that exploits people's direct experience of occurrences of risks in their social network, conformed to people's responses best. We also found direct experience to carry a high degree of ecological validity (and one that clearly surpasses that of affective information). However, the relative impact of affective information (as compared to availability) proved more pronounced in value-of-a-statistical-life and perceived-risk judgments than in risk-frequency judgments. Encounters with risks in the media, in contrast, played a negligible role in people's judgments. Going beyond the assumption of exclusive reliance on either availability or affect, we also found evidence for mechanisms that combine both, either sequentially or in a composite fashion. We conclude with a discussion of policy implications of our results, including how to foster people's risk calibration and the success of education campaigns.
Structural Sustainability - Heuristic Approach
Rostański, Krzysztof
2017-10-01
Nowadays, we are faced with a challenge of having to join building structures with elements of nature, which seems to be the paradigm of modern planning and design. The questions arise, however, with reference to the following categories: the leading idea, the relation between elements of nature and buildings, the features of a structure combining such elements and, finally, our perception of this structure. If we consider both the overwhelming globalization and our attempts to preserve local values, the only reasonable solution is to develop naturalistic greenery. It can add its uniqueness to any building and to any developed area. Our holistic model, presented in this paper, contains the above mentioned categories within the scope of naturalism. The model is divided into principles, actions related, and possible effects to be obtained. It provides a useful tool for determining the ways and priorities of our design. Although it is not possible to consider all possible actions and solutions in order to support sustainability in any particular design, we can choose, however, a proper mode for our design according to the local conditions by turning to the heuristic method, which helps to choose priorities and targets. Our approach is an attempt to follow the ways of nature as in the natural environment it is optimal solutions that appear and survive, idealism being the domain of mankind only. We try to describe various natural processes in a manner comprehensible to us, which is always a generalization. Such definitions, however, called artificial by naturalists, are presented as art or the current state of knowledge by artists and engineers. Reality, in fact, is always more complicated than its definitions. The heuristic method demonstrates the way how to optimize our design. It requires that all possible information about the local environment should be gathered, as the more is known, the fewer mistakes are made. Following the unquestionable principles, we can
Triplet supertree heuristics for the tree of life.
Lin, Harris T; Burleigh, J Gordon; Eulenstein, Oliver
2009-01-30
There is much interest in developing fast and accurate supertree methods to infer the tree of life. Supertree methods combine smaller input trees with overlapping sets of taxa to make a comprehensive phylogenetic tree that contains all of the taxa in the input trees. The intrinsically hard triplet supertree problem takes a collection of input species trees and seeks a species tree (supertree) that maximizes the number of triplet subtrees that it shares with the input trees. However, the utility of this supertree problem has been limited by a lack of efficient and effective heuristics. We introduce fast hill-climbing heuristics for the triplet supertree problem that perform a step-wise search of the tree space, where each step is guided by an exact solution to an instance of a local search problem. To realize time efficient heuristics we designed the first nontrivial algorithms for two standard search problems, which greatly improve on the time complexity to the best known (naïve) solutions by a factor of n and n2 (the number of taxa in the supertree). These algorithms enable large-scale supertree analyses based on the triplet supertree problem that were previously not possible. We implemented hill-climbing heuristics that are based on our new algorithms, and in analyses of two published supertree data sets, we demonstrate that our new heuristics outperform other standard supertree methods in maximizing the number of triplets shared with the input trees. With our new heuristics, the triplet supertree problem is now computationally more tractable for large-scale supertree analyses, and it provides a potentially more accurate alternative to existing supertree methods.
Hybrid fuzzy charged system search algorithm based state estimation in distribution networks
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Sachidananda Prasad
2017-06-01
Full Text Available This paper proposes a new hybrid charged system search (CSS algorithm based state estimation in radial distribution networks in fuzzy framework. The objective of the optimization problem is to minimize the weighted square of the difference between the measured and the estimated quantity. The proposed method of state estimation considers bus voltage magnitude and phase angle as state variable along with some equality and inequality constraints for state estimation in distribution networks. A rule based fuzzy inference system has been designed to control the parameters of the CSS algorithm to achieve better balance between the exploration and exploitation capability of the algorithm. The efficiency of the proposed fuzzy adaptive charged system search (FACSS algorithm has been tested on standard IEEE 33-bus system and Indian 85-bus practical radial distribution system. The obtained results have been compared with the conventional CSS algorithm, weighted least square (WLS algorithm and particle swarm optimization (PSO for feasibility of the algorithm.
An Efficient Tabu Search DSA Algorithm for Heterogeneous Traffic in Cellular Networks
Kamal, Hany; Coupechoux, Marceau; Godlewski, Philippe
2010-01-01
International audience; In this paper, we propose and analyze a TS (Tabu Search) algorithm for DSA (Dynamic Spectrum Access) in cellular networks. We consider a scenario where cellular operators share a common access band, and we focus on the strategy of one operator providing packet services to the end-users. We consider a soft interference requirement for the algorithm's design that suits the packet traffic context. The operator's objective is to maximize its reward while taking into accoun...
Bio-Inspired Meta-Heuristics for Emergency Transportation Problems
Directory of Open Access Journals (Sweden)
Min-Xia Zhang
2014-02-01
Full Text Available Emergency transportation plays a vital role in the success of disaster rescue and relief operations, but its planning and scheduling often involve complex objectives and search spaces. In this paper, we conduct a survey of recent advances in bio-inspired meta-heuristics, including genetic algorithms (GA, particle swarm optimization (PSO, ant colony optimization (ACO, etc., for solving emergency transportation problems. We then propose a new hybrid biogeography-based optimization (BBO algorithm, which outperforms some state-of-the-art heuristics on a typical transportation planning problem.
The use of meta-heuristics for airport gate assignment
DEFF Research Database (Denmark)
Cheng, Chun-Hung; Ho, Sin C.; Kwan, Cheuk-Lam
2012-01-01
Improper assignment of gates may result in flight delays, inefficient use of the resource, customer’s dissatisfaction. A typical metropolitan airport handles hundreds of flights a day. Solving the gate assignment problem (GAP) to optimality is often impractical. Meta-heuristics have recently been...... proposed to generate good solutions within a reasonable timeframe. In this work, we attempt to assess the performance of three meta-heuristics, namely, genetic algorithm (GA), tabu search (TS), simulated annealing (SA) and a hybrid approach based on SA and TS. Flight data from Incheon International Airport...
Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search
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Zong Woo Geem
2015-07-01
Full Text Available Thus far, various phenomenon-mimicking algorithms, such as genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping, ant colony optimization, harmony search, cross entropy, scatter search, and honey-bee mating, have been proposed to optimally design the water distribution networks with respect to design cost. However, flow velocity constraint, which is critical for structural robustness against water hammer or flow circulation against substance sedimentation, was seldom considered in the optimization formulation because of computational complexity. Thus, this study proposes a novel fuzzy-based velocity reliability index, which is to be maximized while the design cost is simultaneously minimized. The velocity reliability index is included in the existing cost optimization formulation and this extended multiobjective formulation is applied to two bench-mark problems. Results show that the model successfully found a Pareto set of multiobjective design solutions in terms of cost minimization and reliability maximization.
Giongo Fernandes, Alexandre; Benjamin, Robert A.; Babler, Brian
2018-01-01
Two sets of infrared images of the Galactic Center region (|L| 100 mas/year). The two image sets come from GALCEN observations in 2005 and GLIMPSE proper observations in 2015 with matched observation modes. We use three different methods to search for these objects in extremely crowded fields: (1) comparing matched point source lists, (2) crowd sourcing by several college introductory astronomy classes in the state of Wisconsin (700 volunteers), and (3) convolutional neural networks trained using objects from the previous two methods. Before our search six high proper objects were known, four of which were found by the VVV near-infrared Galactic plane survey. We compare and describe our methods for this search, and present a preliminary catalog of high proper motions objects.
On the use of harmony search algorithm in the training of wavelet neural networks
Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline
2015-10-01
Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.
Directory of Open Access Journals (Sweden)
Recep Colak
2010-10-01
Full Text Available Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented.We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples.We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely
Zheng, Jun-Xi; Zhang, Ping; Li, Fang; Du, Guang-Long
2016-09-01
Although the sequence-dependent setup times flowshop problem with the total weighted tardiness minimization objective exists widely in industry, work on the problem has been scant in the existing literature. To the authors' best knowledge, the NEH?EWDD heuristic and the Iterated Greedy (IG) algorithm with descent local search have been regarded as the high performing heuristic and the state-of-the-art algorithm for the problem, which are both based on insertion search. In this article firstly, an efficient backtracking algorithm and a novel heuristic (HPIS) are presented for insertion search. Accordingly, two heuristics are introduced, one is NEH?EWDD with HPIS for insertion search, and the other is the combination of NEH?EWDD and both the two methods. Furthermore, the authors improve the IG algorithm with the proposed methods. Finally, experimental results show that both the proposed heuristics and the improved IG (IG*) significantly outperform the original ones.
An Iterated Local Search Algorithm for Multi-Period Water Distribution Network Design Optimization
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Annelies De Corte
2016-08-01
Full Text Available Water distribution networks consist of different components, such as reservoirs and pipes, and exist to provide users (households, agriculture, industry with high-quality water at adequate pressure and flow. Water distribution network design optimization aims to find optimal diameters for every pipe, chosen from a limited set of commercially available diameters. This combinatorial optimization problem has received a lot of attention over the past forty years. In this paper, the well-studied single-period problem is extended to a multi-period setting in which time varying demand patterns occur. Moreover, an additional constraint—which sets a maximum water velocity—is imposed. A metaheuristic technique called iterated local search is applied to tackle this challenging optimization problem. A full-factorial experiment is conducted to validate the added value of the algorithm components and to configure optimal parameter settings. The algorithm is tested on a broad range of 150 different (freely available test networks.
Network module detection: Affinity search technique with the multi-node topological overlap measure
Directory of Open Access Journals (Sweden)
Horvath Steve
2009-07-01
Full Text Available Abstract Background Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. Findings We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST, which is a generalized version of the Cluster Affinity Search Technique (CAST. MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes. We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Conclusion Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/
Search for gravitational waves associated with γ-ray bursts detected by the interplanetary network.
Aasi, J; Abbott, B P; Abbott, R; Abbott, T; Abernathy, M R; Acernese, F; Ackley, K; Adams, C; Adams, T; Addesso, P; Adhikari, R X; Affeldt, C; Agathos, M; Aggarwal, N; Aguiar, O D; Ajith, P; Alemic, A; Allen, B; Allocca, A; Amariutei, D; Andersen, M; Anderson, R A; Anderson, S B; Anderson, W G; Arai, K; Araya, M C; Arceneaux, C; Areeda, J S; Ast, S; Aston, S M; Astone, P; Aufmuth, P; Augustus, H; Aulbert, C; Aylott, B E; Babak, S; Baker, P T; Ballardin, G; Ballmer, S W; Barayoga, J C; Barbet, M; Barish, B C; Barker, D; Barone, F; Barr, B; Barsotti, L; Barsuglia, M; Barton, M A; Bartos, I; Bassiri, R; Basti, A; Batch, J C; Bauchrowitz, J; Bauer, Th S; Baune, C; Bavigadda, V; Behnke, B; Bejger, M; Beker, M G; Belczynski, C; Bell, A S; Bell, C; Bergmann, G; Bersanetti, D; Bertolini, A; Betzwieser, J; Bilenko, I A; Billingsley, G; Birch, J; Biscans, S; Bitossi, M; Biwer, C; Bizouard, M A; Black, E; Blackburn, J K; Blackburn, L; Blair, D; Bloemen, S; Bock, O; Bodiya, T P; Boer, M; Bogaert, G; Bogan, C; Bond, C; Bondu, F; Bonelli, L; Bonnand, R; Bork, R; Born, M; Boschi, V; Bose, Sukanta; Bosi, L; Bradaschia, C; Brady, P R; Braginsky, V B; Branchesi, M; Brau, J E; Briant, T; Bridges, D O; Brillet, A; Brinkmann, M; Brisson, V; Brooks, A F; Brown, D A; Brown, D D; Brückner, F; Buchman, S; Buikema, A; Bulik, T; Bulten, H J; Buonanno, A; Burman, R; Buskulic, D; Buy, C; Cadonati, L; Cagnoli, G; Calderón Bustillo, J; Calloni, E; Camp, J B; Campsie, P; Cannon, K C; Canuel, B; Cao, J; Capano, C D; Carbognani, F; Carbone, L; Caride, S; Castaldi, G; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Celerier, C; Cella, G; Cepeda, C; Cesarini, E; Chakraborty, R; Chalermsongsak, T; Chamberlin, S J; Chao, S; Charlton, P; Chassande-Mottin, E; Chen, X; Chen, Y; Chincarini, A; Chiummo, A; Cho, H S; Cho, M; Chow, J H; Christensen, N; Chu, Q; Chua, S S Y; Chung, S; Ciani, G; Clara, F; Clark, D E; Clark, J A; Clayton, J H; Cleva, F; Coccia, E; Cohadon, P-F; Colla, A; Collette, C; Colombini, M; Cominsky, L; Constancio, M; Conte, A; Cook, D; Corbitt, T R; Cornish, N; Corsi, A; Costa, C A; Coughlin, M W; Coulon, J-P; Countryman, S; Couvares, P; Coward, D M; Cowart, M J; Coyne, D C; Coyne, R; Craig, K; Creighton, J D E; Croce, R P; Crowder, S G; Cumming, A; Cunningham, L; Cuoco, E; Cutler, C; Dahl, K; Dal Canton, T; Damjanic, M; Danilishin, S L; D'Antonio, S; Danzmann, K; Dattilo, V; Daveloza, H; Davier, M; Davies, G S; Daw, E J; Day, R; Dayanga, T; DeBra, D; Debreczeni, G; Degallaix, J; Deléglise, S; Del Pozzo, W; Denker, T; Dent, T; Dereli, H; Dergachev, V; De Rosa, R; DeRosa, R T; DeSalvo, R; Dhurandhar, S; Díaz, M; Dickson, J; Di Fiore, L; Di Lieto, A; Di Palma, I; Di Virgilio, A; Dolique, V; Dominguez, E; Donovan, F; Dooley, K L; Doravari, S; Douglas, R; Downes, T P; Drago, M; Drever, R W P; Driggers, J C; Du, Z; Ducrot, M; Dwyer, S; Eberle, T; Edo, T; Edwards, M; Effler, A; Eggenstein, H-B; Ehrens, P; Eichholz, J; Eikenberry, S S; Endrőczi, G; Essick, R; Etzel, T; Evans, M; Evans, T; Factourovich, M; Fafone, V; Fairhurst, S; Fan, X; Fang, Q; Farinon, S; Farr, B; Farr, W M; Favata, M; Fazi, D; Fehrmann, H; Fejer, M M; Feldbaum, D; Feroz, F; Ferrante, I; Ferreira, E C; Ferrini, F; Fidecaro, F; Finn, L S; Fiori, I; Fisher, R P; Flaminio, R; Fournier, J-D; Franco, S; Frasca, S; Frasconi, F; Frede, M; Frei, Z; Freise, A; Frey, R; Fricke, T T; Fritschel, P; Frolov, V V; Fulda, P; Fyffe, M; Gair, J R; Gammaitoni, L; Gaonkar, S; Garufi, F; Gehrels, N; Gemme, G; Gendre, B; Genin, E; Gennai, A; Ghosh, S; Giaime, J A; Giardina, K D; Giazotto, A; Gleason, J; Goetz, E; Goetz, R; Gondan, L; González, G; Gordon, N; Gorodetsky, M L; Gossan, S; Goßler, S; Gouaty, R; Gräf, C; Graff, P B; Granata, M; Grant, A; Gras, S; Gray, C; Greenhalgh, R J S; Gretarsson, A M; Groot, P; Grote, H; Grover, K; Grunewald, S; Guidi, G M; Guido, C J; Gushwa, K; Gustafson, E K; Gustafson, R; Ha, J; Hall, E D; Hamilton, W; Hammer, D; Hammond, G; Hanke, M; Hanks, J; Hanna, C; Hannam, M D; Hanson, J; Harms, J; Harry, G M; Harry, I W; Harstad, E D; Hart, M; Hartman, M T; Haster, C-J; Haughian, K; Heidmann, A; Heintze, M; Heitmann, H; Hello, P; Hemming, G; Hendry, M; Heng, I S; Heptonstall, A W; Heurs, M; Hewitson, M; Hild, S; Hoak, D; Hodge, K A; Hofman, D; Holt, K; Hopkins, P; Horrom, T; Hoske, D; Hosken, D J; Hough, J; Howell, E J; Hu, Y; Huerta, E; Hughey, B; Husa, S; Huttner, S H; Huynh, M; Huynh-Dinh, T; Idrisy, A; Ingram, D R; Inta, R; Islas, G; Isogai, T; Ivanov, A; Iyer, B R; Izumi, K; Jacobson, M; Jang, H; Jaranowski, P; Ji, Y; Jiménez-Forteza, F; Johnson, W W; Jones, D I; Jones, R; Jonker, R J G; Ju, L; Haris, K; Kalmus, P; Kalogera, V; Kandhasamy, S; Kang, G; Kanner, J B; Karlen, J; Kasprzack, M; Katsavounidis, E; Katzman, W; Kaufer, H; Kaufer, S; Kaur, T; Kawabe, K; Kawazoe, F; Kéfélian, F; Keiser, G M; Keitel, D; Kelley, D B; Kells, W; Keppel, D G
2014-07-04
We present the results of a search for gravitational waves associated with 223 γ-ray bursts (GRBs) detected by the InterPlanetary Network (IPN) in 2005-2010 during LIGO's fifth and sixth science runs and Virgo's first, second, and third science runs. The IPN satellites provide accurate times of the bursts and sky localizations that vary significantly from degree scale to hundreds of square degrees. We search for both a well-modeled binary coalescence signal, the favored progenitor model for short GRBs, and for generic, unmodeled gravitational wave bursts. Both searches use the event time and sky localization to improve the gravitational wave search sensitivity as compared to corresponding all-time, all-sky searches. We find no evidence of a gravitational wave signal associated with any of the IPN GRBs in the sample, nor do we find evidence for a population of weak gravitational wave signals associated with the GRBs. For all IPN-detected GRBs, for which a sufficient duration of quality gravitational wave data are available, we place lower bounds on the distance to the source in accordance with an optimistic assumption of gravitational wave emission energy of 10(-2)M⊙c(2) at 150 Hz, and find a median of 13 Mpc. For the 27 short-hard GRBs we place 90% confidence exclusion distances to two source models: a binary neutron star coalescence, with a median distance of 12 Mpc, or the coalescence of a neutron star and black hole, with a median distance of 22 Mpc. Finally, we combine this search with previously published results to provide a population statement for GRB searches in first-generation LIGO and Virgo gravitational wave detectors and a resulting examination of prospects for the advanced gravitational wave detectors.
Aasi, J.; Abbott, B. P.; Abbott, R.; Abbott, T.; Abernathy, M. R.; Acernese, F.; Blackbum, L.; Camp, J. B.; Gehrels, N.; Graff, P. B.;
2014-01-01
We present the results of a search for gravitational waves associated with 223 gamma ray bursts (GRBs) detected by the InterPlanetary Network (IPN) in 2005-2010 during LIGO's fifth and sixth science runs and Virgo's first, second, and third science runs. The IPN satellites provide accurate times of the bursts and sky localizations that vary significantly from degree scale to hundreds of square degrees. We search for both a well-modeled binary coalescence signal, the favored progenitor model for short GRBs, and for generic, unmodeled gravitational wave bursts. Both searches use the event time and sky localization to improve the gravitational wave search sensitivity as compared to corresponding all-time, all-sky searches. We find no evidence of a gravitational wave signal associated with any of the IPN GRBs in the sample, nor do we find evidence for a population of weak gravitational wave signals associated with the GRBs. For all IPN-detected GRBs, for which a sufficient duration of quality gravitational wave data are available, we place lower bounds on the distance to the source in accordance with an optimistic assumption of gravitational wave emission energy of 10(exp-2) solar mass c(exp 2) at 150 Hz, and find a median of 13 Mpc. For the 27 short-hard GRBs we place 90% confidence exclusion distances to two source models: a binary neutron star coalescence, with a median distance of 12 Mpc, or the coalescence of a neutron star and black hole, with a median distance of 22 Mpc. Finally, we combine this search with previously published results to provide a population statement for GRB searches in first-generation LIGO and Virgo gravitational wave detectors and a resulting examination of prospects for the advanced gravitational wave detectors.
Never Use the Complete Search Space: a Concept to Enhance the Optimization Procedure for Monitoring Networks
Bode, F.; Reuschen, S.; Nowak, W.
2015-12-01
Drinking-water well catchments include many potential sources of contaminations like gas stations or agriculture. Finding optimal positions of early-warning monitoring wells is challenging because there are various parameters (and their uncertainties) that influence the reliability and optimality of any suggested monitoring location or monitoring network.The overall goal of this project is to develop and establish a concept to assess, design and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: a high detection probability, which can be reached by maximizing the "field of vision" of the monitoring network, a long early-warning time such that there is enough time left to install counter measures after first detection, and the overall operating costs of the monitoring network, which should ideally be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, scenario analyses for real data, respectively, wrapped up within the framework of formal multi-objective optimization using a genetic algorithm.In order to speed up the optimization process and to better explore the Pareto-front, we developed a concept that forces the algorithm to search only in regions of the search space where promising solutions can be expected. We are going to show how to define these regions beforehand, using knowledge of the optimization problem, but also how to define them independently of problem attributes. With that, our method can be used with and/or without detailed knowledge of the objective functions.In summary, our study helps to improve optimization results in less optimization time by meaningful restrictions of the search space. These restrictions can be done independently of the optimization problem, but also in a problem-specific manner.
System network planning expansion using mathematical programming, genetic algorithms and tabu search
Energy Technology Data Exchange (ETDEWEB)
Sadegheih, A. [Department of Industrial Engineering, University of Yazd, P.O. Box 89195-741, Yazd (Iran); Drake, P.R. [E-Business and Operations Management Division, University of Liverpool Management School, University of Liverpool, Liverpool (United Kingdom)
2008-06-15
In this paper, system network planning expansion is formulated for mixed integer programming, a genetic algorithm (GA) and tabu search (TS). Compared with other optimization methods, GAs are suitable for traversing large search spaces, since they can do this relatively rapidly and because the use of mutation diverts the method away from local minima, which will tend to become more common as the search space increases in size. GA's give an excellent trade off between solution quality and computing time and flexibility for taking into account specific constraints in real situations. TS has emerged as a new, highly efficient, search paradigm for finding quality solutions to combinatorial problems. It is characterized by gathering knowledge during the search and subsequently profiting from this knowledge. The attractiveness of the technique comes from its ability to escape local optimality. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. The DC load flow equations for the network are embedded in the constraints of the mathematical model to avoid sub-optimal solutions that can arise if the enforcement of such constraints is done in an indirect way. The solution of the model gives the best line additions and also provides information regarding the optimal generation at each generation point. This method of solution is demonstrated on the expansion of a 10 bus bar system to 18 bus bars. Finally, a steady-state genetic algorithm is employed rather than generational replacement, also uniform crossover is used. (author)
Zainuddin, Zarita; Lai, Kee Huong; Ong, Pauline
2013-04-01
Artificial neural networks (ANNs) are powerful mathematical models that are used to solve complex real world problems. Wavelet neural networks (WNNs), which were developed based on the wavelet theory, are a variant of ANNs. During the training phase of WNNs, several parameters need to be initialized; including the type of wavelet activation functions, translation vectors, and dilation parameter. The conventional k-means and fuzzy c-means clustering algorithms have been used to select the translation vectors. However, the solution vectors might get trapped at local minima. In this regard, the evolutionary harmony search algorithm, which is capable of searching for near-optimum solution vectors, both locally and globally, is introduced to circumvent this problem. In this paper, the conventional k-means and fuzzy c-means clustering algorithms were hybridized with the metaheuristic harmony search algorithm. In addition to obtaining the estimation of the global minima accurately, these hybridized algorithms also offer more than one solution to a particular problem, since many possible solution vectors can be generated and stored in the harmony memory. To validate the robustness of the proposed WNNs, the real world problem of epileptic seizure detection was presented. The overall classification accuracy from the simulation showed that the hybridized metaheuristic algorithms outperformed the standard k-means and fuzzy c-means clustering algorithms.
Précis of Simple heuristics that make us smart.
Todd, P M; Gigerenzer, G
2000-10-01
How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), we explore fast and frugal heuristics--simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data--that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program.
Heuristic errors in clinical reasoning.
Rylander, Melanie; Guerrasio, Jeannette
2016-08-01
Errors in clinical reasoning contribute to patient morbidity and mortality. The purpose of this study was to determine the types of heuristic errors made by third-year medical students and first-year residents. This study surveyed approximately 150 clinical educators inquiring about the types of heuristic errors they observed in third-year medical students and first-year residents. Anchoring and premature closure were the two most common errors observed amongst third-year medical students and first-year residents. There was no difference in the types of errors observed in the two groups. Errors in clinical reasoning contribute to patient morbidity and mortality Clinical educators perceived that both third-year medical students and first-year residents committed similar heuristic errors, implying that additional medical knowledge and clinical experience do not affect the types of heuristic errors made. Further work is needed to help identify methods that can be used to reduce heuristic errors early in a clinician's education. © 2015 John Wiley & Sons Ltd.
Study of heuristics in ant system for nuclear reload optimisation
Energy Technology Data Exchange (ETDEWEB)
Lima, Alan M.M. de; Schirru, Roberto; Silva, Fernando C. da; Machado, Marcelo D.; Medeiros, Jose A.C.C. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear]. E-mail: alan@lmp.ufrj.br; schirru@lmp.ufrj.br; fernando@con.ufrj.br; marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br
2007-07-01
A Pressurized Water Reactor core must be reloaded every time the fuel burnup reaches a level when it is not possible to sustain nominal power operation. The nuclear core fuel reload optimization consists in finding a burned-up and fresh-fuel-assembly loading pattern that maximizes the number of effective full power days, minimizing the relationship cost/benefit. This problem is NP-hard, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Besides that, the problem is non-linear and its search space is highly discontinual and multimodal. In this work a parallel computational system based on Ant Colony System (ACS) called Artificial-Ant-Colony Networks is used to solve the nuclear reactor core fuel reload optimization problem, with compatibles heuristics. ACS is a system based on artificial agents that uses the reinforcement learning technique and was originally developed to solve the Traveling Salesman Problem, which is conceptually similar to the nuclear fuel reload problem. (author)
Cañon, Daniel E; Lopez, Diego M; Blobel, Bernd
2014-01-01
Moderation of content in online Health Social Networks (HSN) is critical because information is not only published and produced by experts or health professionals, but also by users of that information. The objective of this paper is to propose a semi-automatic moderation Web Service for assessing the quality (trustworthiness) of health-related videos published on the YouTube social network. The service is relevant for moderators or community managers, who get enabled to control the quality of videos published on their online HSN sites. The HealthTrust metric was selected as the metric to be implemented in the service in order to support the assessment of trustworthiness of videos in Online HSN. The service is a RESTful service which can be integrated into open source Virtual Social Network Platforms, therefore improving trust in the process of searching and publishing content extracted from YouTube. A preliminary pilot evaluation in a simple use case demonstrated that the relevance of videos retrieved using the moderation service was higher compared to the relevance of the videos retrieved using the YouTube search engine.
Mamano, Nil; Hayes, Wayne B
2017-07-15
Every alignment algorithm consists of two orthogonal components: an objective function M measuring the quality of an alignment, and a search algorithm that explores the space of alignments looking for ones scoring well according to M . We introduce a new search algorithm called SANA (Simulated Annealing Network Aligner) and apply it to protein-protein interaction networks using S 3 as the topological measure. Compared against 12 recent algorithms, SANA produces 5-10 times as many correct node pairings as the others when the correct answer is known. We expose an anti-correlation in many existing aligners between their ability to produce good topological vs. functional similarity scores, whereas SANA usually outscores other methods in both measures. If given the perfect objective function encoding the identity mapping, SANA quickly converges to the perfect solution while many other algorithms falter. We observe that when aligning networks with a known mapping and optimizing only S 3 , SANA creates alignments that are not perfect and yet whose S 3 scores match that of the perfect alignment. We call this phenomenon saturation of the topological score . Saturation implies that a measure's correlation with alignment correctness falters before the perfect alignment is reached. This, combined with SANA's ability to produce the perfect alignment if given the perfect objective function, suggests that better objective functions may lead to dramatically better alignments. We conclude that future work should focus on finding better objective functions, and offer SANA as the search algorithm of choice. Software available at http://sana.ics.uci.edu . whayes@uci.edu. Supplementary data are available at Bioinformatics online.
Collective intelligence, social networks and social capital: searching for conceptual connections
Directory of Open Access Journals (Sweden)
Julia Bellia Margoto
2015-04-01
Full Text Available It is a theoretical essay whose goal was the search for conceptual similarities between the phenomena of collective intelligence, social networking and social capital within the context of the information society. Thereby, those elements are presented and discussed considering different authors. It appears that the concepts covered have numerous points of convergence and strong relations of proximity and complementarity. The conclusions point to the idea that the consideration of these relationships in the theoretical perspectives adopted in the research developed in the fields under consideration, could significantly enrich the results to be achieved.
Searching for axion stars and Q-balls with a terrestrial magnetometer network
Energy Technology Data Exchange (ETDEWEB)
Jackson Kimball, D. F. [Cal State, East Bay; Budker, D. [UC, Berkeley; Eby, J. [Fermilab; Pospelov, M. [Perimeter Inst. Theor. Phys.; Pustelny, S. [Jagiellonian U.; Scholtes, T. [Fribourg U.; Stadnik, Y. V. [Helmholtz Inst., Mainz; Weis, A. [Fribourg U.; Wickenbrock, A. [Mainz U.
2017-10-11
Light (pseudo-)scalar fields are promising candidates to be the dark matter in the Universe. Under certain initial conditions in the early Universe and/or with certain types of self-interactions, they can form compact dark-matter objects such as axion stars or Q-balls. Direct encounters with such objects can be searched for by using a global network of atomic magnetometers. It is shown that for a range of masses and radii not ruled out by existing observations, the terrestrial encounter rate with axion stars or Q-balls can be sufficiently high (at least once per year) for a detection. Furthermore, it is shown that a global network of atomic magnetometers is sufficiently sensitive to pseudoscalar couplings to atomic spins so that a transit through an axion star or Q-ball could be detected over a broad range of unexplored parameter space.
Search for large-scale coincidences in network observation of cosmic ray air showers
Ochi, N; Kimura, H; Konishi, T; Nakamura, T; Nakatsuka, T; Ohara, S; Ohmori, N; Okei, K; Saitoh, K; Takahashi, N; Tsuji, S; Wada, T; Yamamoto, I; Yamashita, Y; Yanagimoto, Y
2003-01-01
The Large Area Air Shower (LAAS) group has been performing a network observation of extensive air showers (EAS) since 1996 in Japan. Eight compact EAS arrays (ten in the near future) are operating simultaneously and independently at distant stations (up to approx 1000 km), constituting a gigantic detector system as a whole. Using five stations' datasets, large-scale coincidences of EAS have been searched for with the aim of detecting signals from extremely short bursts in the universe. By comparing arrival times and arrival directions of all registered EAS, three coincident and parallel EAS pairs were extracted out of a sea of background cosmic rays. One of them was observed almost from the direction of the Crab Nebula, a previously reported ultra-high-energy gamma-ray source. The first application reported here allows the analysis techniques to be tested and demonstrates the potential of observations with the full operation of the network detector system.
Sabar, Nasser R; Ayob, Masri; Kendall, Graham; Qu, Rong
2015-02-01
Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite.
Xu, Zhezhuang; Liu, Guanglun; Yan, Haotian; Cheng, Bin; Lin, Feilong
2017-01-01
In wireless sensor and actor networks, when an event is detected, the sensor node needs to transmit an event report to inform the actor. Since the actor moves in the network to execute missions, its location is always unavailable to the sensor nodes. A popular solution is the search strategy that can forward the data to a node without its location information. However, most existing works have not considered the mobility of the node, and thus generate significant energy consumption or transmission delay. In this paper, we propose the trail-based search (TS) strategy that takes advantage of actor’s mobility to improve the search efficiency. The main idea of TS is that, when the actor moves in the network, it can leave its trail composed of continuous footprints. The search packet with the event report is transmitted in the network to search the actor or its footprints. Once an effective footprint is discovered, the packet will be forwarded along the trail until it is received by the actor. Moreover, we derive the condition to guarantee the trail connectivity, and propose the redundancy reduction scheme based on TS (TS-R) to reduce nontrivial transmission redundancy that is generated by the trail. The theoretical and numerical analysis is provided to prove the efficiency of TS. Compared with the well-known expanding ring search (ERS), TS significantly reduces the energy consumption and search delay. PMID:29077017
Xu, Zhezhuang; Liu, Guanglun; Yan, Haotian; Cheng, Bin; Lin, Feilong
2017-10-27
In wireless sensor and actor networks, when an event is detected, the sensor node needs to transmit an event report to inform the actor. Since the actor moves in the network to execute missions, its location is always unavailable to the sensor nodes. A popular solution is the search strategy that can forward the data to a node without its location information. However, most existing works have not considered the mobility of the node, and thus generate significant energy consumption or transmission delay. In this paper, we propose the trail-based search (TS) strategy that takes advantage of actor's mobility to improve the search efficiency. The main idea of TS is that, when the actor moves in the network, it can leave its trail composed of continuous footprints. The search packet with the event report is transmitted in the network to search the actor or its footprints. Once an effective footprint is discovered, the packet will be forwarded along the trail until it is received by the actor. Moreover, we derive the condition to guarantee the trail connectivity, and propose the redundancy reduction scheme based on TS (TS-R) to reduce nontrivial transmission redundancy that is generated by the trail. The theoretical and numerical analysis is provided to prove the efficiency of TS. Compared with the well-known expanding ring search (ERS), TS significantly reduces the energy consumption and search delay.
Role of adaptive heuristic criticism in cascade temperature control of an industrial tubular furnace
Energy Technology Data Exchange (ETDEWEB)
Zeybek, Zehra [Ankara University, Department of Chemical Engineering, Tandogan 06100, Ankara (Turkey)
2006-02-01
The purpose of this research is to improve and apply the multivariable control structure of an industrial furnace on the basis of the adaptive heuristic criticism (AHC). This algorithm is a three-layer feed-forward artificial neural network (ANN) that uses supervised learning with reinforcement in a unique topology. It shows how a system consisting of two neurone-like adaptive elements can solve a difficult learning control problem, i.e. the learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). The task is to balance a pole that hinges on the manipulated variable by applying disturbance forces to the furnace. This approach to solve control problems of furnaces using AHC is discussed and compared with the results from the fuzzy temperature control of the system in this work. (author)
Clustering high throughput biological data with B-MST, a minimum spanning tree based heuristic.
Pirim, Harun; Ekşioğlu, Burak; Perkins, Andy D
2015-07-01
To address important challenges in bioinformatics, high throughput data technologies are needed to interpret biological data efficiently and reliably. Clustering is widely used as a first step to interpreting high dimensional biological data, such as the gene expression data measured by microarrays. A good clustering algorithm should be efficient, reliable, and effective, as demonstrated by its capability of determining biologically relevant clusters. This paper proposes a new minimum spanning tree based heuristic B-MST, that is guided by an innovative objective function: the tightness and separation index (TSI). The TSI presented here obtains biologically meaningful clusters, making use of co-expression network topology, and this paper develops a local search procedure to minimize the TSI value. The proposed B-MST is tested by comparing results to: (1) adjusted rand index (ARI), for microarray data sets with known object classes, and (2) gene ontology (GO) annotations for data sets without documented object classes. Copyright © 2015 Elsevier Ltd. All rights reserved.
Alizadeh Afrouzy, Zahra; Paydar, Mohammad Mahdi; Nasseri, Seyed Hadi; Mahdavi, Iraj
2017-06-01
There are many reasons for the growing interest in developing new product projects for any firm. The most embossed reason is surviving in a highly competitive industry which the customer tastes are changing rapidly. A well-managed supply chain network can provide the most profit for firms due to considering new product development. Along with profit, customer satisfaction and production of new products are goals which lead to a more efficient supply chain. As new products appear in the market, the old products could become obsolete, and then phased out. The most important parameter in a supply chain which considers new and developed products is the time that developed and new products are introduced and old products are phased out. With consideration of the factors noted above, this study proposes to design a tri-objective multi-echelon multi-product multi-period supply chain model, which incorporates product development and new product production and their effects on supply chain configuration. The supply chain under consideration is assumed to consist of suppliers, manufacturers, distributors and customer groups. In terms of overcoming NP-hardness of the proposed model and in order to solve the complicated problem, a non-dominated sorting genetic algorithm is employed. As there is no benchmark available in the literature, the non-dominated ranking genetic algorithm is developed to validate the results obtained and some test problems are provided to show the applicability of the proposed methodology and evaluate the performance of the algorithms.
Fazayeli, Saeed; Eydi, Alireza; Kamalabadi, Isa Nakhai
2017-07-01
Nowadays, organizations have to compete with different competitors in regional, national and international levels, so they have to improve their competition capabilities to survive against competitors. Undertaking activities on a global scale requires a proper distribution system which could take advantages of different transportation modes. Accordingly, the present paper addresses a location-routing problem on multimodal transportation network. The introduced problem follows four objectives simultaneously which form main contribution of the paper; determining multimodal routes between supplier and distribution centers, locating mode changing facilities, locating distribution centers, and determining product delivery tours from the distribution centers to retailers. An integer linear programming is presented for the problem, and a genetic algorithm with a new chromosome structure proposed to solve the problem. Proposed chromosome structure consists of two different parts for multimodal transportation and location-routing parts of the model. Based on published data in the literature, two numerical cases with different sizes generated and solved. Also, different cost scenarios designed to better analyze model and algorithm performance. Results show that algorithm can effectively solve large-size problems within a reasonable time which GAMS software failed to reach an optimal solution even within much longer times.
Fast or frugal, but not both: Decision heuristics under time pressure.
Bobadilla-Suarez, Sebastian; Love, Bradley C
2018-01-01
Heuristics are simple, yet effective, strategies that people use to make decisions. Because heuristics do not require all available information, they are thought to be easy to implement and to not tax limited cognitive resources, which has led heuristics to be characterized as fast-and-frugal. We question this monolithic conception of heuristics by contrasting the cognitive demands of two popular heuristics, Tallying and Take-the-Best. We contend that heuristics that are frugal in terms of information usage may not always be fast because of the attentional control required to implement this focus in certain contexts. In support of this hypothesis, we find that Take-the-Best, while being more frugal in terms of information usage, is slower to implement and fares worse under time pressure manipulations than Tallying. This effect is then reversed when search costs for Take-the-Best are reduced by changing the format of the stimuli. These findings suggest that heuristics are heterogeneous and should be unpacked according to their cognitive demands to determine the circumstances a heuristic best applies. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
An Effective Cuckoo Search Algorithm for Node Localization in Wireless Sensor Network.
Cheng, Jing; Xia, Linyuan
2016-08-31
Localization is an essential requirement in the increasing prevalence of wireless sensor network (WSN) applications. Reducing the computational complexity, communication overhead in WSN localization is of paramount importance in order to prolong the lifetime of the energy-limited sensor nodes and improve localization performance. This paper proposes an effective Cuckoo Search (CS) algorithm for node localization. Based on the modification of step size, this approach enables the population to approach global optimal solution rapidly, and the fitness of each solution is employed to build mutation probability for avoiding local convergence. Further, the approach restricts the population in the certain range so that it can prevent the energy consumption caused by insignificant search. Extensive experiments were conducted to study the effects of parameters like anchor density, node density and communication range on the proposed algorithm with respect to average localization error and localization success ratio. In addition, a comparative study was conducted to realize the same localization task using the same network deployment. Experimental results prove that the proposed CS algorithm can not only increase convergence rate but also reduce average localization error compared with standard CS algorithm and Particle Swarm Optimization (PSO) algorithm.
Conflict and Bias in Heuristic Judgment
Bhatia, Sudeep
2017-01-01
Conflict has been hypothesized to play a key role in recruiting deliberative processing in reasoning and judgment tasks. This claim suggests that changing the task so as to add incorrect heuristic responses that conflict with existing heuristic responses can make individuals less likely to respond heuristically and can increase response accuracy.…
An Effective Exercise for Teaching Cognitive Heuristics
Swinkels, Alan
2003-01-01
This article describes a brief heuristics demonstration and offers suggestions for personalizing examples of heuristics by making them relevant to students. Students complete a handout asking for 4 judgments illustrative of such heuristics. The decisions are cast in the context of students' daily lives at their particular university. After the…
Heuristic Biases in Mathematical Reasoning
Inglis, Matthew; Simpson, Adrian
2005-01-01
In this paper we briefly describe the dual process account of reasoning, and explain the role of heuristic biases in human thought. Concentrating on the so-called matching bias effect, we describe a piece of research that indicates a correlation between success at advanced level mathematics and an ability to override innate and misleading…
Stochastic margin-based structure learning of Bayesian network classifiers.
Pernkopf, Franz; Wohlmayr, Michael
2013-02-01
The margin criterion for parameter learning in graphical models gained significant impact over the last years. We use the maximum margin score for discriminatively optimizing the structure of Bayesian network classifiers. Furthermore, greedy hill-climbing and simulated annealing search heuristics are applied to determine the classifier structures. In the experiments, we demonstrate the advantages of maximum margin optimized Bayesian network structures in terms of classification performance compared to traditionally used discriminative structure learning methods. Stochastic simulated annealing requires less score evaluations than greedy heuristics. Additionally, we compare generative and discriminative parameter learning on both generatively and discriminatively structured Bayesian network classifiers. Margin-optimized Bayesian network classifiers achieve similar classification performance as support vector machines. Moreover, missing feature values during classification can be handled by discriminatively optimized Bayesian network classifiers, a case where purely discriminative classifiers usually require mechanisms to complete unknown feature values in the data first.
A novel heuristic algorithm for capacitated vehicle routing problem
Kır, Sena; Yazgan, Harun Reşit; Tüncel, Emre
2017-02-01
The vehicle routing problem with the capacity constraints was considered in this paper. It is quite difficult to achieve an optimal solution with traditional optimization methods by reason of the high computational complexity for large-scale problems. Consequently, new heuristic or metaheuristic approaches have been developed to solve this problem. In this paper, we constructed a new heuristic algorithm based on the tabu search and adaptive large neighborhood search (ALNS) with several specifically designed operators and features to solve the capacitated vehicle routing problem (CVRP). The effectiveness of the proposed algorithm was illustrated on the benchmark problems. The algorithm provides a better performance on large-scaled instances and gained advantage in terms of CPU time. In addition, we solved a real-life CVRP using the proposed algorithm and found the encouraging results by comparison with the current situation that the company is in.
A heuristic approach to incremental and reactive scheduling
Odubiyi, Jide B.; Zoch, David R.
1989-01-01
An heuristic approach to incremental and reactive scheduling is described. Incremental scheduling is the process of modifying an existing schedule if the initial schedule does not meet its stated initial goals. Reactive scheduling occurs in near real-time in response to changes in available resources or the occurrence of targets of opportunity. Only minor changes are made during both incremental and reactive scheduling because a goal of re-scheduling procedures is to minimally impact the schedule. The described heuristic search techniques, which are employed by the Request Oriented Scheduling Engine (ROSE), a prototype generic scheduler, efficiently approximate the cost of reaching a goal from a given state and effective mechanisms for controlling search.
Comparison of Heuristics for Generating All-partition Arrays in the Style of Milton Babbitt
DEFF Research Database (Denmark)
Bemman, Brian; Meredith, David
2015-01-01
aggregate or an incomplete one that can be made complete by adding OARPs. It is noteworthy that, when constructing an all-partition array, Babbitt started out with a non-self-contained sequence of partitions. In this paper, we use a known self-contained sequence as a basis for forming two heuristics...... that select integer partitions likely to have been chosen by Babbitt. We suggest these heuristics will select integer partitions more likely to produce a self-contained sequence and we present it as a means for efficiently searching the space of possible sequences. We apply our heuristics to both types...
CompositeSearch: A Generalized Network Approach for Composite Gene Families Detection.
Pathmanathan, Jananan Sylvestre; Lopez, Philippe; Lapointe, François-Joseph; Bapteste, Eric
2018-01-01
Genes evolve by point mutations, but also by shuffling, fusion, and fission of genetic fragments. Therefore, similarity between two sequences can be due to common ancestry producing homology, and/or partial sharing of component fragments. Disentangling these processes is especially challenging in large molecular data sets, because of computational time. In this article, we present CompositeSearch, a memory-efficient, fast, and scalable method to detect composite gene families in large data sets (typically in the range of several million sequences). CompositeSearch generalizes the use of similarity networks to detect composite and component gene families with a greater recall, accuracy, and precision than recent programs (FusedTriplets and MosaicFinder). Moreover, CompositeSearch provides user-friendly quality descriptions regarding the distribution and primary sequence conservation of these gene families allowing critical biological analyses of these data. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Meghabghab, George
2001-01-01
Discusses the evaluation of search engines and uses neural networks in stochastic simulation of the number of rejected Web pages per search query. Topics include the iterative radial basis functions (RBF) neural network; precision; response time; coverage; Boolean logic; regression models; crawling algorithms; and implications for search engine…
Hermawati, Setia; Lawson, Glyn
2016-01-01
Heuristics evaluation is frequently employed to evaluate usability. While general heuristics are suitable to evaluate most user interfaces, there is still a need to establish heuristics for specific domains to ensure that their specific usability issues are identified. This paper presents a comprehensive review of 70 studies related to usability heuristics for specific domains. The aim of this paper is to review the processes that were applied to establish heuristics in specific domains and i...
Heuristics for NP-hard optimization problems - simpler is better!?
Directory of Open Access Journals (Sweden)
Žerovnik Janez
2015-11-01
Full Text Available We provide several examples showing that local search, the most basic metaheuristics, may be a very competitive choice for solving computationally hard optimization problems. In addition, generation of starting solutions by greedy heuristics should be at least considered as one of very natural possibilities. In this critical survey, selected examples discussed include the traveling salesman, the resource-constrained project scheduling, the channel assignment, and computation of bounds for the Shannon capacity.
New heuristics for traveling salesman and vehicle routing problems with time windows
Energy Technology Data Exchange (ETDEWEB)
Gendreau, M.; Hertz, A.; Laporte, G.; Mihnea, S.
1994-12-31
We consider variants of the Traveling Salesman (TSP) and Vehicle Routing (VRP) Problems in which each customer can only be visited within a pre-specified (hard) time-window. We first present a two-phase (construction and post-optimization) generalized insertion heuristic for the TSPTW. This insertion heuristic is then imbedded in a tabu search metaheuristic in order to solve the VRPTW. Computational results on standard benchmark problems will be reported.
Lynnes, Chris
2014-01-01
Three current search engines are queried for ozone data at the GES DISC. The results range from sub-optimal to counter-intuitive. We propose a method to fix dataset search by implementing a robust relevancy ranking scheme. The relevancy ranking scheme is based on several heuristics culled from more than 20 years of helping users select datasets.
Tengku Hashim, Tengku Juhana; Mohamed, Azah
2017-01-01
The growing interest in distributed generation (DG) in recent years has led to a number of generators connected to a distribution system. The integration of DGs in a distribution system has resulted in a network known as active distribution network due to the existence of bidirectional power flow in the system. Voltage rise issue is one of the predominantly important technical issues to be addressed when DGs exist in an active distribution network. This paper presents the application of the backtracking search algorithm (BSA), which is relatively new optimisation technique to determine the optimal settings of coordinated voltage control in a distribution system. The coordinated voltage control considers power factor, on-load tap-changer and generation curtailment control to manage voltage rise issue. A multi-objective function is formulated to minimise total losses and voltage deviation in a distribution system. The proposed BSA is compared with that of particle swarm optimisation (PSO) so as to evaluate its effectiveness in determining the optimal settings of power factor, tap-changer and percentage active power generation to be curtailed. The load flow algorithm from MATPOWER is integrated in the MATLAB environment to solve the multi-objective optimisation problem. Both the BSA and PSO optimisation techniques have been tested on a radial 13-bus distribution system and the results show that the BSA performs better than PSO by providing better fitness value and convergence rate.
Drake, John H; Özcan, Ender; Burke, Edmund K
2016-01-01
Hyper-heuristics are high-level methodologies for solving complex problems that operate on a search space of heuristics. In a selection hyper-heuristic framework, a heuristic is chosen from an existing set of low-level heuristics and applied to the current solution to produce a new solution at each point in the search. The use of crossover low-level heuristics is possible in an increasing number of general-purpose hyper-heuristic tools such as HyFlex and Hyperion. However, little work has been undertaken to assess how best to utilise it. Since a single-point search hyper-heuristic operates on a single candidate solution, and two candidate solutions are required for crossover, a mechanism is required to control the choice of the other solution. The frameworks we propose maintain a list of potential solutions for use in crossover. We investigate the use of such lists at two conceptual levels. First, crossover is controlled at the hyper-heuristic level where no problem-specific information is required. Second, it is controlled at the problem domain level where problem-specific information is used to produce good-quality solutions to use in crossover. A number of selection hyper-heuristics are compared using these frameworks over three benchmark libraries with varying properties for an NP-hard optimisation problem: the multidimensional 0-1 knapsack problem. It is shown that allowing crossover to be managed at the domain level outperforms managing crossover at the hyper-heuristic level in this problem domain.
Familiarity and recollection in heuristic decision making.
Schwikert, Shane R; Curran, Tim
2014-12-01
Heuristics involve the ability to utilize memory to make quick judgments by exploiting fundamental cognitive abilities. In the current study we investigated the memory processes that contribute to the recognition heuristic and the fluency heuristic, which are both presumed to capitalize on the byproducts of memory to make quick decisions. In Experiment 1, we used a city-size comparison task while recording event-related potentials (ERPs) to investigate the potential contributions of familiarity and recollection to the 2 heuristics. ERPs were markedly different for recognition heuristic-based decisions and fluency heuristic-based decisions, suggesting a role for familiarity in the recognition heuristic and recollection in the fluency heuristic. In Experiment 2, we coupled the same city-size comparison task with measures of subjective preexperimental memory for each stimulus in the task. Although previous literature suggests the fluency heuristic relies on recognition speed alone, our results suggest differential contributions of recognition speed and recollected knowledge to these decisions, whereas the recognition heuristic relies on familiarity. Based on these results, we created a new theoretical framework that explains decisions attributed to both heuristics based on the underlying memory associated with the choice options. PsycINFO Database Record (c) 2014 APA, all rights reserved.
Heuristics for Routing Heterogeneous Unmanned Vehicles with Fuel Constraints
Directory of Open Access Journals (Sweden)
David Levy
2014-01-01
Full Text Available This paper addresses a multiple depot, multiple unmanned vehicle routing problem with fuel constraints. The objective of the problem is to find a tour for each vehicle such that all the specified targets are visited at least once by some vehicle, the tours satisfy the fuel constraints, and the total travel cost of the vehicles is a minimum. We consider a scenario where the vehicles are allowed to refuel by visiting any of the depots or fuel stations. This is a difficult optimization problem that involves partitioning the targets among the vehicles and finding a feasible tour for each vehicle. The focus of this paper is on developing fast variable neighborhood descent (VND and variable neighborhood search (VNS heuristics for finding good feasible solutions for large instances of the vehicle routing problem. Simulation results are presented to corroborate the performance of the proposed heuristics on a set of 23 large instances obtained from a standard library. These results show that the proposed VND heuristic, on an average, performed better than the proposed VNS heuristic for the tested instances.
How the twain can meet: Prospect theory and models of heuristics in risky choice.
Pachur, Thorsten; Suter, Renata S; Hertwig, Ralph
2017-03-01
Two influential approaches to modeling choice between risky options are algebraic models (which focus on predicting the overt decisions) and models of heuristics (which are also concerned with capturing the underlying cognitive process). Because they rest on fundamentally different assumptions and algorithms, the two approaches are usually treated as antithetical, or even incommensurable. Drawing on cumulative prospect theory (CPT; Tversky & Kahneman, 1992) as the currently most influential instance of a descriptive algebraic model, we demonstrate how the two modeling traditions can be linked. CPT's algebraic functions characterize choices in terms of psychophysical (diminishing sensitivity to probabilities and outcomes) as well as psychological (risk aversion and loss aversion) constructs. Models of heuristics characterize choices as rooted in simple information-processing principles such as lexicographic and limited search. In computer simulations, we estimated CPT's parameters for choices produced by various heuristics. The resulting CPT parameter profiles portray each of the choice-generating heuristics in psychologically meaningful ways-capturing, for instance, differences in how the heuristics process probability information. Furthermore, CPT parameters can reflect a key property of many heuristics, lexicographic search, and track the environment-dependent behavior of heuristics. Finally, we show, both in an empirical and a model recovery study, how CPT parameter profiles can be used to detect the operation of heuristics. We also address the limits of CPT's ability to capture choices produced by heuristics. Our results highlight an untapped potential of CPT as a measurement tool to characterize the information processing underlying risky choice. Copyright © 2017 Elsevier Inc. All rights reserved.
Efficient heuristics for the Rural Postman Problem | Groves | ORiON
African Journals Online (AJOL)
A local search framework for the (undirected) Rural Postman Problem (RPP) is presented in this paper. The framework allows local search approaches that have been applied successfully to the well–known Travelling Salesman Problem also to be applied to the RPP. New heuristics for the RPP, based on this framework, ...
Identifying multiple influential spreaders by a heuristic clustering algorithm
Energy Technology Data Exchange (ETDEWEB)
Bao, Zhong-Kui [School of Mathematical Science, Anhui University, Hefei 230601 (China); Liu, Jian-Guo [Data Science and Cloud Service Research Center, Shanghai University of Finance and Economics, Shanghai, 200133 (China); Zhang, Hai-Feng, E-mail: haifengzhang1978@gmail.com [School of Mathematical Science, Anhui University, Hefei 230601 (China); Department of Communication Engineering, North University of China, Taiyuan, Shan' xi 030051 (China)
2017-03-18
The problem of influence maximization in social networks has attracted much attention. However, traditional centrality indices are suitable for the case where a single spreader is chosen as the spreading source. Many times, spreading process is initiated by simultaneously choosing multiple nodes as the spreading sources. In this situation, choosing the top ranked nodes as multiple spreaders is not an optimal strategy, since the chosen nodes are not sufficiently scattered in networks. Therefore, one ideal situation for multiple spreaders case is that the spreaders themselves are not only influential but also they are dispersively distributed in networks, but it is difficult to meet the two conditions together. In this paper, we propose a heuristic clustering (HC) algorithm based on the similarity index to classify nodes into different clusters, and finally the center nodes in clusters are chosen as the multiple spreaders. HC algorithm not only ensures that the multiple spreaders are dispersively distributed in networks but also avoids the selected nodes to be very “negligible”. Compared with the traditional methods, our experimental results on synthetic and real networks indicate that the performance of HC method on influence maximization is more significant. - Highlights: • A heuristic clustering algorithm is proposed to identify the multiple influential spreaders in complex networks. • The algorithm can not only guarantee the selected spreaders are sufficiently scattered but also avoid to be “insignificant”. • The performance of our algorithm is generally better than other methods, regardless of real networks or synthetic networks.
Special relativity a heuristic approach
Hassani, Sadri
2017-01-01
Special Relativity: A Heuristic Approach provides a qualitative exposition of relativity theory on the basis of the constancy of the speed of light. Using Einstein's signal velocity as the defining idea for the notion of simultaneity and the fact that the speed of light is independent of the motion of its source, chapters delve into a qualitative exposition of the relativity of time and length, discuss the time dilation formula using the standard light clock, explore the Minkowski four-dimensional space-time distance based on how the time dilation formula is derived, and define the components of the two-dimensional space-time velocity, amongst other topics. Provides a heuristic derivation of the Minkowski distance formula Uses relativistic photography to see Lorentz transformation and vector algebra manipulation in action Includes worked examples to elucidate and complement the topic being discussed Written in a very accessible style
Energy Technology Data Exchange (ETDEWEB)
Attisha, Michael J. [Brown U.
2006-01-01
The Cryogenic Dark Matter Search (CDMS) experiment is designed to search for dark matter in the form of Weakly Interacting Massive Particles (WIMPs) via their elastic scattering interactions with nuclei. This dissertation presents the CDMS detector technology and the commissioning of two towers of detectors at the deep underground site in Soudan, Minnesota. CDMS detectors comprise crystals of Ge and Si at temperatures of 20 mK which provide ~keV energy resolution and the ability to perform particle identification on an event by event basis. Event identification is performed via a two-fold interaction signature; an ionization response and an athermal phonon response. Phonons and charged particles result in electron recoils in the crystal, while neutrons and WIMPs result in nuclear recoils. Since the ionization response is quenched by a factor ~ 3(2) in Ge(Si) for nuclear recoils compared to electron recoils, the relative amplitude of the two detector responses allows discrimination between recoil types. The primary source of background events in CDMS arises from electron recoils in the outer 50 µm of the detector surface which have a reduced ionization response. We develop a quantitative model of this ‘dead layer’ effect and successfully apply the model to Monte Carlo simulation of CDMS calibration data. Analysis of data from the two tower run March-August 2004 is performed, resulting in the world’s most sensitive limits on the spin-independent WIMP-nucleon cross-section, with a 90% C.L. upper limit of 1.6 × 10^{-43} cm^{2} on Ge for a 60 GeV WIMP. An approach to performing surface event discrimination using neural networks and wavelets is developed. A Bayesian methodology to classifying surface events using neural networks is found to provide an optimized method based on minimization of the expected dark matter limit. The discrete wavelet analysis of CDMS phonon pulses improves surface event discrimination in conjunction with the neural
Heuristic Synthesis of Reversible Logic – A Comparative Study
Directory of Open Access Journals (Sweden)
Chua Shin Cheng
2014-01-01
Full Text Available Reversible logic circuits have been historically motivated by theoretical research in low-power, and recently attracted interest as components of the quantum algorithm, optical computing and nanotechnology. However due to the intrinsic property of reversible logic, traditional irreversible logic design and synthesis methods cannot be carried out. Thus a new set of algorithms are developed correctly to synthesize reversible logic circuit. This paper presents a comprehensive literature review with comparative study on heuristic based reversible logic synthesis. It reviews a range of heuristic based reversible logic synthesis techniques reported by researchers (BDD-based, cycle-based, search-based, non-search-based, rule-based, transformation-based, and ESOP-based. All techniques are described in detail and summarized in a table based on their features, limitation, library used and their consideration metric. Benchmark comparison of gate count and quantum cost are analysed for each synthesis technique. Comparing the synthesis algorithm outputs over the years, it can be observed that different approach has been used for the synthesis of reversible circuit. However, the improvements are not significant. Quantum cost and gate count has improved over the years, but arguments and debates are still on certain issues such as the issue of garbage outputs that remain the same. This paper provides the information of all heuristic based synthesis of reversible logic method proposed over the years. All techniques are explained in detail and thus informative for new reversible logic researchers and bridging the knowledge gap in this area.
A nuclear heuristic for application to metaheuristics in-core fuel management optimization
Energy Technology Data Exchange (ETDEWEB)
Meneses, Anderson Alvarenga de Moura, E-mail: ameneses@lmp.ufrj.b [COPPE/Federal University of Rio de Janeiro, RJ (Brazil). Nuclear Engineering Program; Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, TI (Switzerland); Gambardella, Luca Maria, E-mail: luca@idsia.c [Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, TI (Switzerland); Schirru, Roberto, E-mail: schirru@lmp.ufrj.b [COPPE/Federal University of Rio de Janeiro, RJ (Brazil). Nuclear Engineering Program
2009-07-01
The In-Core Fuel Management Optimization (ICFMO) is a well-known problem of nuclear engineering whose features are complexity, high number of feasible solutions, and a complex evaluation process with high computational cost, thus it is prohibitive to have a great number of evaluations during an optimization process. Heuristics are criteria or principles for deciding which among several alternative courses of action are more effective with respect to some goal. In this paper, we propose a new approach for the use of relational heuristics for the search in the ICFMO. The Heuristic is based on the reactivity of the fuel assemblies and their position into the reactor core. It was applied to random search, resulting in less computational effort concerning the number of evaluations of loading patterns during the search. The experiments demonstrate that it is possible to achieve results comparable to results in the literature, for future application to metaheuristics in the ICFMO. (author)
Nonparametric Comparison of Two Dynamic Parameter Setting Methods in a Meta-Heuristic Approach
Directory of Open Access Journals (Sweden)
Seyhun HEPDOGAN
2007-10-01
Full Text Available Meta-heuristics are commonly used to solve combinatorial problems in practice. Many approaches provide very good quality solutions in a short amount of computational time; however most meta-heuristics use parameters to tune the performance of the meta-heuristic for particular problems and the selection of these parameters before solving the problem can require much time. This paper investigates the problem of setting parameters using a typical meta-heuristic called Meta-RaPS (Metaheuristic for Randomized Priority Search.. Meta-RaPS is a promising meta-heuristic optimization method that has been applied to different types of combinatorial optimization problems and achieved very good performance compared to other meta-heuristic techniques. To solve a combinatorial problem, Meta-RaPS uses two well-defined stages at each iteration: construction and local search. After a number of iterations, the best solution is reported. Meta-RaPS performance depends on the fine tuning of two main parameters, priority percentage and restriction percentage, which are used during the construction stage. This paper presents two different dynamic parameter setting methods for Meta-RaPS. These dynamic parameter setting approaches tune the parameters while a solution is being found. To compare these two approaches, nonparametric statistic approaches are utilized since the solutions are not normally distributed. Results from both these dynamic parameter setting methods are reported.
Multiobjective Optimization of Water Distribution Networks Using Fuzzy Theory and Harmony Search
National Research Council Canada - National Science Library
Zong Woo Geem
2015-01-01
Thus far, various phenomenon-mimicking algorithms, such as genetic algorithm, simulated annealing, tabu search, shuffled frog-leaping, ant colony optimization, harmony search, cross entropy, scatter...
Zhang, Lu; Du, Hongru; Zhao, Yannan; Wu, Rongwei; Zhang, Xiaolei
2017-01-01
"The Belt and Road" initiative has been expected to facilitate interactions among numerous city centers. This initiative would generate a number of centers, both economic and political, which would facilitate greater interaction. To explore how information flows are merged and the specific opportunities that may be offered, Chinese cities along "the Belt and Road" are selected for a case study. Furthermore, urban networks in cyberspace have been characterized by their infrastructure orientation, which implies that there is a relative dearth of studies focusing on the investigation of urban hierarchies by capturing information flows between Chinese cities along "the Belt and Road". This paper employs Baidu, the main web search engine in China, to examine urban hierarchies. The results show that urban networks become more balanced, shifting from a polycentric to a homogenized pattern. Furthermore, cities in networks tend to have both a hierarchical system and a spatial concentration primarily in regions such as Beijing-Tianjin-Hebei, Yangtze River Delta and the Pearl River Delta region. Urban hierarchy based on web search activity does not follow the existing hierarchical system based on geospatial and economic development in all cases. Moreover, urban networks, under the framework of "the Belt and Road", show several significant corridors and more opportunities for more cities, particularly western cities. Furthermore, factors that may influence web search activity are explored. The results show that web search activity is significantly influenced by the economic gap, geographical proximity and administrative rank of the city.
Directory of Open Access Journals (Sweden)
Lu Zhang
Full Text Available "The Belt and Road" initiative has been expected to facilitate interactions among numerous city centers. This initiative would generate a number of centers, both economic and political, which would facilitate greater interaction. To explore how information flows are merged and the specific opportunities that may be offered, Chinese cities along "the Belt and Road" are selected for a case study. Furthermore, urban networks in cyberspace have been characterized by their infrastructure orientation, which implies that there is a relative dearth of studies focusing on the investigation of urban hierarchies by capturing information flows between Chinese cities along "the Belt and Road". This paper employs Baidu, the main web search engine in China, to examine urban hierarchies. The results show that urban networks become more balanced, shifting from a polycentric to a homogenized pattern. Furthermore, cities in networks tend to have both a hierarchical system and a spatial concentration primarily in regions such as Beijing-Tianjin-Hebei, Yangtze River Delta and the Pearl River Delta region. Urban hierarchy based on web search activity does not follow the existing hierarchical system based on geospatial and economic development in all cases. Moreover, urban networks, under the framework of "the Belt and Road", show several significant corridors and more opportunities for more cities, particularly western cities. Furthermore, factors that may influence web search activity are explored. The results show that web search activity is significantly influenced by the economic gap, geographical proximity and administrative rank of the city.
A single cognitive heuristic process meets the complexity of domain-specific moral heuristics.
Dubljević, Veljko; Racine, Eric
2014-10-01
The inherence heuristic (a) offers modest insights into the complex nature of both the is-ought tension in moral reasoning and moral reasoning per se, and (b) does not reflect the complexity of domain-specific moral heuristics. Formal and general in nature, we contextualize the process described as "inherence heuristic" in a web of domain-specific heuristics (e.g., agent specific; action specific; consequences specific).
Wei, Z; Zhang, X L; Rao, H X; Wang, H F; Wang, X; Qiu, L X
2016-06-01
Under the available data gathered from a coronary study questionnaires with 10 792 cases, this article constructs a Bayesian network model based on the tabu search algorithm and calculates the conditional probability of each node, using the Maximum-likelihood. Pros and cons of the Bayesian network model are evaluated to compare against the logistic regression model in the analysis of coronary factors. Applicability of this network model in clinical study is also investigated. Results show that Bayesian network model can reveal the complex correlations among influencing factors on the coronary and the relationship with coronary heart diseases. Bayesian network model seems promising and more practical than the logistic regression model in analyzing the influencing factors of coronary heart disease.
Exact and heuristic solutions to the Double TSP with Multiple Stacks
DEFF Research Database (Denmark)
Petersen, Hanne Løhmann; Archetti, Claudia; Madsen, Oli B.G.
-pallet, which can be loaded in 3 stacks in a standard 40 foot container. Different exact and heuristic solution approaches to the DTSPMS have been implemented and tested. The exact approaches are based on different mathematical formulations of the problem which are solved using branch-and-cut. One formulation...... instances. The implemented heuristics include tabu search, simulated annealing and large neighbourhood search. Particularly the LNS approach shows promising results. It finds the known optimal solution of smaller instances (15 orders) within 10 seconds in most cases, and in 3 minutes it finds solutions...
Heuristics and bias in homeopathy.
Souter, K
2006-10-01
The practice of Homeopathy ought to be strictly logical. In the Organon Samuel Hahnemann gives the impression that the unprejudiced observer should be able to follow an algorithmic route to the simillimum in every case. Judgement and Decision Research, however, indicates that when people grapple with complex systems like homeopathy they are more likely to use heuristics or empirical rules to help them reach a solution. Thus Hahnemann's concept of the unprejudiced observer is virtually impossible to attain. There is inevitable bias in both case-taking and remedy selection. Understanding the types of bias may enable the practitioner to reduce his/her own bias.
Windows .NET Network Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST)
Dowd, Scot E; Zaragoza, Joaquin; Rodriguez, Javier R; Oliver, Melvin J; Payton, Paxton R
2005-01-01
Background BLAST is one of the most common and useful tools for Genetic Research. This paper describes a software application we have termed Windows .NET Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST), which enhances the BLAST utility by improving usability, fault recovery, and scalability in a Windows desktop environment. Our goal was to develop an easy to use, fault tolerant, high-throughput BLAST solution that incorporates a comprehensive BLAST result viewer with curation and annotation functionality. Results W.ND-BLAST is a comprehensive Windows-based software toolkit that targets researchers, including those with minimal computer skills, and provides the ability increase the performance of BLAST by distributing BLAST queries to any number of Windows based machines across local area networks (LAN). W.ND-BLAST provides intuitive Graphic User Interfaces (GUI) for BLAST database creation, BLAST execution, BLAST output evaluation and BLAST result exportation. This software also provides several layers of fault tolerance and fault recovery to prevent loss of data if nodes or master machines fail. This paper lays out the functionality of W.ND-BLAST. W.ND-BLAST displays close to 100% performance efficiency when distributing tasks to 12 remote computers of the same performance class. A high throughput BLAST job which took 662.68 minutes (11 hours) on one average machine was completed in 44.97 minutes when distributed to 17 nodes, which included lower performance class machines. Finally, there is a comprehensive high-throughput BLAST Output Viewer (BOV) and Annotation Engine components, which provides comprehensive exportation of BLAST hits to text files, annotated fasta files, tables, or association files. Conclusion W.ND-BLAST provides an interactive tool that allows scientists to easily utilizing their available computing resources for high throughput and comprehensive sequence analyses. The install package for W.ND-BLAST is freely downloadable from
Heuristics for multiobjective multiple sequence alignment.
Abbasi, Maryam; Paquete, Luís; Pereira, Francisco B
2016-07-15
Aligning multiple sequences arises in many tasks in Bioinformatics. However, the alignments produced by the current software packages are highly dependent on the parameters setting, such as the relative importance of opening gaps with respect to the increase of similarity. Choosing only one parameter setting may provide an undesirable bias in further steps of the analysis and give too simplistic interpretations. In this work, we reformulate multiple sequence alignment from a multiobjective point of view. The goal is to generate several sequence alignments that represent a trade-off between maximizing the substitution score and minimizing the number of indels/gaps in the sum-of-pairs score function. This trade-off gives to the practitioner further information about the similarity of the sequences, from which she could analyse and choose the most plausible alignment. We introduce several heuristic approaches, based on local search procedures, that compute a set of sequence alignments, which are representative of the trade-off between the two objectives (substitution score and indels). Several algorithm design options are discussed and analysed, with particular emphasis on the influence of the starting alignment and neighborhood search definitions on the overall performance. A perturbation technique is proposed to improve the local search, which provides a wide range of high-quality alignments. The proposed approach is tested experimentally on a wide range of instances. We performed several experiments with sequences obtained from the benchmark database BAliBASE 3.0. To evaluate the quality of the results, we calculate the hypervolume indicator of the set of score vectors returned by the algorithms. The results obtained allow us to identify reasonably good choices of parameters for our approach. Further, we compared our method in terms of correctly aligned pairs ratio and columns correctly aligned ratio with respect to reference alignments. Experimental results show
Heuristic algorithms and a spatial decision support system for locating hydrogen-refueling stations
Lim, Seow
The vision of using hydrogen energy to replace fossil fuels as the primary energy carrier for our transportation infrastructure has been gaining recognition in recent years. The obstacles facing the hydrogen economy vision are technological feasibilities and the cost of infrastructure buildup. Technological feasibilities include the production, storage, distribution, and refueling of hydrogen energy in a safe and economical manner. The cost of infrastructure development includes the building of production plants, storage facilities, distribution network, and refueling stations of hydrogen energy. The purpose of the research is to develop heuristic algorithms and a spatial decision support system (SDSS) to facilitate efficient planning of the refueling infrastructure of hydrogen energy. Facility location-allocation models, specifically the Flow Refueling Location Model (FRLM), have been applied to determine the combination of refueling stations to be built in order to maximize the flow covered with a fixed investment cost. A mixed-integer programming version of the model has been formulated and published. While the mixed-integer programming model could be used to obtain an optimal solution for a problem, it is slow and inefficient in solving problems with a large network and large number of candidate facilities. In this research, heuristics algorithms, specifically the greedy adding, greedy adding with substitution, and genetic algorithm, are developed and applied to solve the FRLM problem. These algorithms are shown to be effective and efficient in solving complex FRLM-problems. The SDSS presented in this research integrates geographical information systems (GIS) and heuristic search algorithms to provide a flexible and powerful system for selecting the locations of hydrogen refueling stations in a real-world scenario. GIS is used to gather and process the input for the model, such as the candidate facilities and traffic flows. The SDSS also uses GIS to display and
Directory of Open Access Journals (Sweden)
Mesay Belete Bejiga
2017-01-01
Full Text Available Following an avalanche, one of the factors that affect victims’ chance of survival is the speed with which they are located and dug out. Rescue teams use techniques like trained rescue dogs and electronic transceivers to locate victims. However, the resources and time required to deploy rescue teams are major bottlenecks that decrease a victim’s chance of survival. Advances in the field of Unmanned Aerial Vehicles (UAVs have enabled the use of flying robots equipped with sensors like optical cameras to assess the damage caused by natural or manmade disasters and locate victims in the debris. In this paper, we propose assisting avalanche search and rescue (SAR operations with UAVs fitted with vision cameras. The sequence of images of the avalanche debris captured by the UAV is processed with a pre-trained Convolutional Neural Network (CNN to extract discriminative features. A trained linear Support Vector Machine (SVM is integrated at the top of the CNN to detect objects of interest. Moreover, we introduce a pre-processing method to increase the detection rate and a post-processing method based on a Hidden Markov Model to improve the prediction performance of the classifier. Experimental results conducted on two different datasets at different levels of resolution show that the detection performance increases with an increase in resolution, while the computation time increases. Additionally, they also suggest that a significant decrease in processing time can be achieved thanks to the pre-processing step.
Conspicuous Waste and Representativeness Heuristic
Directory of Open Access Journals (Sweden)
Tatiana M. Shishkina
2017-12-01
Full Text Available The article deals with the similarities between conspicuous waste and representativeness heuristic. The conspicuous waste is analyzed according to the classic Veblen’ interpretation as a strategy to increase social status through conspicuous consumption and conspicuous leisure. In “The Theory of the Leisure Class” Veblen introduced two different types of utility – conspicuous and functional. The article focuses on the possible benefits of the analysis of conspicuous utility not only in terms of institutional economic theory, but also in terms of behavioral economics. To this end, the representativeness heuristics is considered, on the one hand, as a way to optimize the decision-making process, which allows to examine it in comparison with procedural rationality by Simon. On the other hand, it is also analyzed as cognitive bias within the Kahneman and Twersky’ approach. The article provides the analysis of the patterns in the deviations from the rational behavior strategy that could be observed in case of conspicuous waste both in modern market economies in the form of conspicuous consumption and in archaic economies in the form of gift-exchange. The article also focuses on the marketing strategies for luxury consumption’ advertisement. It highlights the impact of the symbolic capital (in Bourdieu’ interpretation on the social and symbolic payments that actors get from the act of conspicuous waste. This allows to perform a analysis of conspicuous consumption both as a rational way to get the particular kind of payments, and, at the same time, as a form of institutionalized cognitive bias.
Automatic Choice of Scheduling Heuristics for Parallel/Distributed Computing
Directory of Open Access Journals (Sweden)
Clayton S. Ferner
1999-01-01
Full Text Available Task mapping and scheduling are two very difficult problems that must be addressed when a sequential program is transformed into a parallel program. Since these problems are NP‐hard, compiler writers have opted to concentrate their efforts on optimizations that produce immediate gains in performance. As a result, current parallelizing compilers either use very simple methods to deal with task scheduling or they simply ignore it altogether. Unfortunately, the programmer does not have this luxury. The burden of repartitioning or rescheduling, should the compiler produce inefficient parallel code, lies entirely with the programmer. We were able to create an algorithm (called a metaheuristic, which automatically chooses a scheduling heuristic for each input program. The metaheuristic produces better schedules in general than the heuristics upon which it is based. This technique was tested on a suite of real scientific programs written in SISAL and simulated on four different network configurations. Averaged over all of the test cases, the metaheuristic out‐performed all eight underlying scheduling algorithms; beating the best one by 2%, 12%, 13%, and 3% on the four separate network configurations. It is able to do this, not always by picking the best heuristic, but rather by avoiding the heuristics when they would produce very poor schedules. For example, while the metaheuristic only picked the best algorithm about 50% of the time for the 100 Gbps Ethernet, its worst decision was only 49% away from optimal. In contrast, the best of the eight scheduling algorithms was optimal 30% of the time, but its worst decision was 844% away from optimal.
Cooperative heuristic multi-agent planning
De Weerdt, M.M.; Tonino, J.F.M.; Witteveen, C.
2001-01-01
In this paper we will use the framework to study cooperative heuristic multi-agent planning. During the construction of their plans, the agents use a heuristic function inspired by the FF planner (l3l). At any time in the process of planning the agents may exchange available resources, or they may
Effective Heuristics for New Venture Formation
Kraaijenbrink, Jeroen
2010-01-01
Entrepreneurs are often under time pressure and may only have a short window of opportunity to launch their new venture. This means they often have no time for rational analytical decisions and rather rely on heuristics. Past research on entrepreneurial heuristics has primarily focused on predictive
A Heuristic Approach to Scheduling University Timetables.
Loo, E. H.; And Others
1986-01-01
Categories of facilities utilization and scheduling requirements to be considered when using a heuristic approach to timetabling are described together with a nine-step algorithm and the computerized timetabling system, Timetable Schedules System (TTS); TTS utilizes heuristic approach. An example demonstrating use of TTS and a program flowchart…
Query Load Balancing by Caching Search Results in Peer-to-Peer Information Retrieval Networks
Tigelaar, A.S.; Hiemstra, Djoerd
2011-01-01
For peer-to-peer web search engines it is important to keep the delay between receiving a query and providing search results within an acceptable range for the end user. How to achieve this remains an open challenge. One way to reduce delays is by caching search results for queries and allowing
In Search of a Network Organization for TNC’s Innovation
DEFF Research Database (Denmark)
Hu, Yimei; Sørensen, Olav Jull
During the past three decades, there are massive researches on innovation networks and network organizations. However, researchers are holding different understandings, some of which even conflict with each other, thus this paper makes an inductive conceptual analysis to clarify what is a network...... discusses the theoretical foundation of network organization, and proposes that since a focal firm has different strength of power in different levels of network, it will have different roles and may not always have the power to “manage” an innovation network....... organization among many kinds of innovation networks based on review of relative literatures. Then this paper moves one step further to introduce a network perspective, i.e. network is the context of firms as well as TNCs, and market and hierarchy can be analyzed from a network approach. Further on, this paper...
Smoly, Ilan; Carmel, Amir; Shemer-Avni, Yonat; Yeger-Lotem, Esti; Ziv-Ukelson, Michal
2016-03-01
Network querying is a powerful approach to mine molecular interaction networks. Most state-of-the-art network querying tools either confine the search to a prespecified topology in the form of some template subnetwork, or do not specify any topological constraints at all. Another approach is grammar-based queries, which are more flexible and expressive as they allow for expressing the topology of the sought pattern according to some grammar-based logic. Previous grammar-based network querying tools were confined to the identification of paths. In this article, we extend the patterns identified by grammar-based query approaches from paths to trees. For this, we adopt a higher order query descriptor in the form of a regular tree grammar (RTG). We introduce a novel problem and propose an algorithm to search a given graph for the k highest scoring subgraphs matching a tree accepted by an RTG. Our algorithm is based on the combination of dynamic programming with color coding, and includes an extension of previous k-best parsing optimization approaches to avoid isomorphic trees in the output. We implement the new algorithm and exemplify its application to mining viral infection patterns within molecular interaction networks. Our code is available online.
Windows .NET Network Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST
Directory of Open Access Journals (Sweden)
Oliver Melvin J
2005-04-01
Full Text Available Abstract Background BLAST is one of the most common and useful tools for Genetic Research. This paper describes a software application we have termed Windows .NET Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST, which enhances the BLAST utility by improving usability, fault recovery, and scalability in a Windows desktop environment. Our goal was to develop an easy to use, fault tolerant, high-throughput BLAST solution that incorporates a comprehensive BLAST result viewer with curation and annotation functionality. Results W.ND-BLAST is a comprehensive Windows-based software toolkit that targets researchers, including those with minimal computer skills, and provides the ability increase the performance of BLAST by distributing BLAST queries to any number of Windows based machines across local area networks (LAN. W.ND-BLAST provides intuitive Graphic User Interfaces (GUI for BLAST database creation, BLAST execution, BLAST output evaluation and BLAST result exportation. This software also provides several layers of fault tolerance and fault recovery to prevent loss of data if nodes or master machines fail. This paper lays out the functionality of W.ND-BLAST. W.ND-BLAST displays close to 100% performance efficiency when distributing tasks to 12 remote computers of the same performance class. A high throughput BLAST job which took 662.68 minutes (11 hours on one average machine was completed in 44.97 minutes when distributed to 17 nodes, which included lower performance class machines. Finally, there is a comprehensive high-throughput BLAST Output Viewer (BOV and Annotation Engine components, which provides comprehensive exportation of BLAST hits to text files, annotated fasta files, tables, or association files. Conclusion W.ND-BLAST provides an interactive tool that allows scientists to easily utilizing their available computing resources for high throughput and comprehensive sequence analyses. The install package for W.ND-BLAST is
Multiobjective hyper heuristic scheme for system design and optimization
Rafique, Amer Farhan
2012-11-01
As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.
A NONLINEAR FEASIBILITY PROBLEM HEURISTIC
Directory of Open Access Journals (Sweden)
Sergio Drumond Ventura
2015-04-01
Full Text Available In this work we consider a region S ⊂ given by a finite number of nonlinear smooth convex inequalities and having nonempty interior. We assume a point x 0 is given, which is close in certain norm to the analytic center of S, and that a new nonlinear smooth convex inequality is added to those defining S (perturbed region. It is constructively shown how to obtain a shift of the right-hand side of this inequality such that the point x 0 is still close (in the same norm to the analytic center of this shifted region. Starting from this point and using the theoretical results shown, we develop a heuristic that allows us to obtain the approximate analytic center of the perturbed region. Then, we present a procedure to solve the problem of nonlinear feasibility. The procedure was implemented and we performed some numerical tests for the quadratic (random case.
Measurement of Gaseous Oxidized Mercury at a SEARCH Network Site in Florida, USA
Huang, J.; Miller, M. B.; Gustin, M. S.
2013-12-01
There are three operationally defined forms of mercury (Hg) that have been measured in the atmosphere. These include gaseous elemental Hg (GEM), gaseous oxidized Hg (GOM), and particle-bound Hg (PBM). The chemical compounds that make up GOM are currently not well understood, and because of this we do not understand its transport and fate. Additionally, there are limitations associated with the current measurement method, the Tekran 2537/1130/1135 system. Recent work has shown that this system underestimates GOM concentrations, and may not measure all forms. Here we describe work building on ongoing research that focuses on understanding the limitations associated with the instrument, and the chemical forms of GOM. Mercury data have been collected at a Southeastern Aerosol Research and Characterization (SEARCH) network site, Outlying Landing Field (OLF), by the University of Nevada-Reno since 2006. This site is located near the Gulf of Mexico in western Florida. This site is potentially influenced by multiple Hg sources including marine air, electricity generating facilities, mobile sources, and long range transport from high elevation and inland regions. Recent work using data from this location and two others in Florida indicated that on top of background deposition, Hg input to OLF is due to local mobile sources, and long range transport in the spring. Air masses with different chemistry have been hypothesized to carry different GOM compounds. To test this hypothesis, an active Hg sampling system that collects GOM on nylon and cation-exchange membranes is being deployed at OLF. Measurements started March 2013. Here we will present data collected so far, and compare concentrations measured to those obtained using a Tekran system. Ancillary data including meteorology, criteria air pollutants, and those collected using surrogated surfaces for dry Hg deposition and Hg passive samplers will be applied to help understand the sources of GOM. Back trajectory analyses
Grid-Search Location Methods for Ground-Truth Collection From Local and Regional Seismic Networks
Energy Technology Data Exchange (ETDEWEB)
William Rodi; Craig A. Schultz; Gardar Johannesson; Stephen C. Myers
2005-05-13
This project investigated new techniques for improving seismic event locations derived from regional and local networks. The technqiues include a new approach to empirical travel-time calibration that simultaneously fits data from multiple stations and events, using a generalization of the kriging method, and predicts travel-time corrections for arbitrary event-station paths. We combined this calibration approach with grid-search event location to produce a prototype new multiple-event location method that allows the use of spatially well-distributed events and takes into account correlations between the travel-time corrections from proximate event-station paths. Preliminary tests with a high quality data set from Nevada Test Site explosions indicated that our new calibration/location method offers improvement over the conventional multiple-event location methods now in common use, and is applicable to more general event-station geometries than the conventional methods. The tests were limited, however, and further research is needed to fully evaluate, and improve, the approach. Our project also demonstrated the importance of using a realistic model for observational errors in an event location procedure. We took the initial steps in developing a new error model based on mixture-of-Gaussians probability distributions, which possess the properties necessary to characterize the complex arrival time error processes that can occur when picking low signal-to-noise arrivals. We investigated various inference methods for fitting these distributions to observed travel-time residuals, including a Markov Chain Monte Carlo technique for computing Bayesian estimates of the distribution parameters.
THE HEURISTIC FUNCTION OF SPORT
Directory of Open Access Journals (Sweden)
Adam Petrović
2012-09-01
Full Text Available Being a significant area of human activity, sport has multiple functions. One of the more important functions of sport, especially top sport, is the inventive heuristic function. Creative work, being a process of creating new values, represents a significant possibility for advancement of sport. This paper aims at pointing at the various dimensions of human creative work, at the creative work which can be seen in sport (in a narrow sense and at the scientific and practical areas which borderline sport. The method of theoretical analysis of different approaches to the phenomenon of creative work , both in general and in sport, was applied in this paper. This area can be systematized according to various criterion : the level of creative work, different fields where it appears, the subjects of creative work - creators etc. Case analysis shows that the field of creative work in sport is widening and deepening constantly. There are different levels of creativity not only in the system of training and competition, but in a wider social context of sport as well. As a process of human spirit and mind the creative work belongs not just to athletes and coaches, but also to all the people and social groups who's creative power manifests itself in sport. The classification of creative work in sport according to various criterion allows for heuristic function of sport to be explained comprehensively and to create an image how do the sparks of human spirit improve the micro cosmos of sport. A thorough classification of creative work in sport allows for a detailed analysis of all the elements of creative work and each of it’s area in sport. In this way the progress in sport , as a consequence of innovations in both competitions and athletes’ training and of everything that goes with those activities, can be guided into the needed direction more easily as well as studied and applied.
Simple heuristics and rules of thumb: where psychologists and behavioural biologists might meet.
Hutchinson, John M C; Gigerenzer, Gerd
2005-05-31
The Centre for Adaptive Behaviour and Cognition (ABC) has hypothesised that much human decision-making can be described by simple algorithmic process models (heuristics). This paper explains this approach and relates it to research in biology on rules of thumb, which we also review. As an example of a simple heuristic, consider the lexicographic strategy of Take The Best for choosing between two alternatives: cues are searched in turn until one discriminates, then search stops and all other cues are ignored. Heuristics consist of building blocks, and building blocks exploit evolved or learned abilities such as recognition memory; it is the complexity of these abilities that allows the heuristics to be simple. Simple heuristics have an advantage in making decisions fast and with little information, and in avoiding overfitting. Furthermore, humans are observed to use simple heuristics. Simulations show that the statistical structures of different environments affect which heuristics perform better, a relationship referred to as ecological rationality. We contrast ecological rationality with the stronger claim of adaptation. Rules of thumb from biology provide clearer examples of adaptation because animals can be studied in the environments in which they evolved. The range of examples is also much more diverse. To investigate them, biologists have sometimes used similar simulation techniques to ABC, but many examples depend on empirically driven approaches. ABC's theoretical framework can be useful in connecting some of these examples, particularly the scattered literature on how information from different cues is integrated. Optimality modelling is usually used to explain less detailed aspects of behaviour but might more often be redirected to investigate rules of thumb.
Clark, Kevin B
2010-03-01
Fringe quantum biology theories often adopt the concept of Bose-Einstein condensation when explaining how consciousness, emotion, perception, learning, and reasoning emerge from operations of intact animal nervous systems and other computational media. However, controversial empirical evidence and mathematical formalism concerning decoherence rates of bioprocesses keep these frameworks from satisfactorily accounting for the physical nature of cognitive-like events. This study, inspired by the discovery that preferential attachment rules computed by complex technological networks obey Bose-Einstein statistics, is the first rigorous attempt to examine whether analogues of Bose-Einstein condensation precipitate learned decision making in live biological systems as bioenergetics optimization predicts. By exploiting the ciliate Spirostomum ambiguum's capacity to learn and store behavioral strategies advertising mating availability into heuristics of topologically invariant computational networks, three distinct phases of strategy use were found to map onto statistical distributions described by Bose-Einstein, Fermi-Dirac, and classical Maxwell-Boltzmann behavior. Ciliates that sensitized or habituated signaling patterns to emit brief periods of either deceptive 'harder-to-get' or altruistic 'easier-to-get' serial escape reactions began testing condensed on initially perceived fittest 'courting' solutions. When these ciliates switched from their first strategy choices, Bose-Einstein condensation of strategy use abruptly dissipated into a Maxwell-Boltzmann computational phase no longer dominated by a single fittest strategy. Recursive trial-and-error strategy searches annealed strategy use back into a condensed phase consistent with performance optimization. 'Social' decisions performed by ciliates showing no nonassociative learning were largely governed by Fermi-Dirac statistics, resulting in degenerate distributions of strategy choices. These findings corroborate
Comparison of Heuristics for Inhibitory Rule Optimization
Alsolami, Fawaz
2014-09-13
Knowledge representation and extraction are very important tasks in data mining. In this work, we proposed a variety of rule-based greedy algorithms that able to obtain knowledge contained in a given dataset as a series of inhibitory rules containing an expression “attribute ≠ value” on the right-hand side. The main goal of this paper is to determine based on rule characteristics, rule length and coverage, whether the proposed rule heuristics are statistically significantly different or not; if so, we aim to identify the best performing rule heuristics for minimization of rule length and maximization of rule coverage. Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.
Dai, Xinbin; Li, Jun; Liu, Tingsong; Zhao, Patrick Xuechun
2016-01-01
The biological networks controlling plant signal transduction, metabolism and gene regulation are composed of not only tens of thousands of genes, compounds, proteins and RNAs but also the complicated interactions and co-ordination among them. These networks play critical roles in many fundamental mechanisms, such as plant growth, development and environmental response. Although much is known about these complex interactions, the knowledge and data are currently scattered throughout the published literature, publicly available high-throughput data sets and third-party databases. Many 'unknown' yet important interactions among genes need to be mined and established through extensive computational analysis. However, exploring these complex biological interactions at the network level from existing heterogeneous resources remains challenging and time-consuming for biologists. Here, we introduce HRGRN, a graph search-empowered integrative database of Arabidopsis signal transduction, metabolism and gene regulatory networks. HRGRN utilizes Neo4j, which is a highly scalable graph database management system, to host large-scale biological interactions among genes, proteins, compounds and small RNAs that were either validated experimentally or predicted computationally. The associated biological pathway information was also specially marked for the interactions that are involved in the pathway to facilitate the investigation of cross-talk between pathways. Furthermore, HRGRN integrates a series of graph path search algorithms to discover novel relationships among genes, compounds, RNAs and even pathways from heterogeneous biological interaction data that could be missed by traditional SQL database search methods. Users can also build subnetworks based on known interactions. The outcomes are visualized with rich text, figures and interactive network graphs on web pages. The HRGRN database is freely available at http://plantgrn.noble.org/hrgrn/. © The Author 2015. Published
Huesch, Marco D; Galstyan, Aram; Ong, Michael K; Doctor, Jason N
2016-06-01
To pilot public health interventions at women potentially interested in maternity care via campaigns on social media (Twitter), social networks (Facebook), and online search engines (Google Search). Primary data from Twitter, Facebook, and Google Search on users of these platforms in Los Angeles between March and July 2014. Observational study measuring the responses of targeted users of Twitter, Facebook, and Google Search exposed to our sponsored messages soliciting them to start an engagement process by clicking through to a study website containing information on maternity care quality information for the Los Angeles market. Campaigns reached a little more than 140,000 consumers each day across the three platforms, with a little more than 400 engagements each day. Facebook and Google search had broader reach, better engagement rates, and lower costs than Twitter. Costs to reach 1,000 targeted users were approximately in the same range as less well-targeted radio and TV advertisements, while initial engagements-a user clicking through an advertisement-cost less than $1 each. Our results suggest that commercially available online advertising platforms in wide use by other industries could play a role in targeted public health interventions. © Health Research and Educational Trust.
Marcum, Richard A.; Davis, Curt H.; Scott, Grant J.; Nivin, Tyler W.
2017-10-01
We evaluated how deep convolutional neural networks (DCNN) could assist in the labor-intensive process of human visual searches for objects of interest in high-resolution imagery over large areas of the Earth's surface. Various DCNN were trained and tested using fewer than 100 positive training examples (China only) from a worldwide surface-to-air-missile (SAM) site dataset. A ResNet-101 DCNN achieved a 98.2% average accuracy for the China SAM site data. The ResNet-101 DCNN was used to process ˜19.6 M image chips over a large study area in southeastern China. DCNN chip detections (˜9300) were postprocessed with a spatial clustering algorithm to produce a ranked list of ˜2100 candidate SAM site locations. The combination of DCNN processing and spatial clustering effectively reduced the search area by ˜660X (0.15% of the DCNN-processed land area). An efficient web interface was used to facilitate a rapid serial human review of the candidate SAM sites in the China study area. Four novice imagery analysts with no prior imagery analysis experience were able to complete a DCNN-assisted SAM site search in an average time of ˜42 min. This search was ˜81X faster than a traditional visual search over an equivalent land area of ˜88,640 km2 while achieving nearly identical statistical accuracy (˜90% F1).
Formative Research on the Heuristic Task Analysis Process.
Reigeluth, Charles M.; Lee, Ji-Yeon; Peterson, Bruce; Chavez, Michael
Corporate and educational settings increasingly require decision making, problem solving and other complex cognitive skills to handle ill-structured, or heuristic, tasks, but the growing need for heuristic task expertise has outpaced the refinement of task analysis methods for heuristic expertise. The Heuristic Task Analysis (HTA) Method was…
A New Approach to Tuning Heuristic Parameters of Genetic Algorithms
Czech Academy of Sciences Publication Activity Database
Holeňa, Martin
2006-01-01
Roč. 3, č. 3 (2006), s. 562-569 ISSN 1790-0832. [AIKED'06. WSEAS International Conference on Artificial Intelligence , Knowledge Engineering and Data Bases. Madrid, 15.02.2006-17.02.2006] R&D Projects: GA ČR(CZ) GA201/05/0325; GA ČR(CZ) GA201/05/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary optimization * genetic algorithms * heuristic parameters * parameter tuning * artificial neural networks * convergence speed * population diversity Subject RIV: IN - Informatics, Computer Science
Hermawati, Setia; Lawson, Glyn
2016-09-01
Heuristics evaluation is frequently employed to evaluate usability. While general heuristics are suitable to evaluate most user interfaces, there is still a need to establish heuristics for specific domains to ensure that their specific usability issues are identified. This paper presents a comprehensive review of 70 studies related to usability heuristics for specific domains. The aim of this paper is to review the processes that were applied to establish heuristics in specific domains and identify gaps in order to provide recommendations for future research and area of improvements. The most urgent issue found is the deficiency of validation effort following heuristics proposition and the lack of robustness and rigour of validation method adopted. Whether domain specific heuristics perform better or worse than general ones is inconclusive due to lack of validation quality and clarity on how to assess the effectiveness of heuristics for specific domains. The lack of validation quality also affects effort in improving existing heuristics for specific domain as their weaknesses are not addressed. Copyright © 2016 Elsevier Ltd. All rights reserved.
A direct heuristic algorithm for linear programming
Indian Academy of Sciences (India)
Abstract. An (3) mathematically non-iterative heuristic procedure that needs no artificial variable is presented for solving linear programming problems. An optimality test is included. Numerical experiments depict the utility/scope of such a procedure.
Judgements with errors lead to behavioral heuristics
Ungureanu, S.
2016-01-01
A decision process robust to errors in the estimation of values, probabilities and times will employ heuristics that generate consistent apparent biases like loss aversion, nonlinear probability weighting with discontinuities and present bias.
Energy Technology Data Exchange (ETDEWEB)
Yamamoto, Lia; Arruda, Lucia Valeria Ramos de; Libert, Nikolas [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil)
2008-07-01
This work presents the VNS heuristic technique applied on batches ordering in a real network of petroleum derivatives distribution. These ordering have great influence in operational scheduling of a pipeline network. The operational scheduling purposes the efficient utilization of the resources, resulting on a better performance. Due to the great complexity of the real network problem and the necessity of its resolution in little computational time, it was adopted a problem subdivision in assignment of resources, sequencing and timing. In the resources assignment stage, it is considered the production/consumption functions and the products tankages to determine the total batches, including its volume, flow rate and the time-windows to satisfy the demand. These data are used in the sequencing stage, where a VNS based model determines the batches ordering. In a final step, the last block, realize the temporisation considering the network operational constraints. This work shows the results from the optimization of the sequencing stage which aims the improvement of the solution quality of scheduling. (author)
Extracting Backbones from Weighted Complex Networks with Incomplete Information
Directory of Open Access Journals (Sweden)
Liqiang Qian
2015-01-01
Full Text Available The backbone is the natural abstraction of a complex network, which can help people understand a networked system in a more simplified form. Traditional backbone extraction methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency—the exhaustive search of all nodes or edges is often prohibitively expensive. In this paper, we propose a backbone extraction heuristic with incomplete information (BEHwII to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way, which only relies on the local/incomplete knowledge rather than the global view of the network. Experimental results on four real-life networks demonstrate the advantage of BEHwII over the classic disparity filter method by either effectiveness or efficiency validity.
A Modularity Degree Based Heuristic Community Detection Algorithm
Directory of Open Access Journals (Sweden)
Dongming Chen
2014-01-01
Full Text Available A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.
Heuristics and Biases in Retirement Savings Behavior
Shlomo Benartzi; Richard Thaler
2007-01-01
Standard economic theories of saving implicitly assume that households have the cognitive ability to solve the relevant optimization problem and the willpower to execute the optimal plan. Both of the implicit assumptions are suspect. Even among economists, few spend much time calculating a personal optimal savings rate. Instead, most people cope by adopting simple heuristics, or rules of thumb. In this paper, we investigate both the heuristics and the biases that emerge in the area of retirem...
Investigations of quantum heuristics for optimization
Rieffel, Eleanor; Hadfield, Stuart; Jiang, Zhang; Mandra, Salvatore; Venturelli, Davide; Wang, Zhihui
We explore the design of quantum heuristics for optimization, focusing on the quantum approximate optimization algorithm, a metaheuristic developed by Farhi, Goldstone, and Gutmann. We develop specific instantiations of the of quantum approximate optimization algorithm for a variety of challenging combinatorial optimization problems. Through theoretical analyses and numeric investigations of select problems, we provide insight into parameter setting and Hamiltonian design for quantum approximate optimization algorithms and related quantum heuristics, and into their implementation on hardware realizable in the near term.
Case Based Heuristic Selection for Timetabling Problems
Burke, Edmund; Petrovic, Sanja; Qu, Rong
2006-01-01
This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than the current state of the art. Heuristics that worked well in previous similar situations are memorized in a case base and are retrieved for solving the problem in hand. Knowledge discovery techniques are employed in two distinct scenarios. Firstl...
Design Principles of Regulatory Networks: Searching for the Molecular Algorithms of the Cell
Lim, Wendell A.; Lee, Connie M.; Tang, Chao
2013-01-01
A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks. PMID:23352241
Design principles of regulatory networks: searching for the molecular algorithms of the cell.
Lim, Wendell A; Lee, Connie M; Tang, Chao
2013-01-24
A challenge in biology is to understand how complex molecular networks in the cell execute sophisticated regulatory functions. Here we explore the idea that there are common and general principles that link network structures to biological functions, principles that constrain the design solutions that evolution can converge upon for accomplishing a given cellular task. We describe approaches for classifying networks based on abstract architectures and functions, rather than on the specific molecular components of the networks. For any common regulatory task, can we define the space of all possible molecular solutions? Such inverse approaches might ultimately allow the assembly of a design table of core molecular algorithms that could serve as a guide for building synthetic networks and modulating disease networks. Copyright © 2013 Elsevier Inc. All rights reserved.
Social heuristics shape intuitive cooperation.
Rand, David G; Peysakhovich, Alexander; Kraft-Todd, Gordon T; Newman, George E; Wurzbacher, Owen; Nowak, Martin A; Greene, Joshua D
2014-04-22
Cooperation is central to human societies. Yet relatively little is known about the cognitive underpinnings of cooperative decision making. Does cooperation require deliberate self-restraint? Or is spontaneous prosociality reined in by calculating self-interest? Here we present a theory of why (and for whom) intuition favors cooperation: cooperation is typically advantageous in everyday life, leading to the formation of generalized cooperative intuitions. Deliberation, by contrast, adjusts behaviour towards the optimum for a given situation. Thus, in one-shot anonymous interactions where selfishness is optimal, intuitive responses tend to be more cooperative than deliberative responses. We test this 'social heuristics hypothesis' by aggregating across every cooperation experiment using time pressure that we conducted over a 2-year period (15 studies and 6,910 decisions), as well as performing a novel time pressure experiment. Doing so demonstrates a positive average effect of time pressure on cooperation. We also find substantial variation in this effect, and show that this variation is partly explained by previous experience with one-shot lab experiments.
Heuristic thinking makes a chemist smart.
Graulich, Nicole; Hopf, Henning; Schreiner, Peter R
2010-05-01
We focus on the virtually neglected use of heuristic principles in understanding and teaching of organic chemistry. As human thinking is not comparable to computer systems employing factual knowledge and algorithms--people rarely make decisions through careful considerations of every possible event and its probability, risks or usefulness--research in science and teaching must include psychological aspects of the human decision making processes. Intuitive analogical and associative reasoning and the ability to categorize unexpected findings typically demonstrated by experienced chemists should be made accessible to young learners through heuristic concepts. The psychology of cognition defines heuristics as strategies that guide human problem-solving and deciding procedures, for example with patterns, analogies, or prototypes. Since research in the field of artificial intelligence and current studies in the psychology of cognition have provided evidence for the usefulness of heuristics in discovery, the status of heuristics has grown into something useful and teachable. In this tutorial review, we present a heuristic analysis of a familiar fundamental process in organic chemistry--the cyclic six-electron case, and we show that this approach leads to a more conceptual insight in understanding, as well as in teaching and learning.
Special issue on searching and mining the web and social networks
Litvak, Nelli; Vigna, Sebastiano
2014-01-01
The past few decades have seen the rise of online social networks as a worldwide phenomenon with a high impact on our society. Beyond the obvious exposure phenomena, with obvious implications on security and privacy, people have started to become acquainted—even married!—in online social networks. In parallel, we have seen an enormous growth in terms of the number of published articles in computer science, mathematics and physics that study the organization of such networks. The availability ...
Pustelny, S
2016-01-01
In this letter, we describe a novel scheme for searching for physics beyond the Standard Model. The idea is based on correlation of time-synchronized readouts of distant ($\\gtrsim$100~km) optical magnetometers. Such an approach limits hard-to-identify local transient noise, providing the system with unique capabilities of identification of global transient events. Careful analysis of the signal can reveal the nature of the events (e.g., its nonmagnetic origin), which opens avenues for new class of exotic-physics searches (searches for global transient exotic spin couplings) and tests of yet unverified theoretical models.
Use of Tabu Search in a Solver to Map Complex Networks onto Emulab Testbeds
National Research Council Canada - National Science Library
MacDonald, Jason E
2007-01-01
The University of Utah's solver for the testbed mapping problem uses a simulated annealing metaheuristic algorithm to map a researcher's experimental network topology onto available testbed resources...
Directory of Open Access Journals (Sweden)
Farzad Kiani
2016-01-01
Full Text Available Energy issue is one of the most important problems in wireless sensor networks. They consist of low-power sensor nodes and a few base station nodes. They must be adaptive and efficient in data transmission to sink in various areas. This paper proposes an aware-routing protocol based on clustering and recursive search approaches. The paper focuses on the energy efficiency issue with various measures such as prolonging network lifetime along with reducing energy consumption in the sensor nodes and increasing the system reliability. Our proposed protocol consists of two phases. In the first phase (network development phase, the sensors are placed into virtual layers. The second phase (data transmission is related to routes discovery and data transferring so it is based on virtual-based Classic-RBFS algorithm in the lake of energy problem environments but, in the nonchargeable environments, all nodes in each layer can be modeled as a random graph and then begin to be managed by the duty cycle method. Additionally, the protocol uses new topology control, data aggregation, and sleep/wake-up schemas for energy saving in the network. The simulation results show that the proposed protocol is optimal in the network lifetime and packet delivery parameters according to the present protocols.
Directory of Open Access Journals (Sweden)
Vinícius Vilar Jacob
2016-01-01
Full Text Available This paper addresses a single-machine scheduling problem with sequence-dependent family setup times. In this problem the jobs are classified into families according to their similarity characteristics. Setup times are required on each occasion when the machine switches from processing jobs in one family to jobs in another family. The performance measure to be minimized is the total tardiness with respect to the given due dates of the jobs. The problem is classified as NP-hard in the ordinary sense. Since the computational complexity associated with the mathematical formulation of the problem makes it difficult for optimization solvers to deal with large-sized instances in reasonable solution time, efficient heuristic algorithms are needed to obtain near-optimal solutions. In this work we propose three heuristics based on the Iterated Local Search (ILS metaheuristic. The first heuristic is a basic ILS, the second uses a dynamic perturbation size, and the third uses a Path Relinking (PR technique as an intensification strategy. We carry out comprehensive computational and statistical experiments in order to analyze the performance of the proposed heuristics. The computational experiments show that the ILS heuristics outperform a genetic algorithm proposed in the literature. The ILS heuristic with dynamic perturbation size and PR intensification has a superior performance compared to other heuristics.
Comparison of Heuristic Methods for the Design of Edge Disjoint Circuits
Phillipson, F.
2015-01-01
In this paper heuristics are studied for the design of edge disjoint circuits. We formulate the Edge Disjoint Circuits Problem (EDCP) in the context of a telecommunication network design problem: the roll-out of Fibre to the Cabinet. This problem setting can be represented by a graph. A small subset
Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies
Segler, Marwin; Preuß, Mike; Waller, Mark P.
2017-01-01
Retrosynthesis is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro- and fine chemicals. In retrosynthesis, a search tree is built by analysing molecules recursively and dissecting them into simpler molecular building blocks until one obtains a set of known building blocks. The search space is intractably large, and it is difficult to determine the value of retrosynthetic positions. Here, we propose to model retrosynthesis as a Markov Decision Process. I...
Use of Tabu Search in a Solver to Map Complex Networks onto Emulab Testbeds
2007-03-01
large number of chemical interactions between genes and proteins. • Complex networks often display a non-uniform distribution of connectivity among...02-base.html. 15. Huffaker, Bradley, Evi Nemeth, and K Claffy. “Otter: A General-purpose Network Visualization Tool”. In Proceedings of INET 1999
Special issue on searching and mining the web and social networks
Litvak, Nelli; Vigna, Sebastiano
2014-01-01
The past few decades have seen the rise of online social networks as a worldwide phenomenon with a high impact on our society. Beyond the obvious exposure phenomena, with obvious implications on security and privacy, people have started to become acquainted—even married!—in online social networks.
Meta-heuristics in cellular manufacturing: A state-of-the-art review
Directory of Open Access Journals (Sweden)
Tamal Ghosh
2011-01-01
Full Text Available Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various meta-heuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area.
Bordier, Cécile; Nicolini, Carlo; Bifone, Angelo
2017-01-01
Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks' community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman's modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.
Directory of Open Access Journals (Sweden)
Cécile Bordier
2017-08-01
Full Text Available Neuroimaging data can be represented as networks of nodes and edges that capture the topological organization of the brain connectivity. Graph theory provides a general and powerful framework to study these networks and their structure at various scales. By way of example, community detection methods have been widely applied to investigate the modular structure of many natural networks, including brain functional connectivity networks. Sparsification procedures are often applied to remove the weakest edges, which are the most affected by experimental noise, and to reduce the density of the graph, thus making it theoretically and computationally more tractable. However, weak links may also contain significant structural information, and procedures to identify the optimal tradeoff are the subject of active research. Here, we explore the use of percolation analysis, a method grounded in statistical physics, to identify the optimal sparsification threshold for community detection in brain connectivity networks. By using synthetic networks endowed with a ground-truth modular structure and realistic topological features typical of human brain functional connectivity networks, we show that percolation analysis can be applied to identify the optimal sparsification threshold that maximizes information on the networks' community structure. We validate this approach using three different community detection methods widely applied to the analysis of brain connectivity networks: Newman's modularity, InfoMap and Asymptotical Surprise. Importantly, we test the effects of noise and data variability, which are critical factors to determine the optimal threshold. This data-driven method should prove particularly useful in the analysis of the community structure of brain networks in populations characterized by different connectivity strengths, such as patients and controls.
Peek, Joshua E. G.; Hargis, Jonathan R.; Jones, Craig R.
2018-01-01
Astronomical instruments produce petabytes of images every year, vastly more than can be inspected by a member of the astronomical community in search of a specific population of structures. Fortunately, the sky is mostly black and source extraction algorithms have been developed to provide searchable catalogs of unconfused sources like stars and galaxies. These tools often fail for studies of more diffuse structures like the interstellar medium and unresolved stellar structures in nearby galaxies, leaving astronomers interested in observations of photodissociation regions, stellar clusters, diffuse interstellar clouds without the crucial ability to search. In this work we present a new path forward for finding structures in large data sets similar to an input structure using convolutional neural networks, transfer learning, and machine learning clustering techniques. We show applications to archival data in the Mikulski Archive for Space Telescopes (MAST).
Heuristic space diversity management in a meta-hyper-heuristic framework
CSIR Research Space (South Africa)
Grobler, J
2014-07-01
Full Text Available IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 6-11 July 2014 Heuristic Space Diversity Management in a Meta-Hyper- Heuristic Framework Jacomine Grobler1 and Andries P. Engelbrecht2 1Department of Industrial and Systems...
Virtual File System Mounting amp Searching With Network JVM For LAN
Directory of Open Access Journals (Sweden)
Nikita Kamble
2015-08-01
Full Text Available Computer technology has rapidly grown over past decades. Most of this can be attributed to the Internet as many computers now have a need to be networked together to establish an online connection. A local area network is a group of computers and associated devices that share a common communication line or wireless link to the service. Typically a LAN compasses computers and peripherals connected to a secure server within a small geographic area such as an office building or home computer and other mobile devices that share resources such as printer or network storage. A LAN is contrasted in principle to a wide area networkWANwhich covers a larger geographic distance and may involve leased telecom circuits while the media for LANs are locally managed. Ethernet are twisted pair cabling amp Wi-Fi are the two most common transmission technologies in use for LAN. The rise of virtualization has fueled the development of virtual LANWLANwhich allows network administrator to logically group network nodes amp partition their networks without the need for major infrastructure changes. In some situations a wireless LAN or Wi-Fi maybe preferable to a wired LAN because of its flexibility amp cost. Companies are asserting WLANs as a replacement for their wired infrastructure as the number of smart phones tablets amp other mobile devices proliferates.
Prediction-based dynamic load-sharing heuristics
Goswami, Kumar K.; Devarakonda, Murthy; Iyer, Ravishankar K.
1993-01-01
The authors present dynamic load-sharing heuristics that use predicted resource requirements of processes to manage workloads in a distributed system. A previously developed statistical pattern-recognition method is employed for resource prediction. While nonprediction-based heuristics depend on a rapidly changing system status, the new heuristics depend on slowly changing program resource usage patterns. Furthermore, prediction-based heuristics can be more effective since they use future requirements rather than just the current system state. Four prediction-based heuristics, two centralized and two distributed, are presented. Using trace driven simulations, they are compared against random scheduling and two effective nonprediction based heuristics. Results show that the prediction-based centralized heuristics achieve up to 30 percent better response times than the nonprediction centralized heuristic, and that the prediction-based distributed heuristics achieve up to 50 percent improvements relative to their nonprediction counterpart.
Towards Optimal Transport Networks
Directory of Open Access Journals (Sweden)
Erik P. Vargo
2010-08-01
Full Text Available Our ultimate goal is to design transportation net- works whose dynamic performance metrics (e.g. pas- senger throughput, passenger delay, and insensitivity to weather disturbances are optimized. Here the fo- cus is on optimizing static features of the network that are known to directly aﬀect the network dynamics. First, we present simulation results which support a connection between maximizing the ﬁrst non-trivial eigenvalue of a network's Laplacian and superior air- port network performance. Then, we explore the ef- fectiveness of a tabu search heuristic for optimizing this metric by comparing experimental results to the- oretical upper bounds. We also consider generating upper bounds on a network's algebraic connectivity via the solution of semideﬁnite programming (SDP relaxations. A modiﬁcation of an existing subgraph extraction algorithm is implemented to explore the underlying regional structures in the U.S. airport net- work, with the hope that the resulting localized struc- tures can be optimized independently and reconnected via a "backbone" network to achieve superior network performance.
Moving multiple sinks through wireless sensor networks for lifetime maximization.
Energy Technology Data Exchange (ETDEWEB)
Petrioli, Chiara (Universita di Roma); Carosi, Alessio (Universita di Roma); Basagni, Stefano (Northeastern University); Phillips, Cynthia Ann
2008-01-01
via a traveling salesman heuristic, and computing feasible transitions using matching algorithms. This algorithm assumes sinks can get a schedule from a central server or a leader sink. If the network owner prefers the sinks make independent decisions, they can use our distributed heuristic. In this heuristic, sinks maintain estimates of the energy distribution in the network and move greedily (with some coordination) based on local search. This application uses the new SUCASA (Solver Utility for Customization with Automatic Symbol Access) facility within the PICO (Parallel Integer and Combinatorial Optimizer) integer programming solver system. SUCASA allows rapid development of customized math programming (search-based) solvers using a problem's natural multidimensional representation. In this case, SUCASA also significantly improves runtime compared to implementations in the ampl math programming language or in perl.
Directory of Open Access Journals (Sweden)
Jeng-Fung Chen
2014-11-01
Full Text Available The accuracy of reservoir flow forecasting has the most significant influence on the assurance of stability and annual operations of hydro-constructions. For instance, accurate forecasting on the ebb and flow of Vietnam’s Hoabinh Reservoir can aid in the preparation and prevention of lowland flooding and drought, as well as regulating electric energy. This raises the need to propose a model that accurately forecasts the incoming flow of the Hoabinh Reservoir. In this study, a solution to this problem based on neural network with the Cuckoo Search (CS algorithm is presented. In particular, we used hydrographic data and predicted total incoming flows of the Hoabinh Reservoir over a period of 10 days. The Cuckoo Search algorithm was utilized to train the feedforward neural network (FNN for prediction. The algorithm optimized the weights between layers and biases of the neuron network. Different forecasting models for the three scenarios were developed. The constructed models have shown high forecasting performance based on the performance indices calculated. These results were also compared with those obtained from the neural networks trained by the particle swarm optimization (PSO and back-propagation (BP, indicating that the proposed approach performed more effectively. Based on the experimental results, the scenario using the rainfall and the flow as input yielded the highest forecasting accuracy when compared with other scenarios. The performance criteria RMSE, MAPE, and R obtained by the CS-FNN in this scenario were calculated as 48.7161, 0.067268 and 0.8965, respectively. These results were highly correlated to actual values. It is expected that this work may be useful for hydrographic forecasting.
Region-wide search and pursuit system using networked intelligent cameras
Komiya, Kazumi; Irisawa, Kouji
2001-11-01
This paper reports a study on new, region-wide search and pursuit system for missing objects such as stolen cars, wandering people, etc. By using image matching processes on the basis of the object properties such as color and shape, the intelligent camera can search the object. Then the camera transmits the properties to the next camera to pursue the object successively. The experimental results show that the system can judge 2 cars as search object among 40 cars under conditions of changing environment. Based on these data the proposed system can accomplish a fundamental step. Finally, research subjects have been picked up for advancement such as accurate shape extraction processing, camera structure for high speed processing and multimedia attributes such as sound.
Lithic raw material procurement in the Moravian Neolithic: the search for extra-regional networks
Directory of Open Access Journals (Sweden)
Martin Kuča
2009-12-01
Full Text Available The study of lithic raw material procurement can contribute to the study of ancient networks. Petrographic analysis combined with systematic mapping of raw material outcrops has been conducted in Moravia and adjacent territories by A. Přichystal over a period of more than three decades. Combined with well excavated (including wet-screening and 14C (radiometric dated sites,allows us to study changes in the distribution networks of raw materials during the Mesolithic and Neolithic periods.
Localization Accuracy of Track-before-Detect Search Strategies for Distributed Sensor Networks
Directory of Open Access Journals (Sweden)
Michael J. Walsh
2007-12-01
Full Text Available The localization accuracy of a track-before-detect search for a target moving across a distributed sensor field is examined in this paper. The localization accuracy of the search is defined in terms of the area of intersection of the spatial-temporal sensor coverage regions, as seen from the perspective of the target. The expected value and variance of this area are derived for sensors distributed randomly according to an arbitrary distribution function. These expressions provide an important design objective for use in the planning of distributed sensor fields. Several examples are provided that experimentally validate the analytical results.
Search heuristics of chess players of different calibers.
Reynolds, R I
1982-01-01
Information-processing models of chess-playing ability have distinguished between players of different calibers solely in terms of perceptual encoding and recognition of chess configurations. A reanalysis of deGroot's verbal protocols of 1965 indicates that players of different calibers direct their attention toward different areas of the board. Grandmasters and masters consider squares that are affected by many pieces, while lesser players direct their attention toward squares on which the pieces are located. In a recall experiment, chess positions were presented that had been randomly generated so as to differ only in the degree to which the pieces converge on the same squares. Masters showed superior recall for briefly presented positions only when the material affects a highly centralized area of the board.
Heuristic search in robot configuration space using variable metric
Verwer, Ben J. H.
1987-01-01
A method to generate obstacle free trajectories for both mobile robots and linked robots is proposed. The approach generates the shortest paths in a configuration space. The metric in the configuration space can be adjusted to obtain a tradeoff between safety and velocity by imposing extra costs on paths near obstacles.
Implementation of a Tabu Search Heuristic for the Examinations ...
African Journals Online (AJOL)
This paper reports on the design and implementation of an algorithm for the construction of an examinations timetable. The Examinations Timetabling Problem is the problem of assigning examinations and candidates to time periods and examination rooms while satisfying a set of specific constraints. Every University has a ...
Quantifying Heuristic Bias: Anchoring, Availability, and Representativeness.
Richie, Megan; Josephson, S Andrew
2018-01-01
Construct: Authors examined whether a new vignette-based instrument could isolate and quantify heuristic bias. Heuristics are cognitive shortcuts that may introduce bias and contribute to error. There is no standardized instrument available to quantify heuristic bias in clinical decision making, limiting future study of educational interventions designed to improve calibration of medical decisions. This study presents validity data to support a vignette-based instrument quantifying bias due to the anchoring, availability, and representativeness heuristics. Participants completed questionnaires requiring assignment of probabilities to potential outcomes of medical and nonmedical scenarios. The instrument randomly presented scenarios in one of two versions: Version A, encouraging heuristic bias, and Version B, worded neutrally. The primary outcome was the difference in probability judgments for Version A versus Version B scenario options. Of 167 participants recruited, 139 enrolled. Participants assigned significantly higher mean probability values to Version A scenario options (M = 9.56, SD = 3.75) than Version B (M = 8.98, SD = 3.76), t(1801) = 3.27, p = .001. This result remained significant analyzing medical scenarios alone (Version A, M = 9.41, SD = 3.92; Version B, M = 8.86, SD = 4.09), t(1204) = 2.36, p = .02. Analyzing medical scenarios by heuristic revealed a significant difference between Version A and B for availability (Version A, M = 6.52, SD = 3.32; Version B, M = 5.52, SD = 3.05), t(404) = 3.04, p = .003, and representativeness (Version A, M = 11.45, SD = 3.12; Version B, M = 10.67, SD = 3.71), t(396) = 2.28, p = .02, but not anchoring. Stratifying by training level, students maintained a significant difference between Version A and B medical scenarios (Version A, M = 9.83, SD = 3.75; Version B, M = 9.00, SD = 3.98), t(465) = 2.29, p = .02, but not residents or attendings. Stratifying by heuristic and training level, availability maintained
User Oriented Trajectory Search for Trip Recommendation
Ding, Ruogu
2012-07-08
Trajectory sharing and searching have received significant attention in recent years. In this thesis, we propose and investigate the methods to find and recommend the best trajectory to the traveler, and mainly focus on a novel technique named User Oriented Trajectory Search (UOTS) query processing. In contrast to conventional trajectory search by locations (spatial domain only), we consider both spatial and textual domains in the new UOTS query. Given a trajectory data set, the query input contains a set of intended places given by the traveler and a set of textual attributes describing the traveler’s preference. If a trajectory is connecting/close to the specified query locations, and the textual attributes of the trajectory are similar to the traveler’s preference, it will be recommended to the traveler. This type of queries can enable many popular applications such as trip planning and recommendation. There are two challenges in UOTS query processing, (i) how to constrain the searching range in two domains and (ii) how to schedule multiple query sources effectively. To overcome the challenges and answer the UOTS query efficiently, a novel collaborative searching approach is developed. Conceptually, the UOTS query processing is conducted in the spatial and textual domains alternately. A pair of upper and lower bounds are devised to constrain the searching range in two domains. In the meantime, a heuristic searching strategy based on priority ranking is adopted for scheduling the multiple query sources, which can further reduce the searching range and enhance the query efficiency notably. Furthermore, the devised collaborative searching approach can be extended to situations where the query locations are ordered. Extensive experiments are conducted on both real and synthetic trajectory data in road networks. Our approach is verified to be effective in reducing both CPU time and disk I/O time.
User oriented trajectory search for trip recommendation
Shang, Shuo
2012-01-01
Trajectory sharing and searching have received significant attentions in recent years. In this paper, we propose and investigate a novel problem called User Oriented Trajectory Search (UOTS) for trip recommendation. In contrast to conventional trajectory search by locations (spatial domain only), we consider both spatial and textual domains in the new UOTS query. Given a trajectory data set, the query input contains a set of intended places given by the traveler and a set of textual attributes describing the traveler\\'s preference. If a trajectory is connecting/close to the specified query locations, and the textual attributes of the trajectory are similar to the traveler\\'e preference, it will be recommended to the traveler for reference. This type of queries can bring significant benefits to travelers in many popular applications such as trip planning and recommendation. There are two challenges in the UOTS problem, (i) how to constrain the searching range in two domains and (ii) how to schedule multiple query sources effectively. To overcome the challenges and answer the UOTS query efficiently, a novel collaborative searching approach is developed. Conceptually, the UOTS query processing is conducted in the spatial and textual domains alternately. A pair of upper and lower bounds are devised to constrain the searching range in two domains. In the meantime, a heuristic searching strategy based on priority ranking is adopted for scheduling the multiple query sources, which can further reduce the searching range and enhance the query efficiency notably. Furthermore, the devised collaborative searching approach can be extended to situations where the query locations are ordered. The performance of the proposed UOTS query is verified by extensive experiments based on real and synthetic trajectory data in road networks. © 2012 ACM.
Analysis of an iterated local search algorithm for vertex cover in sparse random graphs
DEFF Research Database (Denmark)
Witt, Carsten
2012-01-01
finds an optimal cover in polynomial time with a probability arbitrarily close to 1. This behavior relies on the absence of a giant component. As an additional insight into the randomized search, it is shown that the heuristic fails badly also on graphs consisting of a single tree component of maximum......Recently, various randomized search heuristics have been studied for the solution of the minimum vertex cover problem, in particular for sparse random instances according to the G(n,c/n) model, where c>0 is a constant. Methods from statistical physics suggest that the problem is easy if c......search heuristics on random graphs. For csearch heuristic...
Directory of Open Access Journals (Sweden)
Hani Omar
2016-01-01
Full Text Available Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.
Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren
2016-01-01
Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words.
Omar, Hani; Hoang, Van Hai; Liu, Duen-Ren
2016-01-01
Enhancing sales and operations planning through forecasting analysis and business intelligence is demanded in many industries and enterprises. Publishing industries usually pick attractive titles and headlines for their stories to increase sales, since popular article titles and headlines can attract readers to buy magazines. In this paper, information retrieval techniques are adopted to extract words from article titles. The popularity measures of article titles are then analyzed by using the search indexes obtained from Google search engine. Backpropagation Neural Networks (BPNNs) have successfully been used to develop prediction models for sales forecasting. In this study, we propose a novel hybrid neural network model for sales forecasting based on the prediction result of time series forecasting and the popularity of article titles. The proposed model uses the historical sales data, popularity of article titles, and the prediction result of a time series, Autoregressive Integrated Moving Average (ARIMA) forecasting method to learn a BPNN-based forecasting model. Our proposed forecasting model is experimentally evaluated by comparing with conventional sales prediction techniques. The experimental result shows that our proposed forecasting method outperforms conventional techniques which do not consider the popularity of title words. PMID:27313605
Directory of Open Access Journals (Sweden)
Imran A Mir
2014-06-01
Full Text Available Since last few years, social media have profoundly changed the ways of social and business communication. Particularly, social network sites (SNSs have rapidly grown in popularity and number of users globally. They have become the main place for social interaction, discussion and communication. Today, businesses of various types use SNSs for commercial communication. Banner advertising is one of the common methods of commercial communication on SNSs. Advertising is a key source of revenue for many SNSs firms such as Facebook. In fact, the existence of many SNSs owners and advertisers is contingent upon the success of social network advertising (SNA. Users demand free SNS services which makes SNA crucial for SNSs firms. SNA can be effective only if it is aligned with user motivations. Marketing literature identifies pre-purchase search as a primary consumer motivation for using media. The current study aims to identify the effects of pre-purchase search motivation (PSM on user attitudes toward SNA. It also assesses the association between the attitudes toward SNA and users’ banner ad-clicking behavior on SNSs. Data was gathered from 200 university students in Islamabad using offline survey. Results show positive effects of PSM on user attitudes toward SNA. They also show positive association between user attitudes toward SNA and their SNS banner ad-clicking behavior. The firms which promote their products through SNSs to the young South Asian consumers may benefit from the findings of the current study.
Fast Optimal Replica Placement with Exhaustive Search Using Dynamically Reconfigurable Processor
Directory of Open Access Journals (Sweden)
Hidetoshi Takeshita
2011-01-01
Full Text Available This paper proposes a new replica placement algorithm that expands the exhaustive search limit with reasonable calculation time. It combines a new type of parallel data-flow processor with an architecture tuned for fast calculation. The replica placement problem is to find a replica-server set satisfying service constraints in a content delivery network (CDN. It is derived from the set cover problem which is known to be NP-hard. It is impractical to use exhaustive search to obtain optimal replica placement in large-scale networks, because calculation time increases with the number of combinations. To reduce calculation time, heuristic algorithms have been proposed, but it is known that no heuristic algorithm is assured of finding the optimal solution. The proposed algorithm suits parallel processing and pipeline execution and is implemented on DAPDNA-2, a dynamically reconfigurable processor. Experiments show that the proposed algorithm expands the exhaustive search limit by the factor of 18.8 compared to the conventional algorithm search limit running on a Neumann-type processor.
Jack F. Williams
1981-01-01
Seven heuristic algorithms are discussed. Each can be used for production scheduling in an assembly network (a network where each work station has at most one immediate successor work station, but may have any number of immediate predecessor work stations), distribution scheduling in an arborescence network (a network where each warehouse or stocking point is supplied by at most one immediate predecessor stocking point, but may itself supply any number of immediate successor stocking points),...
Heuristic reusable dynamic programming: efficient updates of local sequence alignment.
Hong, Changjin; Tewfik, Ahmed H
2009-01-01
Recomputation of the previously evaluated similarity results between biological sequences becomes inevitable when researchers realize errors in their sequenced data or when the researchers have to compare nearly similar sequences, e.g., in a family of proteins. We present an efficient scheme for updating local sequence alignments with an affine gap model. In principle, using the previous matching result between two amino acid sequences, we perform a forward-backward alignment to generate heuristic searching bands which are bounded by a set of suboptimal paths. Given a correctly updated sequence, we initially predict a new score of the alignment path for each contour to select the best candidates among them. Then, we run the Smith-Waterman algorithm in this confined space. Furthermore, our heuristic alignment for an updated sequence shows that it can be further accelerated by using reusable dynamic programming (rDP), our prior work. In this study, we successfully validate "relative node tolerance bound" (RNTB) in the pruned searching space. Furthermore, we improve the computational performance by quantifying the successful RNTB tolerance probability and switch to rDP on perturbation-resilient columns only. In our searching space derived by a threshold value of 90 percent of the optimal alignment score, we find that 98.3 percent of contours contain correctly updated paths. We also find that our method consumes only 25.36 percent of the runtime cost of sparse dynamic programming (sDP) method, and to only 2.55 percent of that of a normal dynamic programming with the Smith-Waterman algorithm.
Wavelength converter placement in optical networks with dynamic traffic
DEFF Research Database (Denmark)
Buron, Jakob Due; Ruepp, Sarah Renée; Wessing, Henrik
2008-01-01
We evaluate the connection provisioning performance of GMPLS-controlled wavelength routed networks under dynamic traffic load and using three different wavelength converter placement heuristics. Results show that a simple uniform placement heuristic matches the performance of complex heuristics...... under dynamic traffic assumptions....
Nature-inspired novel Cuckoo Search Algorithm for genome ...
Indian Academy of Sciences (India)
Bioinformatics; Cuckoo search; genome sequence assembly; meta- heuristics. 1. Introduction. The innovation in the cutting edge of soft computing technology proposes solutions to solve many challenging bioinformatics problems. A heuristic optimization technique has been applied for most exciting research areas in ...
Thrush, Simon F; Hewitt, Judi E; Parkes, Samantha; Lohrer, Andrew M; Pilditch, Conrad; Woodin, Sarah A; Wethey, David S; Chiantore, Mariachiara; Asnaghi, Valentina; De Juan, Silvia; Kraan, Casper; Rodil, Ivan; Savage, Candida; Van Colen, Carl
2014-06-01
Thresholds profoundly affect our understanding and management of ecosystem dynamics, but we have yet to develop practical techniques to assess the risk that thresholds will be crossed. Combining ecological knowledge of critical system interdependencies with a large-scale experiment, we tested for breaks in the ecosystem interaction network to identify threshold potential in real-world ecosystem dynamics. Our experiment with the bivalves Macomona liliana and Austrovenus stutchburyi on marine sandflats in New Zealand demonstrated that reductions in incident sunlight changed the interaction network between sediment biogeochemical fluxes, productivity, and macrofauna. By demonstrating loss of positive feedbacks and changes in the architecture of the network, we provide mechanistic evidence that stressors lead to break points in dynamics, which theory predicts predispose a system to a critical transition.
Heuristics for the inversion median problem
2010-01-01
Background The study of genome rearrangements has become a mainstay of phylogenetics and comparative genomics. Fundamental in such a study is the median problem: given three genomes find a fourth that minimizes the sum of the evolutionary distances between itself and the given three. Many exact algorithms and heuristics have been developed for the inversion median problem, of which the best known is MGR. Results We present a unifying framework for median heuristics, which enables us to clarify existing strategies and to place them in a partial ordering. Analysis of this framework leads to a new insight: the best strategies continue to refer to the input data rather than reducing the problem to smaller instances. Using this insight, we develop a new heuristic for inversion medians that uses input data to the end of its computation and leverages our previous work with DCJ medians. Finally, we present the results of extensive experimentation showing that our new heuristic outperforms all others in accuracy and, especially, in running time: the heuristic typically returns solutions within 1% of optimal and runs in seconds to minutes even on genomes with 25'000 genes--in contrast, MGR can take days on instances of 200 genes and cannot be used beyond 1'000 genes. Conclusion Finding good rearrangement medians, in particular inversion medians, had long been regarded as the computational bottleneck in whole-genome studies. Our new heuristic for inversion medians, ASM, which dominates all others in our framework, puts that issue to rest by providing near-optimal solutions within seconds to minutes on even the largest genomes. PMID:20122203
Finding Solutions to Sudoku Puzzles Using Human Intuitive Heuristics
Directory of Open Access Journals (Sweden)
Nelishia Pillay
2012-09-01
Full Text Available Sudoku is a logical puzzle that has achieved international popularity. Given this, there have been a number of computer solvers developed for this puzzle. Various methods including genetic algorithms, simulated annealing, particle swarm optimization and harmony search have been evaluated for this purpose. The approach described in this paper combines human intuition and optimization to solve Sudoku problems. The main contribution of this paper is a set of heuristic moves, incorporating human expertise, to solve Sudoku puzzles. The paper investigates the use of genetic programming to optimize a space of programs composed of these heuristics moves, with the aim of evolving a program that can produce a solution to the Sudoku problem instance. Each program is a combination of randomly selected moves. The approach was tested on 1800 Sudoku puzzles of differing difficulty. The approach presented was able to solve all 1800 problems, with a majority of these problems being solved in under a second. For a majority of the puzzles evolution was not needed and random combinations of the moves created during the initial population produced solutions. For the more difficult problems at least one generation of evolution was needed to find a solution. Further analysis revealed that solution programs for the more difficult problems could be found by enumerating random combinations of the move operators, however at a cost of higher runtimes. The performance of the approach presented was found to be comparable to other methods used to solve Sudoku problems and in a number of cases produced better results.
Fuel lattice design using heuristics and new strategies
Energy Technology Data Exchange (ETDEWEB)
Ortiz S, J. J.; Castillo M, J. A.; Torres V, M.; Perusquia del Cueto, R. [ININ, Carretera Mexico-Toluca s/n, Ocoyoacac 52750, Estado de Mexico (Mexico); Pelta, D. A. [ETS Ingenieria Informatica y Telecomunicaciones, Universidad de Granada, Daniel Saucedo Aranda s/n, 18071 Granada (Spain); Campos S, Y., E-mail: juanjose.ortiz@inin.gob.m [IPN, Escuela Superior de Fisica y Matematicas, Unidad Profesional Adolfo Lopez Mateos, Edif. 9, 07738 Mexico D. F. (Mexico)
2010-10-15
This work show some results of the fuel lattice design in BWRs when some allocation pin rod rules are not taking into account. Heuristics techniques like Path Re linking and Greedy to design fuel lattices were used. The scope of this work is to search about how do classical rules in design fuel lattices affect the heuristics techniques results and the fuel lattice quality. The fuel lattices quality is measured by Power Peaking Factor and Infinite Multiplication Factor at the beginning of the fuel lattice life. CASMO-4 code to calculate these parameters was used. The analyzed rules are the following: pin rods with lowest uranium enrichment are only allocated in the fuel lattice corner, and pin rods with gadolinium cannot allocated in the fuel lattice edge. Fuel lattices with and without gadolinium in the main diagonal were studied. Some fuel lattices were simulated in an equilibrium cycle fuel reload, using Simulate-3 to verify their performance. So, the effective multiplication factor and thermal limits can be verified. The obtained results show a good performance in some fuel lattices designed, even thought, the knowing rules were not implemented. A fuel lattice performance and fuel lattice design characteristics analysis was made. To the realized tests, a dell workstation was used, under Li nux platform. (Author)
Directory of Open Access Journals (Sweden)
Y. D. Shabalin
2016-03-01
Full Text Available A problem of aberrant behavior detection for network communicating computer is discussed. A novel approach based on dynamic response of computer is introduced. The computer is suggested as a multiple-input multiple-output (MIMO plant. To characterize dynamic response of the computer on incoming requests a correlation between input data rate and observed output response (outgoing data rate and performance metrics is used. To distinguish normal and aberrant behavior of the computer one-class neural network classifieris used. General idea of the algorithm is shortly described. Configuration of network testbed for experiments with real attacks and their detection is presented (the automated search for XSS and SQL injections. Real found-XSS and SQL injection attack software was used to model the intrusion scenario. It would be expectable that aberrant behavior of the server will reveal itself by some instantaneous correlation response which will be significantly different from any of normal ones. It is evident that correlation picture of attacks from different malware running, the site homepage overriding on the server (so called defacing, hardware and software failures will differ from correlation picture of normal functioning. Intrusion detection algorithm is investigated to estimate false positive and false negative rates in relation to algorithm parameters. The importance of correlation width value and threshold value selection was emphasized. False positive rate was estimated along the time series of experimental data. Some ideas about enhancement of the algorithm quality and robustness were mentioned.
Age Effects and Heuristics in Decision Making*
Besedeš, Tibor; Deck, Cary; Sarangi, Sudipta; Shor, Mikhael
2011-01-01
Using controlled experiments, we examine how individuals make choices when faced with multiple options. Choice tasks are designed to mimic the selection of health insurance, prescription drug, or retirement savings plans. In our experiment, available options can be objectively ranked allowing us to examine optimal decision making. First, the probability of a person selecting the optimal option declines as the number of options increases, with the decline being more pronounced for older subjects. Second, heuristics differ by age with older subjects relying more on suboptimal decision rules. In a heuristics validation experiment, older subjects make worse decisions than younger subjects. PMID:22544977
Age Effects and Heuristics in Decision Making.
Besedeš, Tibor; Deck, Cary; Sarangi, Sudipta; Shor, Mikhael
2012-05-01
Using controlled experiments, we examine how individuals make choices when faced with multiple options. Choice tasks are designed to mimic the selection of health insurance, prescription drug, or retirement savings plans. In our experiment, available options can be objectively ranked allowing us to examine optimal decision making. First, the probability of a person selecting the optimal option declines as the number of options increases, with the decline being more pronounced for older subjects. Second, heuristics differ by age with older subjects relying more on suboptimal decision rules. In a heuristics validation experiment, older subjects make worse decisions than younger subjects.
Heuristics for container loading of furniture
DEFF Research Database (Denmark)
Egeblad, Jens; Garavelli, Claudio; Lisi, Stefano
2010-01-01
. In the studied company, the problem arises hundreds of times daily during transport planning. Instances may contain more than one hundred different items with irregular shapes. To solve this complex problem we apply a set of heuristics successively that each solve one part of the problem. Large items...... are combined in specific structures to ensure proper protection of the items during transportation and to simplify the problem. The solutions generated by the heuristic has an average loading utilization of 91.3% for the most general instances with average running times around 100 seconds....
Heuristic Drift Elimination for Personnel Tracking Systems
Borenstein, Johann; Ojeda, Lauro
This paper pertains to the reduction of the effects of measurement errors in rate gyros used for tracking, recording, or monitoring the position of persons walking indoors. In such applications, bias drift and other gyro errors can degrade accuracy within minutes. To overcome this problem we developed the Heuristic Drift Elimination (HDE) method, that effectively corrects bias drift and other slow-changing errors. HDE works by making assumptions about walking in structured, indoor environments. The paper explains the heuristic assumptions and the HDE method, and shows experimental results. In typical applications, HDE maintains near-zero heading errors in walks of unlimited duration.
Dutra, Jayne E.; Smith, Lisa
2006-01-01
The goal of this plan is to briefly describe new technologies available to us in the arenas of information discovery and discuss the strategic value they have for the NASA enterprise with some considerations and suggestions for near term implementations using the NASA Engineering Network (NEN) as a delivery venue.
Decision Support for Route Search and Optimum Finding in Transport Networks under Uncertainty
Directory of Open Access Journals (Sweden)
G. Szücs
2015-02-01
Full Text Available The aim of this paper is to find solution for route planning in road network for a user, and to find the equilibrium in the path optimization problem, where the roads have uncertain attributes. The concept is based on the Dempster-Shafer theory and Dijkstra's algorithm, which help to model the uncertainty and to find the best route, respectively. Based on uncertain influencing factors an interval of travel time (so called cost interval of each road can be calculated. An algorithm has been outlined for determining the best route comparing the intervals and using decision rules depending on the user's attitude. Priorities can be defined among the rules, and the constructed rule based mechanism for users’ demands is great contribution of this paper. The first task is discussed in more general in this paper, i.e. instead of travel time a general cost is investigated for any kind of network. At the solution of the second task, where the goal is to find equilibrium in transport network at case of uncertain situation, the result of the first task is used. Simulation tool has been used to find the equilibrium, which gives only approximate solution, but this is sufficient and appropriate solution for large networks. Furthermore this is built in a decision support system, which is another contribution of this work. At the end of the paper the implementation of the theoretical concept is presented with a test bed of a town presenting effects of different uncertain influencing factors for the roads.
Directory of Open Access Journals (Sweden)
Maryam Ashouri
2017-07-01
Full Text Available Vehicle routing problem (VRP is a Nondeterministic Polynomial Hard combinatorial optimization problem to serve the consumers from central depots and returned back to the originated depots with given vehicles. Furthermore, two of the most important extensions of the VRPs are the open vehicle routing problem (OVRP and VRP with simultaneous pickup and delivery (VRPSPD. In OVRP, the vehicles have not return to the depot after last visit and in VRPSPD, customers require simultaneous delivery and pick-up service. The aim of this paper is to present a combined effective ant colony optimization (CEACO which includes sweep and several local search algorithms which is different with common ant colony optimization (ACO. An extensive numerical experiment is performed on benchmark problem instances addressed in the literature. The computational result shows that suggested CEACO approach not only presented a very satisfying scalability, but also was competitive with other meta-heuristic algorithms in the literature for solving VRP, OVRP and VRPSPD problems. Keywords: Meta-heuristic algorithms, Vehicle Routing Problem, Open Vehicle Routing Problem, Simultaneously Pickup and Delivery, Ant Colony Optimization.
Automated generation of constructive ordering heuristics for educational timetabling
Pillay, Nelishia; Özcan, Ender
2017-01-01
Construction heuristics play an important role in solving combinatorial optimization problems. These heuristics are usually used to create an initial solution to the problem which is improved using optimization techniques such as metaheuristics. For examination timetabling and university course timetabling problems essentially graph colouring heuristics have been used for this purpose. The process of deriving heuristics manually for educational timetabling is a time consuming task. Furthermor...
Heuristic control of the Utah/MIT dextrous robot hand
Bass, Andrew H., Jr.
1987-01-01
Basic hand grips and sensor interactions that a dextrous robot hand will need as part of the operation of an EVA Retriever are analyzed. What is to be done with a dextrous robot hand is examined along with how such a complex machine might be controlled. It was assumed throughout that an anthropomorphic robot hand should perform tasks just as a human would; i.e., the most efficient approach to developing control strategies for the hand would be to model actual hand actions and do the same tasks in the same ways. Therefore, basic hand grips that human hands perform, as well as hand grip action were analyzed. It was also important to examine what is termed sensor fusion. This is the integration of various disparate sensor feedback paths. These feedback paths can be spatially and temporally separated, as well as, of different sensor types. Neural networks are seen as a means of integrating these varied sensor inputs and types. Basic heuristics of hand actions and grips were developed. These heuristics offer promise of control dextrous robot hands in a more natural and efficient way.
A matheuristic for the liner shipping network design problem
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Desaulniers, Guy
2012-01-01
We present a matheuristic, an integer programming based heuristic, for the Liner Shipping Network Design Problem. The heuristic applies a greedy construction heuristic based on an interpretation of the liner shipping network design problem as a multiple quadratic knapsack problem. The construction...... heuristic is combined with an improvement heuristic with a neighborhood defined by the solution space of a mixed integer program. The mixed integer program optimizes the removal and insertion of several port calls on a liner shipping service. The objective function is based on evaluation functions...
[CYTED-RITMOS network: toward the search for solutions to promote mobile health in Latin America].
Saigí-Rubió, Francesc; Novillo-Ortiz, David; Piette, John D
2017-05-25
The area of mobile technologies applied to health (mHealth) is a growing worldwide trend that has generated enormous expectations for the mitigation of problems related to medical services delivery and public health stemming from a lack of resources and the limited number of specialists. The numerous opportunities offered by mobile technologies, together with their ease of use, have attracted the interest both of governments and universities. This is the case of the Ibero-American Mobile Technologies and Health Network (CYTED-RITMOS, Spanish acronym). As a result of the network's first year of activity, in October 2015 the RITMOS International Workshop was held in Barcelona to present the priority areas in Latin America where research, development, and innovation (R&D+i) projects on mobile health could be carried out and possible solutions found. The objective of this article is to present the potentialities and applicability of mHealth in the Region of the Americas.
Energy aware swarm optimization with intercluster search for wireless sensor network.
Thilagavathi, Shanmugasundaram; Geetha, Bhavani Gnanasambandan
2015-01-01
Wireless sensor networks (WSNs) are emerging as a low cost popular solution for many real-world challenges. The low cost ensures deployment of large sensor arrays to perform military and civilian tasks. Generally, WSNs are power constrained due to their unique deployment method which makes replacement of battery source difficult. Challenges in WSN include a well-organized communication platform for the network with negligible power utilization. In this work, an improved binary particle swarm optimization (PSO) algorithm with modified connected dominating set (CDS) based on residual energy is proposed for discovery of optimal number of clusters and cluster head (CH). Simulations show that the proposed BPSO-T and BPSO-EADS perform better than LEACH- and PSO-based system in terms of energy savings and QOS.
Approach to design neural cryptography: a generalized architecture and a heuristic rule.
Mu, Nankun; Liao, Xiaofeng; Huang, Tingwen
2013-06-01
Neural cryptography, a type of public key exchange protocol, is widely considered as an effective method for sharing a common secret key between two neural networks on public channels. How to design neural cryptography remains a great challenge. In this paper, in order to provide an approach to solve this challenge, a generalized network architecture and a significant heuristic rule are designed. The proposed generic framework is named as tree state classification machine (TSCM), which extends and unifies the existing structures, i.e., tree parity machine (TPM) and tree committee machine (TCM). Furthermore, we carefully study and find that the heuristic rule can improve the security of TSCM-based neural cryptography. Therefore, TSCM and the heuristic rule can guide us to designing a great deal of effective neural cryptography candidates, in which it is possible to achieve the more secure instances. Significantly, in the light of TSCM and the heuristic rule, we further expound that our designed neural cryptography outperforms TPM (the most secure model at present) on security. Finally, a series of numerical simulation experiments are provided to verify validity and applicability of our results.
Search for Single Top Quark Production at the D0 Experiment using Bayesian Neural Networks
Tanasijczuk, Andres J
2009-01-01
We present the methodology used to measure the single top quark production cross section in the D0 experiment, and show as an example the results that led to the first evidence of single top quark production in D0 at the Fermilab Tevatron proton-antiproton collider. The selected events are mostly backgrounds, which we separate from the expected signals using three multivariate analysis techniques, one of them being Bayesian neural networks, which we will describe here.
Thirty years of European Collaboration in R&D: a Search for Key Network Indicators
Energy Technology Data Exchange (ETDEWEB)
Caloghirou, Y.; Protogerou, A.; Siokas, E.
2016-07-01
The purpose of this session is to present a coherent set of papers offering useful insights on research priority setting processes/activities and indicators used to measure the impact of research and technology development programmes in Europe and Emerging Economies (Brazil, Chile, Peru and Russia). In particular, the first paper focuses on the research collaborative networks funded by the European Union during the past three decades and offers a comprehensive picture of science-industry collaboration in Europe by using network indicators and providing data on the characteristics and the innovative performance of young firms participating in these networks. The second paper presents three cases of non-traditional indicators for R&D funding agencies from emerging economies and aims at contributing to the discussions on the importance of employing suitable indicators that can complement classic STI indicators. The third paper seeks to provide a critical overview of the recent exercise in the evaluation of public research institutions in Russia. The session aims at bringing together researchers from both developed and emerging countries as well as policy makers and will be divided into two parts . The first part will be devoted in papers’ presentation and the second one in papers’ discussion by invited policy experts and officials. (Author)
A rescheduling heuristic for the single machine total tardiness problem
African Journals Online (AJOL)
In this paper, we propose a rescheduling heuristic for scheduling N jobs on a single machine in order to minimise total tardiness. The heuristic is of the interchange type and constructs a schedule from the modified due date (MDD) schedule. Unlike most interchange heuristics that consider interchanges involving only two ...
Heuristic Diagrams as a Tool to Teach History of Science
Chamizo, Jose A.
2012-01-01
The graphic organizer called here heuristic diagram as an improvement of Gowin's Vee heuristic is proposed as a tool to teach history of science. Heuristic diagrams have the purpose of helping students (or teachers, or researchers) to understand their own research considering that asks and problem-solving are central to scientific activity. The…
Heuristics Made Easy: An Effort-Reduction Framework
Shah, Anuj K.; Oppenheimer, Daniel M.
2008-01-01
In this article, the authors propose a new framework for understanding and studying heuristics. The authors posit that heuristics primarily serve the purpose of reducing the effort associated with a task. As such, the authors propose that heuristics can be classified according to a small set of effort-reduction principles. The authors use this…
Usage of Major Heuristics in Property Investment Valuation in Nigeria
African Journals Online (AJOL)
Toshiba
effect of heuristics, but concluded that experience and feedback should mitigate much bias. Tversky and Kahnemann (1974) identified three types of heuristics: representative; availability and anchoring and adjustment. Evans. (1989) later added a fourth: positivity (other lesser heuristics have subsequently been identified).
Heuristic Optimization for the Discrete Virtual Power Plant Dispatch Problem
DEFF Research Database (Denmark)
Petersen, Mette Kirschmeyer; Hansen, Lars Henrik; Bendtsen, Jan Dimon
2014-01-01
of the optimal algorithms we have tested. In particular, GRASP Sorted shows the most promising performance, as it is able to find solutions that are both agile (sorted) and well balanced, and consistently yields the best numerical performance among the developed algorithms....... Problem. First NP-completeness of the Discrete Virtual Power Plant Dispatch Problem is proved formally. We then proceed to develop tailored versions of the meta-heuristic algorithms Hill Climber and Greedy Randomized Adaptive Search Procedure (GRASP). The algorithms are tuned and tested on portfolios...... of varying sizes. We find that all the tailored algorithms perform satisfactorily in the sense that they are able to find sub-optimal, but usable, solutions to very large problems (on the order of 10 5 units) at computation times on the scale of just 10 seconds, which is far beyond the capabilities...
Routing Post-Disaster Traffic Floods Heuristics
Nasralla, ZHA; Musa, MOI; El-Gorashi, TEH; Elmirghani, JMH
2016-01-01
In this paper, we present three heuristics for mitigating post-disaster traffic floods. First exploiting the excess capacity, second rerouting backup paths, finally redistributing the whole traffic by rerouting the working and protection paths to accommodate more floods. Using these mitigation approaches can reduce the blocking by up to 30%.
Heuristic estimates in shortest path algorithms
W.H.L.M. Pijls (Wim)
2007-01-01
textabstractShortest path problems occupy an important position in operations research as well as in artificial intelligence. In this paper we study shortest path algorithms that exploit heuristic estimates. The well-known algorithms are put into one framework. Besides, we present an interesting
Heuristic estimates in shortest path algorithms
W.H.L.M. Pijls (Wim)
2006-01-01
textabstractShortest path problems occupy an important position in Operations Research as well as in Arti¯cial Intelligence. In this paper we study shortest path algorithms that exploit heuristic estimates. The well-known algorithms are put into one framework. Besides we present an interesting
Sensitivity Analysis of List Scheduling Heuristics
A.W.J. Kolen; A.H.G. Rinnooy Kan (Alexander); C.P.M. van Hoesel; A.P.M. Wagelmans (Albert)
1994-01-01
textabstractWhen jobs have to be processed on a set of identical parallel machines so as to minimize the makespan of the schedule, list scheduling rules form a popular class of heuristics. The order in which jobs appear on the list is assumed here to be determined by the relative size of their
The Heuristic Interpretation of Box Plots
Lem, Stephanie; Onghena, Patrick; Verschaffel, Lieven; Van Dooren, Wim
2013-01-01
Box plots are frequently used, but are often misinterpreted by students. Especially the area of the box in box plots is often misinterpreted as representing number or proportion of observations, while it actually represents their density. In a first study, reaction time evidence was used to test whether heuristic reasoning underlies this…
Heuristics for speeding up gaze estimation
DEFF Research Database (Denmark)
Leimberg, Denis; Vester-Christensen, Martin; Ersbøll, Bjarne Kjær
2005-01-01
A deformable template method for eye tracking on full face images is presented. The strengths of the method are that it is fast and retains accuracy independently of the resolution. We compare the method with a state of the art active contour approach, showing that the heuristic method is more...
Heuristic framework for parallel sorting computations | Nwanze ...
African Journals Online (AJOL)
The decreasing cost of these processors will probably in the future, make the solutions that are derived thereof to be more appealing. Efficient algorithms for sorting scheme that are encountered in a number of operations are considered for multi-user machines. A heuristic framework for exploiting parallelism inherent in ...
Heuristics for Knowledge Acquisition from Maps.
Thorndyke, Perry W.
This paper investigates how people acquire knowledge from maps. Emphasis is placed on heuristics--defined as the procedures that people use to select, combine, and encode map information in memory. The objective is to develop a theory of expertise in map learning by analyzing differences between fast and slow learners in terms of differences in…
Directory of Open Access Journals (Sweden)
Ahmed R. Abdelaziz
2015-08-01
Full Text Available This paper presents an application of Chaotic differential evolution optimization approach meta-heuristics in solving transmission network expansion planning TNEP using an AC model associated with reactive power planning RPP. The reliabilityredundancy of network analysis optimization problems implicate selection of components with multiple choices and redundancy levels that produce maximum benefits can be subject to the cost weight and volume constraints is presented in this paper. Classical mathematical methods have failed in handling non-convexities and non-smoothness in optimization problems. As an alternative to the classical optimization approaches the meta-heuristics have attracted lot of attention due to their ability to find an almost global optimal solution in reliabilityredundancy optimization problems. Evolutionary algorithms EAs paradigms of evolutionary computation field are stochastic and robust meta-heuristics useful to solve reliabilityredundancy optimization problems. EAs such as genetic algorithm evolutionary programming evolution strategies and differential evolution are being used to find global or near global optimal solution. The Differential Evolution Algorithm DEA population-based algorithm is an optimal algorithm with powerful global searching capability but it is usually in low convergence speed and presents bad searching capability in the later evolution stage. A new Chaotic Differential Evolution algorithm CDE based on the cat map is recommended which combines DE and chaotic searching algorithm. Simulation results and comparisons show that the chaotic differential evolution algorithm using Cat map is competitive and stable in performance with other optimization approaches and other maps.
Energy Technology Data Exchange (ETDEWEB)
Conte, Viviane Cristhyne Bini; Arruda, Lucia Valeria Ramos de; Yamamoto, Lia [Universidade Tecnologica Federal do Parana (UTFPR), Curitiba, PR (Brazil)
2008-07-01
Planning and scheduling of the pipeline network operations aim the most efficient use of the resources resulting in a better performance of the network. A petroleum distribution pipeline network is composed by refineries, sources and/or storage parks, connected by a set of pipelines, which operate the transportation of petroleum and derivatives among adjacent areas. In real scenes, this problem is considered a combinatorial problem, which has difficult solution, which makes necessary methodologies of the resolution that present low computational time. This work aims to get solutions that attempt the demands and minimize the number of batch fragmentations on the sent operations of products for the pipelines in a simplified model of a real network, through by application of the local search metaheuristic GRASP. GRASP does not depend of solutions of previous iterations and works in a random way so it allows the search for the solution in an ampler and diversified search space. GRASP utilization does not demand complex calculation, even the construction stage that requires more computational effort, which provides relative rapidity in the attainment of good solutions. GRASP application on the scheduling of the operations of this network presented feasible solutions in a low computational time. (author)
Black-Box Search by Unbiased Variation
DEFF Research Database (Denmark)
Lehre, Per Kristian; Witt, Carsten
2012-01-01
The complexity theory for black-box algorithms, introduced by Droste, Jansen, and Wegener (Theory Comput. Syst. 39:525–544, 2006), describes common limits on the efficiency of a broad class of randomised search heuristics. There is an obvious trade-off between the generality of the black-box model...... and the strength of the bounds that can be proven in such a model. In particular, the original black-box model provides for well-known benchmark problems relatively small lower bounds, which seem unrealistic in certain cases and are typically not met by popular search heuristics.In this paper, we introduce a more...... restricted black-box model for optimisation of pseudo-Boolean functions which we claim captures the working principles of many randomised search heuristics including simulated annealing, evolutionary algorithms, randomised local search, and others. The key concept worked out is an unbiased variation operator...
High-Quality Ultra-Compact Grid Layout of Grouped Networks.
Yoghourdjian, Vahan; Dwyer, Tim; Gange, Graeme; Kieffer, Steve; Klein, Karsten; Marriott, Kim
2016-01-01
Prior research into network layout has focused on fast heuristic techniques for layout of large networks, or complex multi-stage pipelines for higher quality layout of small graphs. Improvements to these pipeline techniques, especially for orthogonal-style layout, are difficult and practical results have been slight in recent years. Yet, as discussed in this paper, there remain significant issues in the quality of the layouts produced by these techniques, even for quite small networks. This is especially true when layout with additional grouping constraints is required. The first contribution of this paper is to investigate an ultra-compact, grid-like network layout aesthetic that is motivated by the grid arrangements that are used almost universally by designers in typographical layout. Since the time when these heuristic and pipeline-based graph-layout methods were conceived, generic technologies (MIP, CP and SAT) for solving combinatorial and mixed-integer optimization problems have improved massively. The second contribution of this paper is to reassess whether these techniques can be used for high-quality layout of small graphs. While they are fast enough for graphs of up to 50 nodes we found these methods do not scale up. Our third contribution is a large-neighborhood search meta-heuristic approach that is scalable to larger networks.
Chatrchyan, Serguei; Sirunyan, Albert M; Tumasyan, Armen; Adam, Wolfgang; Aguilo, Ernest; Bergauer, Thomas; Dragicevic, Marko; Erö, Janos; Fabjan, Christian; Friedl, Markus; Fruehwirth, Rudolf; Ghete, Vasile Mihai; Hörmann, Natascha; Hrubec, Josef; Jeitler, Manfred; Kiesenhofer, Wolfgang; Knünz, Valentin; Krammer, Manfred; Krätschmer, Ilse; Liko, Dietrich; Mikulec, Ivan; Pernicka, Manfred; Rabady, Dinyar; Rahbaran, Babak; Rohringer, Christine; Rohringer, Herbert; Schöfbeck, Robert; Strauss, Josef; Taurok, Anton; Waltenberger, Wolfgang; Wulz, Claudia-Elisabeth; Mossolov, Vladimir; Shumeiko, Nikolai; Suarez Gonzalez, Juan; Bansal, Monika; Bansal, Sunil; Cornelis, Tom; De Wolf, Eddi A; Janssen, Xavier; Luyckx, Sten; Mucibello, Luca; Ochesanu, Silvia; Roland, Benoit; Rougny, Romain; Selvaggi, Michele; Van Haevermaet, Hans; Van Mechelen, Pierre; Van Remortel, Nick; Van Spilbeeck, Alex; Blekman, Freya; Blyweert, Stijn; D'Hondt, Jorgen; Gonzalez Suarez, Rebeca; Kalogeropoulos, Alexis; Maes, Michael; Olbrechts, Annik; Van Doninck, Walter; Van Mulders, Petra; Van Onsem, Gerrit Patrick; Villella, Ilaria; Clerbaux, Barbara; De Lentdecker, Gilles; Dero, Vincent; Gay, Arnaud; Hreus, Tomas; Léonard, Alexandre; Marage, Pierre Edouard; Mohammadi, Abdollah; Reis, Thomas; Thomas, Laurent; Vander Velde, Catherine; Vanlaer, Pascal; Wang, Jian; Adler, Volker; Beernaert, Kelly; Cimmino, Anna; Costantini, Silvia; Garcia, Guillaume; Grunewald, Martin; Klein, Benjamin; Lellouch, Jérémie; Marinov, Andrey; Mccartin, Joseph; Ocampo Rios, Alberto Andres; Ryckbosch, Dirk; Strobbe, Nadja; Thyssen, Filip; Tytgat, Michael; Walsh, Sinead; Yazgan, Efe; Zaganidis, Nicolas; Basegmez, Suzan; Bruno, Giacomo; Castello, Roberto; Ceard, Ludivine; Delaere, Christophe; Du Pree, Tristan; Favart, Denis; Forthomme, Laurent; Giammanco, Andrea; Hollar, Jonathan; Lemaitre, Vincent; Liao, Junhui; Militaru, Otilia; Nuttens, Claude; Pagano, Davide; Pin, Arnaud; Piotrzkowski, Krzysztof; Vizan Garcia, Jesus Manuel; Beliy, Nikita; Caebergs, Thierry; Daubie, Evelyne; Hammad, Gregory Habib; Alves, Gilvan; Correa Martins Junior, Marcos; Martins, Thiago; Pol, Maria Elena; Henrique Gomes E Souza, Moacyr; Aldá Júnior, Walter Luiz; Carvalho, Wagner; Custódio, Analu; Melo Da Costa, Eliza; De Jesus Damiao, Dilson; De Oliveira Martins, Carley; Fonseca De Souza, Sandro; Malbouisson, Helena; Malek, Magdalena; Matos Figueiredo, Diego; Mundim, Luiz; Nogima, Helio; Prado Da Silva, Wanda Lucia; Santoro, Alberto; Soares Jorge, Luana; Sznajder, Andre; Vilela Pereira, Antonio; Souza Dos Anjos, Tiago; Bernardes, Cesar Augusto; De Almeida Dias, Flavia; Tomei, Thiago; De Moraes Gregores, Eduardo; Lagana, Caio; Da Cunha Marinho, Franciole; Mercadante, Pedro G; Novaes, Sergio F; Padula, Sandra; Genchev, Vladimir; Iaydjiev, Plamen; Piperov, Stefan; Rodozov, Mircho; Stoykova, Stefka; Sultanov, Georgi; Tcholakov, Vanio; Trayanov, Rumen; Vutova, Mariana; Dimitrov, Anton; Hadjiiska, Roumyana; Kozhuharov, Venelin; Litov, Leander; Pavlov, Borislav; Petkov, Peicho; Bian, Jian-Guo; Chen, Guo-Ming; Chen, He-Sheng; Jiang, Chun-Hua; Liang, Dong; Liang, Song; Meng, Xiangwei; Tao, Junquan; Wang, Jian; Wang, Xianyou; Wang, Zheng; Xiao, Hong; Xu, Ming; Zang, Jingjing; Zhang, Zhen; Asawatangtrakuldee, Chayanit; Ban, Yong; Guo, Yifei; Li, Wenbo; Liu, Shuai; Mao, Yajun; Qian, Si-Jin; Teng, Haiyun; Wang, Dayong; Zhang, Linlin; Zou, Wei; Avila, Carlos; Gomez, Juan Pablo; Gomez Moreno, Bernardo; Osorio Oliveros, Andres Felipe; Sanabria, Juan Carlos; Godinovic, Nikola; Lelas, Damir; Plestina, Roko; Polic, Dunja; Puljak, Ivica; Antunovic, Zeljko; Kovac, Marko; Brigljevic, Vuko; Duric, Senka; Kadija, Kreso; Luetic, Jelena; Mekterovic, Darko; Morovic, Srecko; Attikis, Alexandros; Galanti, Mario; Mavromanolakis, Georgios; Mousa, Jehad; Nicolaou, Charalambos; Ptochos, Fotios; Razis, Panos A; Finger, Miroslav; Finger Jr, Michael; Assran, Yasser; Elgammal, Sherif; Ellithi Kamel, Ali; Mahmoud, Mohammed; Mahrous, Ayman; Radi, Amr; Kadastik, Mario; Müntel, Mait; Murumaa, Marion; Raidal, Martti; Rebane, Liis; Tiko, Andres; Eerola, Paula; Fedi, Giacomo; Voutilainen, Mikko; Härkönen, Jaakko; Heikkinen, Mika Aatos; Karimäki, Veikko; Kinnunen, Ritva; Kortelainen, Matti J; Lampén, Tapio; Lassila-Perini, Kati; Lehti, Sami; Lindén, Tomas; Luukka, Panja-Riina; Mäenpää, Teppo; Peltola, Timo; Tuominen, Eija; Tuominiemi, Jorma; Tuovinen, Esa; Ungaro, Donatella; Wendland, Lauri; Banzuzi, Kukka; Karjalainen, Ahti; Korpela, Arja; Tuuva, Tuure; Besancon, Marc; Choudhury, Somnath; Dejardin, Marc; Denegri, Daniel; Fabbro, Bernard; Faure, Jean-Louis; Ferri, Federico; Ganjour, Serguei; Givernaud, Alain; Gras, Philippe; Hamel de Monchenault, Gautier; Jarry, Patrick; Locci, Elizabeth; Malcles, Julie; Millischer, Laurent; Nayak, Aruna; Rander, John; Rosowsky, André; Titov, Maksym; Baffioni, Stephanie; Beaudette, Florian; Benhabib, Lamia; Bianchini, Lorenzo; Bluj, Michal; Busson, Philippe; Charlot, Claude; Daci, Nadir; Dahms, Torsten; Dalchenko, Mykhailo; Dobrzynski, Ludwik; Florent, Alice; Granier de Cassagnac, Raphael; Haguenauer, Maurice; Miné, Philippe; Mironov, Camelia; Naranjo, Ivo Nicolas; Nguyen, Matthew; Ochando, Christophe; Paganini, Pascal; Sabes, David; Salerno, Roberto; Sirois, Yves; Veelken, Christian; Zabi, Alexandre; Agram, Jean-Laurent; Andrea, Jeremy; Bloch, Daniel; Bodin, David; Brom, Jean-Marie; Cardaci, Marco; Chabert, Eric Christian; Collard, Caroline; Conte, Eric; Drouhin, Frédéric; Fontaine, Jean-Charles; Gelé, Denis; Goerlach, Ulrich; Juillot, Pierre; Le Bihan, Anne-Catherine; Van Hove, Pierre; Fassi, Farida; Mercier, Damien; Beauceron, Stephanie; Beaupere, Nicolas; Bondu, Olivier; Boudoul, Gaelle; Brochet, Sébastien; Chasserat, Julien; Chierici, Roberto; Contardo, Didier; Depasse, Pierre; El Mamouni, Houmani; Fay, Jean; Gascon, Susan; Gouzevitch, Maxime; Ille, Bernard; Kurca, Tibor; Lethuillier, Morgan; Mirabito, Laurent; Perries, Stephane; Sgandurra, Louis; Sordini, Viola; Tschudi, Yohann; Verdier, Patrice; Viret, Sébastien; Tsamalaidze, Zviad; Autermann, Christian; Beranek, Sarah; Calpas, Betty; Edelhoff, Matthias; Feld, Lutz; Heracleous, Natalie; Hindrichs, Otto; Jussen, Ruediger; Klein, Katja; Merz, Jennifer; Ostapchuk, Andrey; Perieanu, Adrian; Raupach, Frank; Sammet, Jan; Schael, Stefan; Sprenger, Daniel; Weber, Hendrik; Wittmer, Bruno; Zhukov, Valery; Ata, Metin; Caudron, Julien; Dietz-Laursonn, Erik; Duchardt, Deborah; Erdmann, Martin; Fischer, Robert; Güth, Andreas; Hebbeker, Thomas; Heidemann, Carsten; Hoepfner, Kerstin; Klingebiel, Dennis; Kreuzer, Peter; Merschmeyer, Markus; Meyer, Arnd; Olschewski, Mark; Papacz, Paul; Pieta, Holger; Reithler, Hans; Schmitz, Stefan Antonius; Sonnenschein, Lars; Steggemann, Jan; Teyssier, Daniel; Thüer, Sebastian; Weber, Martin; Bontenackels, Michael; Cherepanov, Vladimir; Erdogan, Yusuf; Flügge, Günter; Geenen, Heiko; Geisler, Matthias; Haj Ahmad, Wael; Hoehle, Felix; Kargoll, Bastian; Kress, Thomas; Kuessel, Yvonne; Lingemann, Joschka; Nowack, Andreas; Perchalla, Lars; Pooth, Oliver; Sauerland, Philip; Stahl, Achim; Aldaya Martin, Maria; Behr, Joerg; Behrenhoff, Wolf; Behrens, Ulf; Bergholz, Matthias; Bethani, Agni; Borras, Kerstin; Burgmeier, Armin; Cakir, Altan; Calligaris, Luigi; Campbell, Alan; Castro, Elena; Costanza, Francesco; Dammann, Dirk; Diez Pardos, Carmen; Eckerlin, Guenter; Eckstein, Doris; Flucke, Gero; Geiser, Achim; Glushkov, Ivan; Gunnellini, Paolo; Habib, Shiraz; Hauk, Johannes; Hellwig, Gregor; Jung, Hannes; Kasemann, Matthias; Katsas, Panagiotis; Kleinwort, Claus; Kluge, Hannelies; Knutsson, Albert; Krämer, Mira; Krücker, Dirk; Kuznetsova, Ekaterina; Lange, Wolfgang; Leonard, Jessica; Lohmann, Wolfgang; Lutz, Benjamin; Mankel, Rainer; Marfin, Ihar; Marienfeld, Markus; Melzer-Pellmann, Isabell-Alissandra; Meyer, Andreas Bernhard; Mnich, Joachim; Mussgiller, Andreas; Naumann-Emme, Sebastian; Novgorodova, Olga; Olzem, Jan; Perrey, Hanno; Petrukhin, Alexey; Pitzl, Daniel; Raspereza, Alexei; Ribeiro Cipriano, Pedro M; Riedl, Caroline; Ron, Elias; Rosin, Michele; Salfeld-Nebgen, Jakob; Schmidt, Ringo; Schoerner-Sadenius, Thomas; Sen, Niladri; Spiridonov, Alexander; Stein, Matthias; Walsh, Roberval; Wissing, Christoph; Blobel, Volker; Enderle, Holger; Erfle, Joachim; Gebbert, Ulla; Görner, Martin; Gosselink, Martijn; Haller, Johannes; Hermanns, Thomas; Höing, Rebekka Sophie; Kaschube, Kolja; Kaussen, Gordon; Kirschenmann, Henning; Klanner, Robert; Lange, Jörn; Nowak, Friederike; Peiffer, Thomas; Pietsch, Niklas; Rathjens, Denis; Sander, Christian; Schettler, Hannes; Schleper, Peter; Schlieckau, Eike; Schmidt, Alexander; Schröder, Matthias; Schum, Torben; Seidel, Markus; Sibille, Jennifer; Sola, Valentina; Stadie, Hartmut; Steinbrück, Georg; Thomsen, Jan; Vanelderen, Lukas; Barth, Christian; Berger, Joram; Böser, Christian; Chwalek, Thorsten; De Boer, Wim; Descroix, Alexis; Dierlamm, Alexander; Feindt, Michael; Guthoff, Moritz; Hackstein, Christoph; Hartmann, Frank; Hauth, Thomas; Heinrich, Michael; Held, Hauke; Hoffmann, Karl-Heinz; Husemann, Ulrich; Katkov, Igor; Komaragiri, Jyothsna Rani; Lobelle Pardo, Patricia; Martschei, Daniel; Mueller, Steffen; Müller, Thomas; Niegel, Martin; Nürnberg, Andreas; Oberst, Oliver; Oehler, Andreas; Ott, Jochen; Quast, Gunter; Rabbertz, Klaus; Ratnikov, Fedor; Ratnikova, Natalia; Röcker, Steffen; Schilling, Frank-Peter; Schott, Gregory; Simonis, Hans-Jürgen; Stober, Fred-Markus Helmut; Troendle, Daniel; Ulrich, Ralf; Wagner-Kuhr, Jeannine; Wayand, Stefan; Weiler, Thomas; Zeise, Manuel; Anagnostou, Georgios; Daskalakis, Georgios; Geralis, Theodoros; Kesisoglou, Stilianos; Kyriakis, Aristotelis; Loukas, Demetrios; Manolakos, Ioannis; Markou, Athanasios; Markou, Christos; Ntomari, Eleni; Gouskos, Loukas; Mertzimekis, Theodoros; Panagiotou, Apostolos; Saoulidou, Niki; Evangelou, Ioannis; Foudas, Costas; Kokkas, Panagiotis; Manthos, Nikolaos; Papadopoulos, Ioannis; Patras, Vaios; Bencze, Gyorgy; Hajdu, Csaba; Hidas, Pàl; Horvath, Dezso; Sikler, Ferenc; Veszpremi, Viktor; Vesztergombi, Gyorgy; Beni, Noemi; Czellar, Sandor; Molnar, Jozsef; Palinkas, Jozsef; Szillasi, Zoltan; Karancsi, János; Raics, Peter; Trocsanyi, Zoltan Laszlo; Ujvari, Balazs; Beri, Suman Bala; Bhatnagar, Vipin; Dhingra, Nitish; Gupta, Ruchi; Kaur, Manjit; Mehta, Manuk Zubin; Nishu, Nishu; Saini, Lovedeep Kaur; Sharma, Archana; Singh, Jasbir; Kumar, Ashok; Kumar, Arun; Ahuja, Sudha; Bhardwaj, Ashutosh; Choudhary, Brajesh C; Malhotra, Shivali; Naimuddin, Md; Ranjan, Kirti; Sharma, Varun; Shivpuri, Ram Krishen; Banerjee, Sunanda; Bhattacharya, Satyaki; Dutta, Suchandra; Gomber, Bhawna; Jain, Sandhya; Jain, Shilpi; Khurana, Raman; Sarkar, Subir; Sharan, Manoj; Abdulsalam, Abdulla; Dutta, Dipanwita; Kailas, Swaminathan; Kumar, Vineet; Mohanty, Ajit Kumar; Pant, Lalit Mohan; Shukla, Prashant; Aziz, Tariq; Ganguly, Sanmay; Guchait, Monoranjan; Gurtu, Atul; Maity, Manas; Majumder, Gobinda; Mazumdar, Kajari; Mohanty, Gagan Bihari; Parida, Bibhuti; Sudhakar, Katta; Wickramage, Nadeesha; Banerjee, Sudeshna; Dugad, Shashikant; Arfaei, Hessamaddin; Bakhshiansohi, Hamed; Etesami, Seyed Mohsen; Fahim, Ali; Hashemi, Majid; Hesari, Hoda; Jafari, Abideh; Khakzad, Mohsen; Mohammadi Najafabadi, Mojtaba; Paktinat Mehdiabadi, Saeid; Safarzadeh, Batool; Zeinali, Maryam; Abbrescia, Marcello; Barbone, Lucia; Calabria, Cesare; Chhibra, Simranjit Singh; Colaleo, Anna; Creanza, Donato; De Filippis, Nicola; De Palma, Mauro; Fiore, Luigi; Iaselli, Giuseppe; Maggi, Giorgio; Maggi, Marcello; Marangelli, Bartolomeo; My, Salvatore; Nuzzo, Salvatore; Pacifico, Nicola; Pompili, Alexis; Pugliese, Gabriella; Selvaggi, Giovanna; Silvestris, Lucia; Singh, Gurpreet; Venditti, Rosamaria; Verwilligen, Piet; Zito, Giuseppe; Abbiendi, Giovanni; Benvenuti, Alberto; Bonacorsi, Daniele; Braibant-Giacomelli, Sylvie; Brigliadori, Luca; Capiluppi, Paolo; Castro, Andrea; Cavallo, Francesca Romana; Cuffiani, Marco; Dallavalle, Gaetano-Marco; Fabbri, Fabrizio; Fanfani, Alessandra; Fasanella, Daniele; Giacomelli, Paolo; Grandi, Claudio; Guiducci, Luigi; Marcellini, Stefano; Masetti, Gianni; Meneghelli, Marco; Montanari, Alessandro; Navarria, Francesco; Odorici, Fabrizio; Perrotta, Andrea; Primavera, Federica; Rossi, Antonio; Rovelli, Tiziano; Siroli, Gian Piero; Tosi, Nicolò; Travaglini, Riccardo; Albergo, Sebastiano; Cappello, Gigi; Chiorboli, Massimiliano; Costa, Salvatore; Potenza, Renato; Tricomi, Alessia; Tuve, Cristina; Barbagli, Giuseppe; Ciulli, Vitaliano; Civinini, Carlo; D'Alessandro, Raffaello; Focardi, Ettore; Frosali, Simone; Gallo, Elisabetta; Gonzi, Sandro; Meschini, Marco; Paoletti, Simone; Sguazzoni, Giacomo; Tropiano, Antonio; Benussi, Luigi; Bianco, Stefano; Colafranceschi, Stefano; Fabbri, Franco; Piccolo, Davide; Fabbricatore, Pasquale; Musenich, Riccardo; Tosi, Silvano; Benaglia, Andrea; De Guio, Federico; Di Matteo, Leonardo; Fiorendi, Sara; Gennai, Simone; Ghezzi, Alessio; Malvezzi, Sandra; Manzoni, Riccardo Andrea; Martelli, Arabella; Massironi, Andrea; Menasce, Dario; Moroni, Luigi; Paganoni, Marco; Pedrini, Daniele; Ragazzi, Stefano; Redaelli, Nicola; Sala, Silvano; Tabarelli de Fatis, Tommaso; Buontempo, Salvatore; Carrillo Montoya, Camilo Andres; Cavallo, Nicola; De Cosa, Annapaola; Dogangun, Oktay; Fabozzi, Francesco; Iorio, Alberto Orso Maria; Lista, Luca; Meola, Sabino; Merola, Mario; Paolucci, Pierluigi; Azzi, Patrizia; Bacchetta, Nicola; Bisello, Dario; Branca, Antonio; Carlin, Roberto; Checchia, Paolo; Dorigo, Tommaso; Gasparini, Fabrizio; Gasparini, Ugo; Gozzelino, Andrea; Kanishchev, Konstantin; Lacaprara, Stefano; Lazzizzera, Ignazio; Margoni, Martino; Meneguzzo, Anna Teresa; Pazzini, Jacopo; Pozzobon, Nicola; Ronchese, Paolo; Simonetto, Franco; Torassa, Ezio; Tosi, Mia; Vanini, Sara; Zotto, Pierluigi; Zucchetta, Alberto; Zumerle, Gianni; Gabusi, Michele; Ratti, Sergio P; Riccardi, Cristina; Torre, Paola; Vitulo, Paolo; Biasini, Maurizio; Bilei, Gian Mario; Fanò, Livio; Lariccia, Paolo; Mantovani, Giancarlo; Menichelli, Mauro; Nappi, Aniello; Romeo, Francesco; Saha, Anirban; Santocchia, Attilio; Spiezia, Aniello; Taroni, Silvia; Azzurri, Paolo; Bagliesi, Giuseppe; Bernardini, Jacopo; Boccali, Tommaso; Broccolo, Giuseppe; Castaldi, Rino; D'Agnolo, Raffaele Tito; Dell'Orso, Roberto; Fiori, Francesco; Foà, Lorenzo; Giassi, Alessandro; Kraan, Aafke; Ligabue, Franco; Lomtadze, Teimuraz; Martini, Luca; Messineo, Alberto; Palla, Fabrizio; Rizzi, Andrea; Serban, Alin Titus; Spagnolo, Paolo; Squillacioti, Paola; Tenchini, Roberto; Tonelli, Guido; Venturi, Andrea; Verdini, Piero Giorgio; Barone, Luciano; Cavallari, Francesca; Del Re, Daniele; Diemoz, Marcella; Fanelli, Cristiano; Grassi, Marco; Longo, Egidio; Meridiani, Paolo; Micheli, Francesco; Nourbakhsh, Shervin; Organtini, Giovanni; Paramatti, Riccardo; Rahatlou, Shahram; Sigamani, Michael; Soffi, Livia; Amapane, Nicola; Arcidiacono, Roberta; Argiro, Stefano; Arneodo, Michele; Biino, Cristina; Cartiglia, Nicolo; Casasso, Stefano; Costa, Marco; Demaria, Natale; Mariotti, Chiara; Maselli, Silvia; Migliore, Ernesto; Monaco, Vincenzo; Musich, Marco; Obertino, Maria Margherita; Pastrone, Nadia; Pelliccioni, Mario; Potenza, Alberto; Romero, Alessandra; Ruspa, Marta; Sacchi, Roberto; Solano, Ada; Staiano, Amedeo; Belforte, Stefano; Candelise, Vieri; Casarsa, Massimo; Cossutti, Fabio; Della Ricca, Giuseppe; Gobbo, Benigno; Marone, Matteo; Montanino, Damiana; Penzo, Aldo; Schizzi, Andrea; Kim, Tae Yeon; Nam, Soon-Kwon; Chang, Sunghyun; Kim, Dong Hee; Kim, Gui Nyun; Kong, Dae Jung; Park, Hyangkyu; Son, Dong-Chul; Son, Taejin; Kim, Jae Yool; Kim, Zero Jaeho; Song, Sanghyeon; Choi, Suyong; Gyun, Dooyeon; Hong, Byung-Sik; Jo, Mihee; Kim, Hyunchul; Kim, Tae Jeong; Lee, Kyong Sei; Moon, Dong Ho; Park, Sung Keun; Roh, Youn; Choi, Minkyoo; Kim, Ji Hyun; Park, Chawon; Park, Inkyu; Park, Sangnam; Ryu, Geonmo; Choi, Young-Il; Choi, Young Kyu; Goh, Junghwan; Kim, Min Suk; Kwon, Eunhyang; Lee, Byounghoon; Lee, Jongseok; Lee, Sungeun; Seo, Hyunkwan; Yu, Intae; Bilinskas, Mykolas Jurgis; Grigelionis, Ignas; Janulis, Mindaugas; Juodagalvis, Andrius; Castilla-Valdez, Heriberto; De La Cruz-Burelo, Eduard; Heredia-de La Cruz, Ivan; Lopez-Fernandez, Ricardo; Martínez-Ortega, Jorge; Sánchez Hernández, Alberto; Villasenor-Cendejas, Luis Manuel; Carrillo Moreno, Salvador; Vazquez Valencia, Fabiola; Salazar Ibarguen, Humberto Antonio; Casimiro Linares, Edgar; Morelos Pineda, Antonio; Reyes-Santos, Marco A; Krofcheck, David; Bell, Alan James; Butler, Philip H; Doesburg, Robert; Reucroft, Steve; Silverwood, Hamish; Ahmad, Muhammad; Asghar, Muhammad Irfan; Butt, Jamila; Hoorani, Hafeez R; Khalid, Shoaib; Khan, Wajid Ali; Khurshid, Taimoor; Qazi, Shamona; Shah, Mehar Ali; Shoaib, Muhammad; Bialkowska, Helena; Boimska, Bozena; Frueboes, Tomasz; Górski, Maciej; Kazana, Malgorzata; Nawrocki, Krzysztof; Romanowska-Rybinska, Katarzyna; Szleper, Michal; Wrochna, Grzegorz; Zalewski, Piotr; Brona, Grzegorz; Bunkowski, Karol; Cwiok, Mikolaj; Dominik, Wojciech; Doroba, Krzysztof; Kalinowski, Artur; Konecki, Marcin; Krolikowski, Jan; Misiura, Maciej; Almeida, Nuno; Bargassa, Pedrame; David Tinoco Mendes, Andre; Faccioli, Pietro; Ferreira Parracho, Pedro Guilherme; Gallinaro, Michele; Seixas, Joao; Varela, Joao; Vischia, Pietro; Belotelov, Ivan; Bunin, Pavel; Golutvin, Igor; Gorbunov, Ilya; Kamenev, Alexey; Karjavin, Vladimir; Kozlov, Guennady; Lanev, Alexander; Malakhov, Alexander; Moisenz, Petr; Palichik, Vladimir; Perelygin, Victor; Savina, Maria; Shmatov, Sergey; Smirnov, Vitaly; Volodko, Anton; Zarubin, Anatoli; Evstyukhin, Sergey; Golovtsov, Victor; Ivanov, Yury; Kim, Victor; Levchenko, Petr; Murzin, Victor; Oreshkin, Vadim; Smirnov, Igor; Sulimov, Valentin; Uvarov, Lev; Vavilov, Sergey; Vorobyev, Alexey; Vorobyev, Andrey; Andreev, Yuri; Dermenev, Alexander; Gninenko, Sergei; Golubev, Nikolai; Kirsanov, Mikhail; Krasnikov, Nikolai; Matveev, Viktor; Pashenkov, Anatoli; Tlisov, Danila; Toropin, Alexander; Epshteyn, Vladimir; Erofeeva, Maria; Gavrilov, Vladimir; Kossov, Mikhail; Lychkovskaya, Natalia; Popov, Vladimir; Safronov, Grigory; Semenov, Sergey; Shreyber, Irina; Stolin, Viatcheslav; Vlasov, Evgueni; Zhokin, Alexander; Belyaev, Andrey; Boos, Edouard; Dubinin, Mikhail; Dudko, Lev; Ershov, Alexander; Gribushin, Andrey; Klyukhin, Vyacheslav; Kodolova, Olga; Lokhtin, Igor; Markina, Anastasia; Obraztsov, Stepan; Perfilov, Maxim; Petrushanko, Sergey; Popov, Andrey; Sarycheva, Ludmila; Savrin, Viktor; Snigirev, Alexander; Andreev, Vladimir; Azarkin, Maksim; Dremin, Igor; Kirakosyan, Martin; Leonidov, Andrey; Mesyats, Gennady; Rusakov, Sergey V; Vinogradov, Alexey; Azhgirey, Igor; Bayshev, Igor; Bitioukov, Sergei; Grishin, Viatcheslav; Kachanov, Vassili; Konstantinov, Dmitri; Krychkine, Victor; Petrov, Vladimir; Ryutin, Roman; Sobol, Andrei; Tourtchanovitch, Leonid; Troshin, Sergey; Tyurin, Nikolay; Uzunian, Andrey; Volkov, Alexey; Adzic, Petar; Djordjevic, Milos; Ekmedzic, Marko; Krpic, Dragomir; Milosevic, Jovan; Aguilar-Benitez, Manuel; Alcaraz Maestre, Juan; Arce, Pedro; Battilana, Carlo; Calvo, Enrique; Cerrada, Marcos; Chamizo Llatas, Maria; Colino, Nicanor; De La Cruz, Begona; Delgado Peris, Antonio; Domínguez Vázquez, Daniel; Fernandez Bedoya, Cristina; Fernández Ramos, Juan Pablo; Ferrando, Antonio; Flix, Jose; Fouz, Maria Cruz; Garcia-Abia, Pablo; Gonzalez Lopez, Oscar; Goy Lopez, Silvia; Hernandez, Jose M; Josa, Maria Isabel; Merino, Gonzalo; Puerta Pelayo, Jesus; Quintario Olmeda, Adrián; Redondo, Ignacio; Romero, Luciano; Santaolalla, Javier; Senghi Soares, Mara; Willmott, Carlos; Albajar, Carmen; Codispoti, Giuseppe; de Trocóniz, Jorge F; Brun, Hugues; Cuevas, Javier; Fernandez Menendez, Javier; Folgueras, Santiago; Gonzalez Caballero, Isidro; Lloret Iglesias, Lara; Piedra Gomez, Jonatan; Brochero Cifuentes, Javier Andres; Cabrillo, Iban Jose; Calderon, Alicia; Chuang, Shan-Huei; Duarte Campderros, Jordi; Felcini, Marta; Fernandez, Marcos; Gomez, Gervasio; Gonzalez Sanchez, Javier; Graziano, Alberto; Jorda, Clara; Lopez Virto, Amparo; Marco, Jesus; Marco, Rafael; Martinez Rivero, Celso; Matorras, Francisco; Munoz Sanchez, Francisca Javiela; Rodrigo, Teresa; Rodríguez-Marrero, Ana Yaiza; Ruiz-Jimeno, Alberto; Scodellaro, Luca; Vila, Ivan; Vilar Cortabitarte, Rocio; Abbaneo, Duccio; Auffray, Etiennette; Auzinger, Georg; Bachtis, Michail; Baillon, Paul; Ball, Austin; Barney, David; Benitez, Jose F; Bernet, Colin; Bianchi, Giovanni; Bloch, Philippe; Bocci, Andrea; Bonato, Alessio; Botta, Cristina; Breuker, Horst; Camporesi, Tiziano; Cerminara, Gianluca; Christiansen, Tim; Coarasa Perez, Jose Antonio; D'Enterria, David; Dabrowski, Anne; De Roeck, Albert; Di Guida, Salvatore; Dobson, Marc; Dupont-Sagorin, Niels; Elliott-Peisert, Anna; Frisch, Benjamin; Funk, Wolfgang; Georgiou, Georgios; Giffels, Manuel; Gigi, Dominique; Gill, Karl; Giordano, Domenico; Girone, Maria; Giunta, Marina; Glege, Frank; Gomez-Reino Garrido, Robert; Govoni, Pietro; Gowdy, Stephen; Guida, Roberto; Gundacker, Stefan; Hammer, Josef; Hansen, Magnus; Harris, Philip; Hartl, Christian; Harvey, John; Hegner, Benedikt; Hinzmann, Andreas; Innocente, Vincenzo; Janot, Patrick; Kaadze, Ketino; Karavakis, Edward; Kousouris, Konstantinos; Lecoq, Paul; Lee, Yen-Jie; Lenzi, Piergiulio; Lourenco, Carlos; Magini, Nicolo; Maki, Tuula; Malberti, Martina; Malgeri, Luca; Mannelli, Marcello; Masetti, Lorenzo; Meijers, Frans; Mersi, Stefano; Meschi, Emilio; Moser, Roland; Mozer, Matthias Ulrich; Mulders, Martijn; Musella, Pasquale; Nesvold, Erik; Orsini, Luciano; Palencia Cortezon, Enrique; Perez, Emmanuelle; Perrozzi, Luca; Petrilli, Achille; Pfeiffer, Andreas; Pierini, Maurizio; Pimiä, Martti; Piparo, Danilo; Polese, Giovanni; Quertenmont, Loic; Racz, Attila; Reece, William; Rodrigues Antunes, Joao; Rolandi, Gigi; Rovelli, Chiara; Rovere, Marco; Sakulin, Hannes; Santanastasio, Francesco; Schäfer, Christoph; Schwick, Christoph; Segoni, Ilaria; Sekmen, Sezen; Sharma, Archana; Siegrist, Patrice; Silva, Pedro; Simon, Michal; Sphicas, Paraskevas; Spiga, Daniele; Tsirou, Andromachi; Veres, Gabor Istvan; Vlimant, Jean-Roch; Wöhri, Hermine Katharina; Worm, Steven; Zeuner, Wolfram Dietrich; Bertl, Willi; Deiters, Konrad; Erdmann, Wolfram; Gabathuler, Kurt; Horisberger, Roland; Ingram, Quentin; Kaestli, Hans-Christian; König, Stefan; Kotlinski, Danek; Langenegger, Urs; Meier, Frank; Renker, Dieter; Rohe, Tilman; Bäni, Lukas; Bortignon, Pierluigi; Buchmann, Marco-Andrea; Casal, Bruno; Chanon, Nicolas; Deisher, Amanda; Dissertori, Günther; Dittmar, Michael; Donegà, Mauro; Dünser, Marc; Eller, Philipp; Eugster, Jürg; Freudenreich, Klaus; Grab, Christoph; Hits, Dmitry; Lecomte, Pierre; Lustermann, Werner; Marini, Andrea Carlo; Martinez Ruiz del Arbol, Pablo; Mohr, Niklas; Moortgat, Filip; Nägeli, Christoph; Nef, Pascal; Nessi-Tedaldi, Francesca; Pandolfi, Francesco; Pape, Luc; Pauss, Felicitas; Peruzzi, Marco; Ronga, Frederic Jean; Rossini, Marco; Sala, Leonardo; Sanchez, Ann - Karin; Starodumov, Andrei; Stieger, Benjamin; Takahashi, Maiko; Tauscher, Ludwig; Thea, Alessandro; Theofilatos, Konstantinos; Treille, Daniel; Urscheler, Christina; Wallny, Rainer; Weber, Hannsjoerg Artur; Wehrli, Lukas; Amsler, Claude; Chiochia, Vincenzo; De Visscher, Simon; Favaro, Carlotta; Ivova Rikova, Mirena; Kilminster, Benjamin; Millan Mejias, Barbara; Otiougova, Polina; Robmann, Peter; Snoek, Hella; Tupputi, Salvatore; Verzetti, Mauro; Chang, Yuan-Hann; Chen, Kuan-Hsin; Ferro, Cristina; Kuo, Chia-Ming; Li, Syue-Wei; Lin, Willis; Lu, Yun-Ju; Singh, Anil; Volpe, Roberta; Yu, Shin-Shan; Bartalini, Paolo; Chang, Paoti; Chang, You-Hao; Chang, Yu-Wei; Chao, Yuan; Chen, Kai-Feng; Dietz, Charles; Grundler, Ulysses; Hou, George Wei-Shu; Hsiung, Yee; Kao, Kai-Yi; Lei, Yeong-Jyi; Lu, Rong-Shyang; Majumder, Devdatta; Petrakou, Eleni; Shi, Xin; Shiu, Jing-Ge; Tzeng, Yeng-Ming; Wan, Xia; Wang, Minzu; Asavapibhop, Burin; Srimanobhas, Norraphat; Adiguzel, Aytul; Bakirci, Mustafa Numan; Cerci, Salim; Dozen, Candan; Dumanoglu, Isa; Eskut, Eda; Girgis, Semiray; Gokbulut, Gul; Gurpinar, Emine; Hos, Ilknur; Kangal, Evrim Ersin; Karaman, Turker; Karapinar, Guler; Kayis Topaksu, Aysel; Onengut, Gulsen; Ozdemir, Kadri; Ozturk, Sertac; Polatoz, Ayse; Sogut, Kenan; Sunar Cerci, Deniz; Tali, Bayram; Topakli, Huseyin; Vergili, Latife Nukhet; Vergili, Mehmet; Akin, Ilina Vasileva; Aliev, Takhmasib; Bilin, Bugra; Bilmis, Selcuk; Deniz, Muhammed; Gamsizkan, Halil; Guler, Ali Murat; Ocalan, Kadir; Ozpineci, Altug; Serin, Meltem; Sever, Ramazan; Surat, Ugur Emrah; Yalvac, Metin; Yildirim, Eda; Zeyrek, Mehmet; Gülmez, Erhan; Isildak, Bora; Kaya, Mithat; Kaya, Ozlem; Ozkorucuklu, Suat; Sonmez, Nasuf; Cankocak, Kerem; Levchuk, Leonid; Brooke, James John; Clement, Emyr; Cussans, David; Flacher, Henning; Frazier, Robert; Goldstein, Joel; Grimes, Mark; Heath, Greg P; Heath, Helen F; Kreczko, Lukasz; Metson, Simon; Newbold, Dave M; Nirunpong, Kachanon; Poll, Anthony; Senkin, Sergey; Smith, Vincent J; Williams, Thomas; Basso, Lorenzo; Bell, Ken W; Belyaev, Alexander; Brew, Christopher; Brown, Robert M; Cockerill, David JA; Coughlan, John A; Harder, Kristian; Harper, Sam; Jackson, James; Kennedy, Bruce W; Olaiya, Emmanuel; Petyt, David; Radburn-Smith, Benjamin Charles; Shepherd-Themistocleous, Claire; Tomalin, Ian R; Womersley, William John; Bainbridge, Robert; Ball, Gordon; Beuselinck, Raymond; Buchmuller, Oliver; Colling, David; Cripps, Nicholas; Cutajar, Michael; Dauncey, Paul; Davies, Gavin; Della Negra, Michel; Ferguson, William; Fulcher, Jonathan; Futyan, David; Gilbert, Andrew; Guneratne Bryer, Arlo; Hall, Geoffrey; Hatherell, Zoe; Hays, Jonathan; Iles, Gregory; Jarvis, Martyn; Karapostoli, Georgia; Lyons, Louis; Magnan, Anne-Marie; Marrouche, Jad; Mathias, Bryn; Nandi, Robin; Nash, Jordan; Nikitenko, Alexander; Pela, Joao; Pesaresi, Mark; Petridis, Konstantinos; Pioppi, Michele; Raymond, David Mark; Rogerson, Samuel; Rose, Andrew; Ryan, Matthew John; Seez, Christopher; Sharp, Peter; Sparrow, Alex; Stoye, Markus; Tapper, Alexander; Vazquez Acosta, Monica; Virdee, Tejinder; Wakefield, Stuart; Wardle, Nicholas; Whyntie, Tom; Chadwick, Matthew; Cole, Joanne; Hobson, Peter R; Khan, Akram; Kyberd, Paul; Leggat, Duncan; Leslie, Dawn; Martin, William; Reid, Ivan; Symonds, Philip; Teodorescu, Liliana; Turner, Mark; Hatakeyama, Kenichi; Liu, Hongxuan; Scarborough, Tara; Charaf, Otman; Henderson, Conor; Rumerio, Paolo; Avetisyan, Aram; Bose, Tulika; Fantasia, Cory; Heister, Arno; St John, Jason; Lawson, Philip; Lazic, Dragoslav; Rohlf, James; Sperka, David; Sulak, Lawrence; Alimena, Juliette; Bhattacharya, Saptaparna; Christopher, Grant; Cutts, David; Demiragli, Zeynep; Ferapontov, Alexey; Garabedian, Alex; Heintz, Ulrich; Jabeen, Shabnam; Kukartsev, Gennadiy; Laird, Edward; Landsberg, Greg; Luk, Michael; Narain, Meenakshi; Nguyen, Duong; Segala, Michael; Sinthuprasith, Tutanon; Speer, Thomas; Breedon, Richard; Breto, Guillermo; Calderon De La Barca Sanchez, Manuel; Chauhan, Sushil; Chertok, Maxwell; Conway, John; Conway, Rylan; Cox, Peter Timothy; Dolen, James; Erbacher, Robin; Gardner, Michael; Houtz, Rachel; Ko, Winston; Kopecky, Alexandra; Lander, Richard; Mall, Orpheus; Miceli, Tia; Pellett, Dave; Ricci-Tam, Francesca; Rutherford, Britney; Searle, Matthew; Smith, John; Squires, Michael; Tripathi, Mani; Vasquez Sierra, Ricardo; Yohay, Rachel; Andreev, Valeri; Cline, David; Cousins, Robert; Duris, Joseph; Erhan, Samim; Everaerts, Pieter; Farrell, Chris; Hauser, Jay; Ignatenko, Mikhail; Jarvis, Chad; Rakness, Gregory; Schlein, Peter; Traczyk, Piotr; Valuev, Vyacheslav; Weber, Matthias; Babb, John; Clare, Robert; Dinardo, Mauro Emanuele; Ellison, John Anthony; Gary, J William; Giordano, Ferdinando; Hanson, Gail; Liu, Hongliang; Long, Owen Rosser; Luthra, Arun; Nguyen, Harold; Paramesvaran, Sudarshan; Sturdy, Jared; Sumowidagdo, Suharyo; Wilken, Rachel; Wimpenny, Stephen; Andrews, Warren; Branson, James G; Cerati, Giuseppe Benedetto; Cittolin, Sergio; Evans, David; Holzner, André; Kelley, Ryan; Lebourgeois, Matthew; Letts, James; Macneill, Ian; Mangano, Boris; Padhi, Sanjay; Palmer, Christopher; Petrucciani, Giovanni; Pieri, Marco; Sani, Matteo; Sharma, Vivek; Simon, Sean; Sudano, Elizabeth; Tadel, Matevz; Tu, Yanjun; Vartak, Adish; Wasserbaech, Steven; Würthwein, Frank; Yagil, Avraham; Yoo, Jaehyeok; Barge, Derek; Bellan, Riccardo; Campagnari, Claudio; D'Alfonso, Mariarosaria; Danielson, Thomas; Flowers, Kristen; Geffert, Paul; Golf, Frank; Incandela, Joe; Justus, Christopher; Kalavase, Puneeth; Kovalskyi, Dmytro; Krutelyov, Vyacheslav; Lowette, Steven; Magaña Villalba, Ricardo; Mccoll, Nickolas; Pavlunin, Viktor; Ribnik, Jacob; Richman, Jeffrey; Rossin, Roberto; Stuart, David; To, Wing; West, Christopher; Apresyan, Artur; Bornheim, Adolf; Chen, Yi; Di Marco, Emanuele; Duarte, Javier; Gataullin, Marat; Ma, Yousi; Mott, Alexander; Newman, Harvey B; Rogan, Christopher; Spiropulu, Maria; Timciuc, Vladlen; Veverka, Jan; Wilkinson, Richard; Xie, Si; Yang, Yong; Zhu, Ren-Yuan; Azzolini, Virginia; Calamba, Aristotle; Carroll, Ryan; Ferguson, Thomas; Iiyama, Yutaro; Jang, Dong Wook; Liu, Yueh-Feng; Paulini, Manfred; Vogel, Helmut; Vorobiev, Igor; Cumalat, John Perry; Drell, Brian Robert; Ford, William T; Gaz, Alessandro; Luiggi Lopez, Eduardo; Smith, James; Stenson, Kevin; Ulmer, Keith; Wagner, Stephen Robert; Alexander, James; Chatterjee, Avishek; Eggert, Nicholas; Gibbons, Lawrence Kent; Heltsley, Brian; Hopkins, Walter; Khukhunaishvili, Aleko; Kreis, Benjamin; Mirman, Nathan; Nicolas Kaufman, Gala; Patterson, Juliet Ritchie; Ryd, Anders; Salvati, Emmanuele; Sun, Werner; Teo, Wee Don; Thom, Julia; Thompson, Joshua; Tucker, Jordan; Vaughan, Jennifer; Weng, Yao; Winstrom, Lucas; Wittich, Peter; Winn, Dave; Abdullin, Salavat; Albrow, Michael; Anderson, Jacob; Bauerdick, Lothar AT; Beretvas, Andrew; Berryhill, Jeffrey; Bhat, Pushpalatha C; Burkett, Kevin; Butler, Joel Nathan; Chetluru, Vasundhara; Cheung, Harry; Chlebana, Frank; Elvira, Victor Daniel; Fisk, Ian; Freeman, Jim; Gao, Yanyan; Green, Dan; Gutsche, Oliver; Hanlon, Jim; Harris, Robert M; Hirschauer, James; Hooberman, Benjamin; Jindariani, Sergo; Johnson, Marvin; Joshi, Umesh; Klima, Boaz; Kunori, Shuichi; Kwan, Simon; Leonidopoulos, Christos; Linacre, Jacob; Lincoln, Don; Lipton, Ron; Lykken, Joseph; Maeshima, Kaori; Marraffino, John Michael; Maruyama, Sho; Mason, David; McBride, Patricia; Mishra, Kalanand; Mrenna, Stephen; Musienko, Yuri; Newman-Holmes, Catherine; O'Dell, Vivian; Prokofyev, Oleg; Sexton-Kennedy, Elizabeth; Sharma, Seema; Spalding, William J; Spiegel, Leonard; Taylor, Lucas; Tkaczyk, Slawek; Tran, Nhan Viet; Uplegger, Lorenzo; Vaandering, Eric Wayne; Vidal, Richard; Whitmore, Juliana; Wu, Weimin; Yang, Fan; Yun, Jae Chul; Acosta, Darin; Avery, Paul; Bourilkov, Dimitri; Chen, Mingshui; Cheng, Tongguang; Das, Souvik; De Gruttola, Michele; Di Giovanni, Gian Piero; Dobur, Didar; Drozdetskiy, Alexey; Field, Richard D; Fisher, Matthew; Fu, Yu; Furic, Ivan-Kresimir; Gartner, Joseph; Hugon, Justin; Kim, Bockjoo; Konigsberg, Jacobo; Korytov, Andrey; Kropivnitskaya, Anna; Kypreos, Theodore; Low, Jia Fu; Matchev, Konstantin; Milenovic, Predrag; Mitselmakher, Guenakh; Muniz, Lana; Park, Myeonghun; Remington, Ronald; Rinkevicius, Aurelijus; Sellers, Paul; Skhirtladze, Nikoloz; Snowball, Matthew; Yelton, John; Zakaria, Mohammed; Gaultney, Vanessa; Hewamanage, Samantha; Lebolo, Luis Miguel; Linn, Stephan; Markowitz, Pete; Martinez, German; Rodriguez, Jorge Luis; Adams, Todd; Askew, Andrew; Bochenek, Joseph; Chen, Jie; Diamond, Brendan; Gleyzer, Sergei V; Haas, Jeff; Hagopian, Sharon; Hagopian, Vasken; Jenkins, Merrill; Johnson, Kurtis F; Prosper, Harrison; Veeraraghavan, Venkatesh; Weinberg, Marc; Baarmand, Marc M; Dorney, Brian; Hohlmann, Marcus; Kalakhety, Himali; Vodopiyanov, Igor; Yumiceva, Francisco; Adams, Mark Raymond; Anghel, Ioana Maria; Apanasevich, Leonard; Bai, Yuting; Bazterra, Victor Eduardo; Betts, Russell Richard; Bucinskaite, Inga; Callner, Jeremy; Cavanaugh, Richard; Evdokimov, Olga; Gauthier, Lucie; Gerber, Cecilia Elena; Hofman, David Jonathan; Khalatyan, Samvel; Lacroix, Florent; O'Brien, Christine; Silkworth, Christopher; Strom, Derek; Turner, Paul; Varelas, Nikos; Akgun, Ugur; Albayrak, Elif Asli; Bilki, Burak; Clarida, Warren; Duru, Firdevs; Griffiths, Scott; Merlo, Jean-Pierre; Mermerkaya, Hamit; Mestvirishvili, Alexi; Moeller, Anthony; Nachtman, Jane; Newsom, Charles Ray; Norbeck, Edwin; Onel, Yasar; Ozok, Ferhat; Sen, Sercan; Tan, Ping; Tiras, Emrah; Wetzel, James; Yetkin, Taylan; Yi, Kai; Barnett, Bruce Arnold; Blumenfeld, Barry; Bolognesi, Sara; Fehling, David; Giurgiu, Gavril; Gritsan, Andrei; Guo, Zijin; Hu, Guofan; Maksimovic, Petar; Swartz, Morris; Whitbeck, Andrew; Baringer, Philip; Bean, Alice; Benelli, Gabriele; Kenny Iii, Raymond Patrick; Murray, Michael; Noonan, Daniel; Sanders, Stephen; Stringer, Robert; Tinti, Gemma; Wood, Jeffrey Scott; Barfuss, Anne-Fleur; Bolton, Tim; Chakaberia, Irakli; Ivanov, Andrew; Khalil, Sadia; Makouski, Mikhail; Maravin, Yurii; Shrestha, Shruti; Svintradze, Irakli; Gronberg, Jeffrey; Lange, David; Rebassoo, Finn; Wright, Douglas; Baden, Drew; Calvert, Brian; Eno, Sarah Catherine; Gomez, Jaime; Hadley, Nicholas John; Kellogg, Richard G; Kirn, Malina; Kolberg, Ted; Lu, Ying; Marionneau, Matthieu; Mignerey, Alice; Pedro, Kevin; Peterman, Alison; Skuja, Andris; Temple, Jeffrey; Tonjes, Marguerite; Tonwar, Suresh C; Apyan, Aram; Bauer, Gerry; Bendavid, Joshua; Busza, Wit; Butz, Erik; Cali, Ivan Amos; Chan, Matthew; Dutta, Valentina; Gomez Ceballos, Guillelmo; Goncharov, Maxim; Kim, Yongsun; Klute, Markus; Krajczar, Krisztian; Levin, Andrew; Luckey, Paul David; Ma, Teng; Nahn, Steve; Paus, Christoph; Ralph, Duncan; Roland, Christof; Roland, Gunther; Rudolph, Matthew; Stephans, George; Stöckli, Fabian; Sumorok, Konstanty; Sung, Kevin; Velicanu, Dragos; Wenger, Edward Allen; Wolf, Roger; Wyslouch, Bolek; Yang, Mingming; Yilmaz, Yetkin; Yoon, Sungho; Zanetti, Marco; Zhukova, Victoria; Cooper, Seth; Dahmes, Bryan; De Benedetti, Abraham; Franzoni, Giovanni; Gude, Alexander; Kao, Shih-Chuan; Klapoetke, Kevin; Kubota, Yuichi; Mans, Jeremy; Pastika, Nathaniel; Rusack, Roger; Sasseville, Michael; Singovsky, Alexander; Tambe, Norbert; Turkewitz, Jared; Cremaldi, Lucien Marcus; Kroeger, Rob; Perera, Lalith; Rahmat, Rahmat; Sanders, David A; Avdeeva, Ekaterina; Bloom, Kenneth; Bose, Suvadeep; Claes, Daniel R; Dominguez, Aaron; Eads, Michael; Keller, Jason; Kravchenko, Ilya; Lazo-Flores, Jose; Malik, Sudhir; Snow, Gregory R; Godshalk, Andrew; Iashvili, Ia; Jain, Supriya; Kharchilava, Avto; Kumar, Ashish; Rappoccio, Salvatore; Alverson, George; Barberis, Emanuela; Baumgartel, Darin; Chasco, Matthew; Haley, Joseph; Nash, David; Orimoto, Toyoko; Trocino, Daniele; Wood, Darien; Zhang, Jinzhong; Anastassov, Anton; Hahn, Kristan Allan; Kubik, Andrew; Lusito, Letizia; Mucia, Nicholas; Odell, Nathaniel; Ofierzynski, Radoslaw Adrian; Pollack, Brian; Pozdnyakov, Andrey; Schmitt, Michael Henry; Stoynev, Stoyan; Velasco, Mayda; Won, Steven; Antonelli, Louis; Berry, Douglas; Brinkerhoff, Andrew; Chan, Kwok Ming; Hildreth, Michael; Jessop, Colin; Karmgard, Daniel John; Kolb, Jeff; Lannon, Kevin; Luo, Wuming; Lynch, Sean; Marinelli, Nancy; Morse, David Michael; Pearson, Tessa; Planer, Michael; Ruchti, Randy; Slaunwhite, Jason; Valls, Nil; Wayne, Mitchell; Wolf, Matthias; Bylsma, Ben; Durkin, Lloyd Stanley; Hill, Christopher; Hughes, Richard; Kotov, Khristian; Ling, Ta-Yung; Puigh, Darren; Rodenburg, Marissa; Vuosalo, Carl; Williams, Grayson; Winer, Brian L; Berry, Edmund; Elmer, Peter; Halyo, Valerie; Hebda, Philip; Hegeman, Jeroen; Hunt, Adam; Jindal, Pratima; Koay, Sue Ann; Lopes Pegna, David; Lujan, Paul; Marlow, Daniel; Medvedeva, Tatiana; Mooney, Michael; Olsen, James; Piroué, Pierre; Quan, Xiaohang; Raval, Amita; Saka, Halil; Stickland, David; Tully, Christopher; Werner, Jeremy Scott; Zuranski, Andrzej; Brownson, Eric; Lopez, Angel; Mendez, Hector; Ramirez Vargas, Juan Eduardo; Alagoz, Enver; Barnes, Virgil E; Benedetti, Daniele; Bolla, Gino; Bortoletto, Daniela; De Mattia, Marco; Everett, Adam; Hu, Zhen; Jones, Matthew; Koybasi, Ozhan; Kress, Matthew; Laasanen, Alvin T; Leonardo, Nuno; Maroussov, Vassili; Merkel, Petra; Miller, David Harry; Neumeister, Norbert; Shipsey, Ian; Silvers, David; Svyatkovskiy, Alexey; Vidal Marono, Miguel; Yoo, Hwi Dong; Zablocki, Jakub; Zheng, Yu; Guragain, Samir; Parashar, Neeti; Adair, Antony; Akgun, Bora; Boulahouache, Chaouki; Ecklund, Karl Matthew; Geurts, Frank JM; Li, Wei; Padley, Brian Paul; Redjimi, Radia; Roberts, Jay; Zabel, James; Betchart, Burton; Bodek, Arie; Chung, Yeon Sei; Covarelli, Roberto; de Barbaro, Pawel; Demina, Regina; Eshaq, Yossof; Ferbel, Thomas; Garcia-Bellido, Aran; Goldenzweig, Pablo; Han, Jiyeon; Harel, Amnon; Miner, Daniel Carl; Vishnevskiy, Dmitry; Zielinski, Marek; Bhatti, Anwar; Ciesielski, Robert; Demortier, Luc; Goulianos, Konstantin; Lungu, Gheorghe; Malik, Sarah; Mesropian, Christina; Arora, Sanjay; Barker, Anthony; Chou, John Paul; Contreras-Campana, Christian; Contreras-Campana, Emmanuel; Duggan, Daniel; Ferencek, Dinko; Gershtein, Yuri; Gray, Richard; Halkiadakis, Eva; Hidas, Dean; Lath, Amitabh; Panwalkar, Shruti; Park, Michael; Patel, Rishi; Rekovic, Vladimir; Robles, Jorge; Rose, Keith; Salur, Sevil; Schnetzer, Steve; Seitz, Claudia; Somalwar, Sunil; Stone, Robert; Thomas, Scott; Walker, Matthew; Cerizza, Giordano; Hollingsworth, Matthew; Spanier, Stefan; Yang, Zong-Chang; York, Andrew; Eusebi, Ricardo; Flanagan, Will; Gilmore, Jason; Kamon, Teruki; Khotilovich, Vadim; Montalvo, Roy; Osipenkov, Ilya; Pakhotin, Yuriy; Perloff, Alexx; Roe, Jeffrey; Safonov, Alexei; Sakuma, Tai; Sengupta, Sinjini; Suarez, Indara; Tatarinov, Aysen; Toback, David; Akchurin, Nural; Damgov, Jordan; Dragoiu, Cosmin; Dudero, Phillip Russell; Jeong, Chiyoung; Kovitanggoon, Kittikul; Lee, Sung Won; Libeiro, Terence; Volobouev, Igor; Appelt, Eric; Delannoy, Andrés G; Florez, Carlos; Greene, Senta; Gurrola, Alfredo; Johns, Willard; Kurt, Pelin; Maguire, Charles; Melo, Andrew; Sharma, Monika; Sheldon, Paul; Snook, Benjamin; Tuo, Shengquan; Velkovska, Julia; Arenton, Michael Wayne; Balazs, Michael; Boutle, Sarah; Cox, Bradley; Francis, Brian; Goodell, Joseph; Hirosky, Robert; Ledovskoy, Alexander; Lin, Chuanzhe; Neu, Christopher; Wood, John; Gollapinni, Sowjanya; Harr, Robert; Karchin, Paul Edmund; Kottachchi Kankanamge Don, Chamath; Lamichhane, Pramod; Sakharov, Alexandre; Anderson, Michael; Belknap, Donald; Borrello, Laura; Carlsmith, Duncan; Cepeda, Maria; Dasu, Sridhara; Friis, Evan; Gray, Lindsey; Grogg, Kira Suzanne; Grothe, Monika; Hall-Wilton, Richard; Herndon, Matthew; Hervé, Alain; Klabbers, Pamela; Klukas, Jeffrey; Lanaro, Armando; Lazaridis, Christos; Loveless, Richard; Mohapatra, Ajit; Ojalvo, Isabel; Palmonari, Francesco; Pierro, Giuseppe Antonio; Ross, Ian; Savin, Alexander; Smith, Wesley H; Swanson, Joshua
2013-04-02
In this paper, a search for supersymmetry (SUSY) is presented in events with two opposite-sign isolated leptons in the final state, accompanied by hadronic jets and missing transverse energy. An artificial neural network is employed to discriminate possible SUSY signals from standard model background. The analysis uses a data sample collected with the CMS detector during the 2011 LHC run, corresponding to an integrated luminosity of 4.98 inverse femtobarns of proton-proton collisions at the center of mass energy of 7 TeV. Compared to other CMS analyses, this one uses relaxed criteria on missing transverse energy (missing ET > 40 GeV) and total hadronic transverse energy (HT > 120 GeV), thus probing different regions of parameter space. Agreement is found between standard model expectation and observation, yielding limits in the context of the constrained mininal supersymmetric standard model and on a set of simplified models.
Heuristics structure and pervade formal risk assessment.
MacGillivray, Brian H
2014-04-01
Lay perceptions of risk appear rooted more in heuristics than in reason. A major concern of the risk regulation literature is that such "error-strewn" perceptions may be replicated in policy, as governments respond to the (mis)fears of the citizenry. This has led many to advocate a relatively technocratic approach to regulating risk, characterized by high reliance on formal risk and cost-benefit analysis. However, through two studies of chemicals regulation, we show that the formal assessment of risk is pervaded by its own set of heuristics. These include rules to categorize potential threats, define what constitutes valid data, guide causal inference, and to select and apply formal models. Some of these heuristics lay claim to theoretical or empirical justifications, others are more back-of-the-envelope calculations, while still more purport not to reflect some truth but simply to constrain discretion or perform a desk-clearing function. These heuristics can be understood as a way of authenticating or formalizing risk assessment as a scientific practice, representing a series of rules for bounding problems, collecting data, and interpreting evidence (a methodology). Heuristics are indispensable elements of induction. And so they are not problematic per se, but they can become so when treated as laws rather than as contingent and provisional rules. Pitfalls include the potential for systematic error, masking uncertainties, strategic manipulation, and entrenchment. Our central claim is that by studying the rules of risk assessment qua rules, we develop a novel representation of the methods, conventions, and biases of the prior art. © 2013 Society for Risk Analysis.
Hybrid local search algorithm via evolutionary avalanches for spin glass based portfolio selection
Directory of Open Access Journals (Sweden)
Majid Vafaei Jahan
2012-07-01
As shown in this paper, this strategy can lead to faster rate of convergence and improved performance than conventional SA and EO algorithm. The resulting are then used to solve the portfolio selection multi-objective problem that is a non-deterministic polynomial complete (NPC problem. This is confirmed by test results of five of the world’s major stock markets, reliability test and phase transition diagram; and finally, the convergence speed is compared to other heuristic methods such as Neural Network (NN, Tabu Search (TS, and Genetic Algorithm (GA.
Directory of Open Access Journals (Sweden)
Mehmet Fatih Tasgetiren
2016-10-01
Full Text Available In this paper, we present a variable block insertion heuristic (VBIH algorithm to solve the blocking flowshop scheduling problem with the total flowtime criterion. In the VBIH algorithm, we define a minimum and a maximum block size. After constructing the initial sequence, the VBIH algorithm starts with a minimum block size being equal to one. It removes the block from the current sequence and inserts it into the partial sequence sequentially with a predetermined move size. The sequence, which is obtained after several block moves, goes under a variable local search (VLS, which is based on traditional insertion and swap neighborhood structures. If the new sequence obtained after the VLS local search is better than the current sequence, it replaces the current sequence. As long as it improves, it keeps the same block size. However, if it does not improve, the block size is incremented by one and a simulated annealing-type of acceptance criterion is used to accept the current sequence. This process is repeated until the block size reaches at the maximum block size. Furthermore, we present a novel constructive heuristic, which is based on the profile fitting heuristic from the literature. The proposed constructive heuristic is able to further improve the best known solutions for some larger instances in a few seconds. Parameters of the constructive heuristic and the VBIH algorithm are determined through a design of experiment approach. Extensive computational results on the Taillard’s well-known benchmark suite show that the proposed VBIH algorithm outperforms the discrete artificial bee colony algorithm, which is one of the most efficient algorithms recently in the literature. Ultimately, 52 out of the 150 best known solutions are further improved with substantial margins.
Heuristic Evaluation of E-Learning Courses: A Comparative Analysis of Two E-Learning Heuristic Sets
Zaharias, Panagiotis; Koutsabasis, Panayiotis
2012-01-01
Purpose: The purpose of this paper is to discuss heuristic evaluation as a method for evaluating e-learning courses and applications and more specifically to investigate the applicability and empirical use of two customized e-learning heuristic protocols. Design/methodology/approach: Two representative e-learning heuristic protocols were chosen…
On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer
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Agam Gupta
2015-07-01
Full Text Available With the advancement in the field of Artificial Intelligence, there have been considerable efforts to develop technologies for pattern recognition related to medical diagnosis. Artificial Neural Networks (ANNs, a significant piece of Artificial Intelligence forms the base for most of the marvels in the former field. However, ANNs face the problem of premature convergence at a local minimum and inability to set hyper-parameters (like the number of neurons, learning rate, etc. while using Back Propagation Algorithm (BPA. In this paper, we have used the Genetic Algorithm (GA for the evolution of the ANN, which overcomes the limitations of the BPA. Since GA alone cannot fit for a high-dimensional, complex and multi-modal optimization landscape of the ANN, BPA is used as a local search algorithm to aid the evolution. The contributions of GA and BPA in the resultant approach are adjudged to determine the magnitude of local search necessary for optimization, striking a clear balance between exploration and exploitation in the evolution. The algorithm was applied to deal with the problem of Breast Cancer diagnosis. Results showed that under optimal settings, hybrid algorithm performs better than BPA or GA alone.
Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia
2014-01-01
For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.
Directory of Open Access Journals (Sweden)
Jie-sheng Wang
2014-01-01
Full Text Available For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.
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M. Sreeshakthy
2016-01-01
Full Text Available Department of Computer Science and Engineering,Anna University Regional Centre, Coimbatore, Indiam.sribtechit@gmail.comJ. PreethiDepartment of Computer Science and EngineeringAnna University Regional Centre, Coimbatore, Indiapreethi17j@yahoo.comEmotions are very important in human decision handling, interaction and cognitive process. In this paper describes that recognize the human emotions from DEAP EEG dataset with different kind of methods. Audio – video based stimuli is used to extract the emotions. EEG signal is divided into different bands using discrete wavelet transformation with db8 wavelet function for further process. Statistical and energy based features are extracted from the bands, based on the features emotions are classified with feed forward neural network with weight optimized algorithm like PSO. Before that the particular band has to be selected based on the training performance of neural networks and then the emotions are classified. In this experimental result describes that the gamma and alpha bands are provides the accurate classification result with average classification rate of 90.3% of using NNRBF, 90.325% of using PNN, 96.3% of using PSO trained NN, 98.1 of using Cuckoo trained NN. At last the emotions are classified into two different groups like valence and arousal. Based on that identifies the person normal and abnormal behavioral using classified emotion.
Tracking disease progression by searching paths in a temporal network of biological processes.
Anand, Rajat; Chatterjee, Samrat
2017-01-01
Metabolic disorders such as obesity and diabetes are diseases which develop gradually over time through the perturbations of biological processes. These perturbed biological processes usually work in an interdependent way. Systematic experiments tracking disease progression at gene level are usually conducted through a temporal microarray data. There is a need for developing methods to analyze such highly complex data to capture disease progression at the molecular level. In the present study, we have considered temporal microarray data from an experiment conducted to study development of obesity and diabetes in mice. We first constructed a network between biological processes through common genes. We analyzed the data to obtain perturbed biological processes at each time point. Finally, we used the biological process network to find links between these perturbed biological processes. This enabled us to identify paths linking initial perturbed processes with final perturbed processes which capture disease progression. Using different datasets and statistical tests, we established that these paths are highly precise to the dataset from which these are obtained. We also established that the connecting genes present in these paths might contain some biological information and thus can be used for further mechanistic studies. The methods developed in our study are also applicable to a broad array of temporal data.
Learning process mapping heuristics under stochastic sampling overheads
Ieumwananonthachai, Arthur; Wah, Benjamin W.
1991-01-01
A statistical method was developed previously for improving process mapping heuristics. The method systematically explores the space of possible heuristics under a specified time constraint. Its goal is to get the best possible heuristics while trading between the solution quality of the process mapping heuristics and their execution time. The statistical selection method is extended to take into consideration the variations in the amount of time used to evaluate heuristics on a problem instance. The improvement in performance is presented using the more realistic assumption along with some methods that alleviate the additional complexity.
Greedy Local Search and Vertex Cover in Sparse Random Graphs
DEFF Research Database (Denmark)
Witt, Carsten
2009-01-01
finds an optimal cover in polynomial time with a probability arbitrarily close to 1. This behavior relies on the absence of a giant component. As an additional insight into the randomized search, it is shown that the heuristic fails badly also on graphs consisting of a single tree component of maximum......Recently, various randomized search heuristics have been studied for the solution of the minimum vertex cover problem, in particular for sparse random instances according to the G(n, c/n) model, where c > 0 is a constant. Methods from statistical physics suggest that the problem is easy if c .... This work starts with a rigorous explanation for this claim based on the refined analysis of the Karp-Sipser algorithm by Aronson et al. Subsequently, theoretical supplements are given to experimental studies of search heuristics on random graphs. For c search heuristic...
Breadth-first search-based single-phase algorithms for bridge detection in wireless sensor networks.
Akram, Vahid Khalilpour; Dagdeviren, Orhan
2013-07-10
Wireless sensor networks (WSNs) are promising technologies for exploring harsh environments, such as oceans, wild forests, volcanic regions and outer space. Since sensor nodes may have limited transmission range, application packets may be transmitted by multi-hop communication. Thus, connectivity is a very important issue. A bridge is a critical edge whose removal breaks the connectivity of the network. Hence, it is crucial to detect bridges and take preventions. Since sensor nodes are battery-powered, services running on nodes should consume low energy. In this paper, we propose energy-efficient and distributed bridge detection algorithms for WSNs. Our algorithms run single phase and they are integrated with the Breadth-First Search (BFS) algorithm, which is a popular routing algorithm. Our first algorithm is an extended version of Milic's algorithm, which is designed to reduce the message length. Our second algorithm is novel and uses ancestral knowledge to detect bridges. We explain the operation of the algorithms, analyze their proof of correctness, message, time, space and computational complexities. To evaluate practical importance, we provide testbed experiments and extensive simulations. We show that our proposed algorithms provide less resource consumption, and the energy savings of our algorithms are up by 5.5-times.
Combining factual and heuristic knowledge in knowledge acquisition
Gomez, Fernando; Hull, Richard; Karr, Clark; Hosken, Bruce; Verhagen, William
1992-01-01
A knowledge acquisition technique that combines heuristic and factual knowledge represented as two hierarchies is described. These ideas were applied to the construction of a knowledge acquisition interface to the Expert System Analyst (OPERA). The goal of OPERA is to improve the operations support of the computer network in the space shuttle launch processing system. The knowledge acquisition bottleneck lies in gathering knowledge from human experts and transferring it to OPERA. OPERA's knowledge acquisition problem is approached as a classification problem-solving task, combining this approach with the use of factual knowledge about the domain. The interface was implemented in a Symbolics workstation making heavy use of windows, pull-down menus, and other user-friendly devices.
Using Heuristic Algorithms to Optimize Observing Target Sequences
Sosnowska, D.; Ouadahi, A.; Buchschacher, N.; Weber, L.; Pepe, F.
2014-05-01
The preparation of observations is normally carried out at the telescope by the visiting observer. In order to help the observer, we propose several algorithms to automatically optimize the sequence of targets. The optimization consists of assuring that all the chosen targets are observable within the given time interval, and to find their best execution order in terms of the observation quality and the shortest telescope displacement time. Since an exhaustive search is too expensive in time, we researched heuristic algorithms, specifically: Min-Conflict, Non-Sorting Genetic Algorithms and Simulated Annealing. Multiple metaheuristics are used in parallel to swiftly give an approximation of the best solution, with all the constraints satisfied and the total execution time minimized. The optimization process has a duration on the order of tens of seconds, allowing for quick re-adaptation in case of changing atmospheric conditions. The graphical user interface allows the user to control the parameters of the optimization process. Therefore, the search can be adjusted in real time. The module was coded in a way to allow easily the addition of new constraints, and thus ensure its compatibility with different instruments. For now, the application runs as a plug-in to the observation preparation tool called New Short Term Scheduler, which is used on three spectrographs dedicated to the exoplanets search: HARPS at the La Silla observatory, HARPS North at the La Palma observatory and SOPHIE at the Observatoire de Haute-Provence.
Search for natural products related to regeneration of the neuronal network.
Tohda, Chihiro; Kuboyama, Tomoharu; Komatsu, Katsuko
2005-01-01
The reconstruction of neuronal networks in the damaged brain is necessary for the therapeutic treatment of neurodegenerative diseases. We have screened the neurite outgrowth activity of herbal drugs, and identified several active constituents. In each compound, neurite outgrowth activity was investigated under amyloid-beta-induced neuritic atrophy. Most of the compounds with neurite regenerative activity also demonstrated memory improvement activity in Alzheimer's disease-model mice. Protopanaxadiol-type saponins in Ginseng drugs and their metabolite, M1 (20-O-beta-D-glucopyranosyl-(20S)-protopanaxadiol), showed potent regeneration activity for axons and synapses, and amelioration of memory impairment. Withanolide derivatives (withanolide A, withanoside IV, and withanoside VI) isolated from the Indian herbal drug Ashwagandha, also showed neurite extension in normal and damaged cortical neurons. Trigonelline, a constituent of coffee beans, demonstrated the regeneration of dendrites and axons, in addition to memory improvement. 2005 S. Karger AG, Basel
Reconsidering "evidence" for fast-and-frugal heuristics.
Hilbig, Benjamin E
2010-12-01
In several recent reviews, authors have argued for the pervasive use of fast-and-frugal heuristics in human judgment. They have provided an overview of heuristics and have reiterated findings corroborating that such heuristics can be very valid strategies leading to high accuracy. They also have reviewed previous work that implies that simple heuristics are actually used by decision makers. Unfortunately, concerning the latter point, these reviews appear to be somewhat incomplete. More important, previous conclusions have been derived from investigations that bear some noteworthy methodological limitations. I demonstrate these by proposing a new heuristic and provide some novel critical findings. Also, I review some of the relevant literature often not-or only partially-considered. Overall, although some fast-and-frugal heuristics indeed seem to predict behavior at times, there is little to no evidence for others. More generally, the empirical evidence available does not warrant the conclusion that heuristics are pervasively used.
Veenstra-VanderWeele, Jeremy; Blakely, Randy D
2012-01-01
Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder affecting approximately 1% of children. ASD is defined by core symptoms in two domains: negative symptoms of impairment in social and communication function, and positive symptoms of restricted and repetitive behaviors. Available treatments are inadequate for treating both core symptoms and associated conditions. Twin studies indicate that ASD susceptibility has a large heritable component. Genetic studies have identified promising leads, with converging insights emerging from single-gene disorders that bear ASD features, with particular interest in mammalian target of rapamycin (mTOR)-linked synaptic plasticity mechanisms. Mouse models of these disorders are revealing not only opportunities to model behavioral perturbations across species, but also evidence of postnatal rescue of brain and behavioral phenotypes. An intense search for ASD biomarkers has consistently pointed to elevated platelet serotonin (5-HT) levels and a surge in brain growth in the first 2 years of life. Following a review of the diversity of ASD phenotypes and its genetic origins and biomarkers, we discuss opportunities for translation of these findings into novel ASD treatments, focusing on mTor- and 5-HT-signaling pathways, and their possible intersection. Paralleling the progress made in understanding the root causes of rare genetic syndromes that affect cognitive development, we anticipate progress in models systems using bona fide ASD-associated molecular changes that have the potential to accelerate the development of ASD diagnostics and therapeutics.
Path-Wise Test Data Generation Based on Heuristic Look-Ahead Methods
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Ying Xing
2014-01-01
Full Text Available Path-wise test data generation is generally considered an important problem in the automation of software testing. In essence, it is a constraint optimization problem, which is often solved by search methods such as backtracking algorithms. In this paper, the backtracking algorithm branch and bound and state space search in artificial intelligence are introduced to tackle the problem of path-wise test data generation. The former is utilized to explore the space of potential solutions and the latter is adopted to construct the search tree dynamically. Heuristics are employed in the look-ahead stage of the search. Dynamic variable ordering is presented with a heuristic rule to break ties, values of a variable are determined by the monotonicity analysis on branching conditions, and maintaining path consistency is achieved through analysis on the result of interval arithmetic. An optimization method is also proposed to reduce the search space. The results of empirical experiments show that the search is conducted in a basically backtrack-free manner, which ensures both test data generation with promising performance and its excellence over some currently existing static and dynamic methods in terms of coverage. The results also demonstrate that the proposed method is applicable in engineering.
Simple heuristics in over-the-counter drug choices: a new hint for medical education and practice.
Riva, Silvia; Monti, Marco; Antonietti, Alessandro
2011-01-01
Over-the-counter (OTC) drugs are widely available and often purchased by consumers without advice from a health care provider. Many people rely on self-management of medications to treat common medical conditions. Although OTC medications are regulated by the National and the International Health and Drug Administration, many people are unaware of proper dosing, side effects, adverse drug reactions, and possible medication interactions. This study examined how subjects make their decisions to select an OTC drug, evaluating the role of cognitive heuristics which are simple and adaptive rules that help the decision-making process of people in everyday contexts. By analyzing 70 subjects' information-search and decision-making behavior when selecting OTC drugs, we examined the heuristics they applied in order to assess whether simple decision-making processes were also accurate and relevant. Subjects were tested with a sequence of two experimental tests based on a computerized Java system devised to analyze participants' choices in a virtual environment. We found that subjects' information-search behavior reflected the use of fast and frugal heuristics. In addition, although the heuristics which correctly predicted subjects' decisions implied significantly fewer cues on average than the subjects did in the information-search task, they were accurate in describing order of information search. A simple combination of a fast and frugal tree and a tallying rule predicted more than 78% of subjects' decisions. The current emphasis in health care is to shift some responsibility onto the consumer through expansion of self medication. To know which cognitive mechanisms are behind the choice of OTC drugs is becoming a relevant purpose of current medical education. These findings have implications both for the validity of simple heuristics describing information searches in the field of OTC drug choices and for current medical education, which has to prepare competent health
Searching for collective behavior in a large network of sensory neurons.
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Gašper Tkačik
2014-01-01
Full Text Available Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such "K-pairwise" models--being systematic extensions of the previously used pairwise Ising models--provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1 estimating its entropy, which constrains the population's capacity to represent visual information; 2 classifying activity patterns into a small set of metastable collective modes; 3 showing that the neural codeword ensembles are extremely inhomogenous; 4 demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction.
Döveling, Katrin
2015-04-01
In an age of rising impact of online communication in social network sites (SNS), emotional interaction is neither limited nor restricted by time or space. Bereavement extends to the anonymity of cyberspace. What role does virtual interaction play in SNS in dealing with the basic human emotion of grief caused by the loss of a beloved person? The analysis laid out in this article provides answers in light of an interdisciplinary perspective on online bereavement. Relevant lines of research are scrutinized. After laying out the theoretical spectrum for the study, hypotheses based on a prior in-depth qualitative content analysis of 179 postings in three different German online bereavement platforms are proposed and scrutinized in a quantitative content analysis (2127 postings from 318 users). Emotion regulation patterns in SNS and similarities as well as differences in online bereavement of children, adolescents and adults are revealed. Large-scale quantitative findings into central motives, patterns, and restorative effects of online shared bereavement in regulating distress, fostering personal empowerment, and engendering meaning are presented. The article closes with implications for further analysis in memorialization practices.
Addressing Authorship Issues Prospectively: A Heuristic Approach.
Roberts, Laura Weiss
2017-02-01
Collaborative writing in academic medicine gives rise to more richly informed scholarship, and yet challenging ethical issues surrounding authorship are commonly encountered. International guidelines on authorship help clarify whether individuals who have contributed to a completed scholarly work have been correctly included as authors, but these guidelines do not facilitate intentional and proactive authorship planning or decisions regarding authorship order.In this Commentary, the author presents a heuristic approach to help collaborators clarify, anticipate, and resolve practical and ethically important authorship issues as they engage in the process of developing manuscripts. As this approach illustrates, assignment of authorship should balance work effort and professional responsibility, reflecting the effort and intellectual contribution and the public accountability of the individuals who participate in the work. Using a heuristic approach for managing authorship issues prospectively can foster an ethical, collaborative writing process in which individuals are properly recognized for their contributions.
Solving the Tractor and Semi-Trailer Routing Problem Based on a Heuristic Approach
Directory of Open Access Journals (Sweden)
Hongqi Li
2012-01-01
Full Text Available We study the tractor and semi-trailer routing problem (TSRP, a variant of the vehicle routing problem (VRP. In the TSRP model for this paper, vehicles are dispatched on a trailer-flow network where there is only one main depot, and all tractors originate and terminate in the main depot. Two types of decisions are involved: the number of tractors and the route of each tractor. Heuristic algorithms have seen widespread application to various extensions of the VRP. However, this approach has not been applied to the TSRP. We propose a heuristic algorithm to solve the TSRP. The proposed heuristic algorithm first constructs the initial route set by the limitation of a driver’s on-duty time. The candidate routes in the initial set are then filtered by a two-phase approach. The computational study shows that our algorithm is feasible for the TSRP. Moreover, the algorithm takes relatively little time to obtain satisfactory solutions. The results suggest that our heuristic algorithm is competitive in solving the TSRP.
Modeling soil bulk density through a complete data scanning procedure: Heuristic alternatives
Shiri, Jalal; Keshavarzi, Ali; Kisi, Ozgur; Karimi, Sepideh; Iturraran-Viveros, Ursula
2017-06-01
Soil bulk density (BD) is very important factor in land drainage and reclamation, irrigation scheduling (for estimating the soil volumetric water content), and assessing soil carbon and nutrient stock as well as determining the pollutant mass balance in soils. Numerous pedotransfer functions have been suggested so far to relate the soil BD values to soil parameters (e.g. soil separates, carbon content, etc). The present paper aims at simulating soil BD using easily measured soil variables through heuristic gene expression programming (GEP), neural networks (NN), random forest (RF), support vector machine (SVM), and boosted regression trees (BT) techniques. The statistical Gamma test was utilized to identify the most influential soil parameters on BD. The applied models were assessed through k-fold testing where all the available data patterns were involved in the both training and testing stages, which provide an accurate assessment of the models accuracy. Some existing pedotransfer functions were also applied and compared with the heuristic models. The obtained results revealed that the heuristic GEP model outperformed the other applied models globally and per test stage. Nevertheless, the performance accuracy of the applied heuristic models was much better than those of the applied pedotransfer functions. Using k-fold testing provides a more-in-detail judgment of the models.
Methodology and Implementation on DSP of Heuristic Multiuser DS/CDMA Detectors
Directory of Open Access Journals (Sweden)
Alex Miyamoto Mussi
2010-12-01
Full Text Available The growing number of users of mobile communications networks and the scarcity of the electromagnetic spectrum make the use of diversity techniques and detection/decoding efficient, such as the use of multiple antennas at the transmitter and/or receiver, multiuser detection (MuD – Multiuser Detection, among others, have an increasingly prominent role in the telecommunications landscape. This paper presents a design methodology based on digital signal processors (DSP – Digital Signal Processor with a view to the implementation of multiuser heuristics detectors in systems DS/CDMA (Direct Sequence Code Division Multiple Access. Heuristics detection techniques result in near-optimal performance in order to approach the performance of maximum-likelihood (ML. In this work, was employed the DSP development platform called the C6713 DSK, which is based in Texas TMS320C6713 processor. The heuristics techniques proposed are based on well established algorithms in the literature. The efficiency of the algorithms implemented in DSP has been evaluated numerically by computing the measure of bit error rate (BER. Finally, the feasibility of implementation in DSP could then be verified by comparing results from multiple Monte-Carlo simulation in Matlab, with those obtained from implementation on DSP. It also demonstrates the effective increase in performance and system capacity of DS/CDMA with the use of heuristic multiuser detection techniques, implemented directly in the DSP.
Heuristic Optimization Approach to Selecting a Transport Connection in City Public Transport
Kul’ka Jozef; Mantič Martin; Kopas Melichar; Faltinová Eva; Kachman Daniel
2017-01-01
The article presents a heuristic optimization approach to select a suitable transport connection in the framework of a city public transport. This methodology was applied on a part of the public transport in Košice, because it is the second largest city in the Slovak Republic and its network of the public transport creates a complex transport system, which consists of three different transport modes, namely from the bus transport, tram transport and trolley-bus transport. This solution focuse...
Heuristics and Biases in Military Decision Making
2010-10-01
critical com- ponents increases, we find mathematically that the probability of event (or system) failure increases. However, we again find that...Uncertainty: Heuristics and Biases, ed. Daniel Kahneman and Amos Tversky (New York, Cambridge University Press, 1982), 156-57. It is similar to a quiz I...gave during my Game Theory class at West Point. 38. Mathematically , this problem can be solved using Bayesian inference. 39. Some may feel that the
Empirical heuristics for improving Intermittent Demand Forecasting
Petropoulos, Fotios; Nikolopoulos, Konstantinos; Spithourakis, George; Assimakopoulos, Vassilios
2013-01-01
Purpose– Intermittent demand appears sporadically, with some time periods not even displaying any demand at all. Even so, such patterns constitute considerable proportions of the total stock in many industrial settings. Forecasting intermittent demand is a rather difficult task but of critical importance for corresponding cost savings. The current study aims to examine the empirical outcomes of three heuristics towards the modification of established intermittent demand forecasting approaches...
The recognition heuristic: A decade of research
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Gerd Gigerenzer
2011-02-01
Full Text Available The recognition heuristic exploits the basic psychological capacity for recognition in order to make inferences about unknown quantities in the world. In this article, we review and clarify issues that emerged from our initial work (Goldstein and Gigerenzer, 1999, 2002, including the distinction between a recognition and an evaluation process. There is now considerable evidence that (i the recognition heuristic predicts the inferences of a substantial proportion of individuals consistently, even in the presence of one or more contradicting cues, (ii people are adaptive decision makers in that accordance increases with larger recognition validity and decreases in situations when the validity is low or wholly indeterminable, and (iii in the presence of contradicting cues, some individuals appear to select different strategies. Little is known about these individual differences, or how to precisely model the alternative strategies. Although some researchers have attributed judgments inconsistent with the use of the recognition heuristic to compensatory processing, little research on such compensatory models has been reported. We discuss extensions of the recognition model, open questions, unanticipated results, and the surprising predictive power of recognition in forecasting.
When decision heuristics and science collide.
Yu, Erica C; Sprenger, Amber M; Thomas, Rick P; Dougherty, Michael R
2014-04-01
The ongoing discussion among scientists about null-hypothesis significance testing and Bayesian data analysis has led to speculation about the practices and consequences of "researcher degrees of freedom." This article advances this debate by asking the broader questions that we, as scientists, should be asking: How do scientists make decisions in the course of doing research, and what is the impact of these decisions on scientific conclusions? We asked practicing scientists to collect data in a simulated research environment, and our findings show that some scientists use data collection heuristics that deviate from prescribed methodology. Monte Carlo simulations show that data collection heuristics based on p values lead to biases in estimated effect sizes and Bayes factors and to increases in both false-positive and false-negative rates, depending on the specific heuristic. We also show that using Bayesian data collection methods does not eliminate these biases. Thus, our study highlights the little appreciated fact that the process of doing science is a behavioral endeavor that can bias statistical description and inference in a manner that transcends adherence to any particular statistical framework.
The affect heuristic in occupational safety.
Savadori, Lucia; Caovilla, Jessica; Zaniboni, Sara; Fraccaroli, Franco
2015-07-08
The affect heuristic is a rule of thumb according to which, in the process of making a judgment or decision, people use affect as a cue. If a stimulus elicits positive affect then risks associated to that stimulus are viewed as low and benefits as high; conversely, if the stimulus elicits negative affect, then risks are perceived as high and benefits as low. The basic tenet of this study is that affect heuristic guides worker's judgment and decision making in a risk situation. The more the worker likes her/his organization the less she/he will perceive the risks as high. A sample of 115 employers and 65 employees working in small family agricultural businesses completed a questionnaire measuring perceived safety costs, psychological safety climate, affective commitment and safety compliance. A multi-sample structural analysis supported the thesis that safety compliance can be explained through an affect-based heuristic reasoning, but only for employers. Positive affective commitment towards their family business reduced employers' compliance with safety procedures by increasing the perceived cost of implementing them.
A Matheuristic for the Liner Shipping Network Design Problem with Transit Time Restrictions
DEFF Research Database (Denmark)
Brouer, Berit Dangaard; Desaulniers, Guy; Karsten, Christian Vad
2015-01-01
We present a mathematical model for the liner shipping network design problem with transit time restrictions on the cargo flow. We extend an existing matheuristic for the liner shipping network design problem to consider transit time restrictions. The matheuristic is an improvement heuristic, where...... an integer program is solved iteratively as a move operator in a large-scale neighborhood search. To assess the effects of insertions/removals of port calls, flow and revenue changes are estimated for relevant commodities along with an estimation of the change in the vessel cost. Computational results...
Kheiri, Ahmed; Keedwell, Ed
2017-01-01
Operations research is a well-established field that uses computational systems to support decisions in business and public life. Good solutions to operations research problems can make a large difference to the efficient running of businesses and organisations and so the field often searches for new methods to improve these solutions. The high school timetabling problem is an example of an operations research problem and is a challenging task which requires assigning events and resources to time slots subject to a set of constraints. In this article, a new sequence-based selection hyper-heuristic is presented that produces excellent results on a suite of high school timetabling problems. In this study, we present an easy-to-implement, easy-to-maintain, and effective sequence-based selection hyper-heuristic to solve high school timetabling problems using a benchmark of unified real-world instances collected from different countries. We show that with sequence-based methods, it is possible to discover new best known solutions for a number of the problems in the timetabling domain. Through this investigation, the usefulness of sequence-based selection hyper-heuristics has been demonstrated and the capability of these methods has been shown to exceed the state of the art.
Search Space Characterization for a Telescope Scheduling Application
Bresina, John; Drummond, Mark; Swanson, Keith; Friedland, Peter (Technical Monitor)
1994-01-01
This paper presents a technique for statistically characterizing a search space and demonstrates the use of this technique within a practical telescope scheduling application. The characterization provides the following: (i) an estimate of the search space size, (ii) a scaling technique for multi-attribute objective functions and search heuristics, (iii) a "quality density function" for schedules in a search space, (iv) a measure of a scheduler's performance, and (v) support for constructing and tuning search heuristics. This paper describes the random sampling algorithm used to construct this characterization and explains how it can be used to produce this information. As an example, we include a comparative analysis of an heuristic dispatch scheduler and a look-ahead scheduler that performs greedy search.
Heuristic space diversity management in a meta-hyper-heuristic framework
CSIR Research Space (South Africa)
Grobler, J
2014-07-01
Full Text Available stream_source_info Grobler1_2014.pdf.txt stream_content_type text/plain stream_size 35657 Content-Encoding UTF-8 stream_name Grobler1_2014.pdf.txt Content-Type text/plain; charset=UTF-8 Heuristic Space Diversity... Management in a Meta-Hyper-Heuristic Framework Jacomine Grobler1 and Andries P. Engelbrecht2 1Department of Industrial and Systems Engineering University of Pretoria and Council for Scientific and Industrial Research Email: jacomine.grobler@gmail.com 2...
Cultural heuristics in risk assessment of HIV/AIDS.
Bailey, Ajay; Hutter, Inge
2006-01-01
Behaviour change models in HIV prevention tend to consider that risky sexual behaviours reflect risk assessments and that by changing risk assessments behaviour can be changed. Risk assessment is however culturally constructed. Individuals use heuristics or bounded cognitive devices derived from broader cultural meaning systems to rationalize uncertainty. In this study, we identify some of the cultural heuristics used by migrant men in Goa, India to assess their risk of HIV infection from different sexual partners. Data derives from a series of in-depth interviews and a locally informed survey. Cultural heuristics identified include visual heuristics, heuristics of gender roles, vigilance and trust. The paper argues that, for more culturally informed HIV/AIDS behaviour change interventions, knowledge of cultural heuristics is essential.
Identifying product development crises: The potential of adaptive heuristics
DEFF Research Database (Denmark)
Münzberger, C.; Stingl, Verena; Oehmen, Josef
2017-01-01
This paper introduces adaptive heuristics as a tool to identify crises in design projects and highlights potential applications of these heuristics as decision support tool for crisis identification. Crises may emerge slowly or suddenly, and often have ambiguous signals. Thus the identification...... of a project crisis is often difficult. Yet, to allow fast crisis response, timely identification is critical for successful crisis management. Adaptive heuristics are judgement strategies that can strive in circumstances of limited and ambiguous information. This article presents a theoretical proposition...... for the application of heuristics in design sciences. To achieve this, the paper compares crises to 'business as usual', and presents sixteen indicators for emerging crises. These indicators are potential cues for adaptive heuristics. Specifically three adaptive heuristics, One-single-cue, Fast...
Chiroma, Haruna; Abdul-kareem, Sameem; Khan, Abdullah; Nawi, Nazri Mohd.; Gital, Abdulsalam Ya’u; Shuib, Liyana; Abubakar, Adamu I.; Rahman, Muhammad Zubair; Herawan, Tutut
2015-01-01
Background Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research. Methods/Findings The OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods. Conclusion An accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks—hence, reducing global warming. The policy implications are discussed in the paper. PMID:26305483
Becker, Stefanie I; Grubert, Anna; Dux, Paul E
2014-11-15
In visual search, responses are slowed, from one trial to the next, both when the target dimension changes (e.g., from a color target to a size target) and when the target feature changes (e.g., from a red target to a green target) relative to being repeated across trials. The present study examined whether such feature and dimension switch costs can be attributed to the same underlying mechanism(s). Contrary to this contention, an EEG study showed that feature changes influenced visual selection of the target (i.e., delayed N2pc onset), whereas dimension changes influenced the later process of response selection (i.e., delayed s-LRP onset). An fMRI study provided convergent evidence for the two-system view: Compared with repetitions, feature changes led to increased activation in the occipital cortex, and superior and inferior parietal lobules, which have been implicated in spatial attention. By contrast, dimension changes led to activation of a fronto-posterior network that is primarily linked with response selection (i.e., pre-motor cortex, supplementary motor area and frontal areas). Taken together, the results suggest that feature and dimension switch costs are based on different processes. Specifically, whereas target feature changes delay attention shifts to the target, target dimension changes interfere with later response selection operations. Crown Copyright © 2014. Published by Elsevier Inc. All rights reserved.
Chiroma, Haruna; Abdul-kareem, Sameem; Khan, Abdullah; Nawi, Nazri Mohd; Gital, Abdulsalam Ya'u; Shuib, Liyana; Abubakar, Adamu I; Rahman, Muhammad Zubair; Herawan, Tutut
2015-01-01
Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research. The OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods. An accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks--hence, reducing global warming. The policy implications are discussed in the paper.
Directory of Open Access Journals (Sweden)
Haruna Chiroma
Full Text Available Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2 from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC CO2 emissions from petroleum consumption have motivated this research.The OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods.An accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks--hence, reducing global warming. The policy implications are discussed in the paper.
A novel hybrid meta-heuristic technique applied to the well-known benchmark optimization problems
Abtahi, Amir-Reza; Bijari, Afsane
2017-09-01
In this paper, a hybrid meta-heuristic algorithm, based on imperialistic competition algorithm (ICA), harmony search (HS), and simulated annealing (SA) is presented. The body of the proposed hybrid algorithm is based on ICA. The proposed hybrid algorithm inherits the advantages of the process of harmony creation in HS algorithm to improve the exploitation phase of the ICA algorithm. In addition, the proposed hybrid algorithm uses SA to make a balance between exploration and exploitation phases. The proposed hybrid algorithm is compared with several meta-heuristic methods, including genetic algorithm (GA), HS, and ICA on several well-known benchmark instances. The comprehensive experiments and statistical analysis on standard benchmark functions certify the superiority of the proposed method over the other algorithms. The efficacy of the proposed hybrid algorithm is promising and can be used in several real-life engineering and management problems.
Efficient heuristic algorithm used for optimal capacitor placement in distribution systems
Energy Technology Data Exchange (ETDEWEB)
Segura, Silvio; Rider, Marcos J. [Department of Electric Energy Systems, University of Campinas, Campinas, Sao Paulo (Brazil); Romero, Ruben [Faculty of Engineering of Ilha Solteira, Paulista State University, Ilha Solteira, Sao Paulo (Brazil)
2010-01-15
An efficient heuristic algorithm is presented in this work in order to solve the optimal capacitor placement problem in radial distribution systems. The proposal uses the solution from the mathematical model after relaxing the integrality of the discrete variables as a strategy to identify the most attractive bus to add capacitors to each step of the heuristic algorithm. The relaxed mathematical model is a non-linear programming problem and is solved using a specialized interior point method. The algorithm still incorporates an additional strategy of local search that enables the finding of a group of quality solutions after small alterations in the optimization strategy. Proposed solution methodology has been implemented and tested in known electric systems getting a satisfactory outcome compared with metaheuristic methods. The tests carried out in electric systems known in specialized literature reveal the satisfactory outcome of the proposed algorithm compared with metaheuristic methods. (author)
Directory of Open Access Journals (Sweden)
Tashkova Katerina
2011-10-01
Full Text Available Abstract Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA, particle-swarm optimization (PSO, and differential evolution (DE, as well as a local-search derivative-based algorithm 717 (A717 to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE clearly and significantly outperform the local derivative-based method (A717. Among the three meta-heuristics, differential evolution (DE performs best in terms of the objective function, i.e., reconstructing the output, and in terms of
2011-01-01
Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These
Proposing New Heuristic Approaches for Preventive Maintenance Scheduling
Directory of Open Access Journals (Sweden)
majid Esmailian
2013-08-01
Full Text Available The purpose of preventive maintenance management is to perform a series of tasks that prevent or minimize production breakdowns and improve reliability of production facilities. An important objective of preventive maintenance management is to minimize downtime of production facilities. In order to accomplish this objective, personnel should efficiently allocate resources and determine an effective maintenance schedule. Gopalakrishnan (1997 developed a mathematical model and four heuristic approaches to solve the preventive maintenance scheduling problem of assigning skilled personnel to work with tasks that require a set of corresponding skills. However, there are several limitations in the prior work in this area of research. The craft combination problem has not been solved because the craft combination is assumed as given. The craft combination problem concerns the computation of all combinations of assigning multi skilled workers to accomplishment of a particular task. In fact, determining craft combinations is difficult because of the exponential number of craft combinations that are possible. This research provides a heuristic approach for determining the craft combination and four new heuristic approach solution for the preventive maintenance scheduling problem with multi skilled workforce constraints. In order to examine the new heuristic approach and to compare the new heuristic approach with heuristic approach of Gopalakrishnan (1997, 81 standard problems have been generated based on the criterion suggested by from Gopalakrishnan (1997. The average solution quality (SQ of the new heuristic approaches is 1.86% and in old heuristic approaches is 8.32%. The solution time of new heuristic approaches are shorter than old heuristic approaches. The solution time of new heuristic approaches is 0.78 second and old heuristic approaches is 6.43 second, but the solution time of mathematical model provided by Gopalakrishnan (1997 is 152 second.
A heuristic evaluation of the Facebook's advertising tool beacon
Jamal, A; Cole, M
2009-01-01
Interface usability is critical to the successful adoption of information systems. The aim of this study is to evaluate interface of Facebook's advertising tool Beacon by using privacy heuristics [4]. Beacon represents an interesting case study because of the negative media and user backlash it received. The findings of heuristic evaluation suggest violation of privacy heuristics [4]. Here, analysis identified concerns about user choice and consent, integrity and security of data, and awarene...
Why less can be more: A Bayesian framework for heuristics
Parpart, Paula
2017-01-01
When making decisions under uncertainty, one common view is that people rely on simple heuristics that deliberately ignore information. One of the greatest puzzles in cognitive science concerns why heuristics can sometimes outperform full-information models, such as linear regression, which make full use of the available information. In this thesis, I will contribute the novel idea that heuristics can be thought of as embodying extreme Bayesian priors. Thereby, an explanation for less-is-more...
Discovery of IPV6 Router Interface Addresses via Heuristic Methods
2015-09-01
NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS DISCOVERY OF IPV6 ROUTER INTERFACE ADDRESSES VIA HEURISTIC METHODS by Matthew D. Gray September...AND SUBTITLE DISCOVERY OF IPV6 ROUTER INTERFACE ADDRESSES VIA HEURISTIC METHODS 5. FUNDING NUMBERS CNS-1111445 6. AUTHOR(S) Matthew D. Gray 7...focuses on IPv6 router infrastructure and examines the possibility of using heuristic methods in order to discover IPv6 router interfaces. We consider two
An extension for dynamic lot-sizing heuristics
Directory of Open Access Journals (Sweden)
Fabian G. Beck
2015-01-01
Full Text Available This paper presents an efficient procedure to extend dynamic lot-sizing heuristics that has been overlooked by inventory management literature and practice. Its intention is to show that the extension improves the results of basic heuristics significantly. We first present a comprehensive description of the extension procedure and then test its performance in an extensive numerical study. Our analysis shows that the extension is an efficient tool to improve basic dynamic lot-sizing heuristics. The results of the paper may be used in inventory management to assist researchers in selecting dynamic lot-sizing heuristics and may be of help for practitioners as decision support.
Heuristic theory of positron-helium scattering.
Drachman, R. J.
1971-01-01
An error in a previous modified adiabatic approximation (Drachman, 1966), due to a lack of generality in the form of the short-range correlation part of the wave function for L greater than zero, is corrected heuristically by allowing the monopole suppression parameter to depend on L. An L-dependent local potential is constructed to fit the well-known positron-hydrogen s, p, and d wave phase shifts below the rearrangement threshold. The same form of potential yields a positron-helium cross-section in agreement with a recent experimental measurement near threshold.
Advances in heuristic signal processing and applications
Chatterjee, Amitava; Siarry, Patrick
2013-01-01
There have been significant developments in the design and application of algorithms for both one-dimensional signal processing and multidimensional signal processing, namely image and video processing, with the recent focus changing from a step-by-step procedure of designing the algorithm first and following up with in-depth analysis and performance improvement to instead applying heuristic-based methods to solve signal-processing problems. In this book the contributing authors demonstrate both general-purpose algorithms and those aimed at solving specialized application problems, with a spec
Rearrangement moves on rooted phylogenetic networks.
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Philippe Gambette
2017-08-01
Full Text Available Phylogenetic tree reconstruction is usually done by local search heuristics that explore the space of the possible tree topologies via simple rearrangements of their structure. Tree rearrangement heuristics have been used in combination with practically all optimization criteria in use, from maximum likelihood and parsimony to distance-based principles, and in a Bayesian context. Their basic components are rearrangement moves that specify all possible ways of generating alternative phylogenies from a given one, and whose fundamental property is to be able to transform, by repeated application, any phylogeny into any other phylogeny. Despite their long tradition in tree-based phylogenetics, very little research has gone into studying similar rearrangement operations for phylogenetic network-that is, phylogenies explicitly representing scenarios that include reticulate events such as hybridization, horizontal gene transfer, population admixture, and recombination. To fill this gap, we propose "horizontal" moves that ensure that every network of a certain complexity can be reached from any other network of the same complexity, and "vertical" moves that ensure reachability between networks of different complexities. When applied to phylogenetic trees, our horizontal moves-named rNNI and rSPR-reduce to the best-known moves on rooted phylogenetic trees, nearest-neighbor interchange and rooted subtree pruning and regrafting. Besides a number of reachability results-separating the contributions of horizontal and vertical moves-we prove that rNNI moves are local versions of rSPR moves, and provide bounds on the sizes of the rNNI neighborhoods. The paper focuses on the most biologically meaningful versions of phylogenetic networks, where edges are oriented and reticulation events clearly identified. Moreover, our rearrangement moves are robust to the fact that networks with higher complexity usually allow a better fit with the data. Our goal is to provide
Rearrangement moves on rooted phylogenetic networks.
Gambette, Philippe; van Iersel, Leo; Jones, Mark; Lafond, Manuel; Pardi, Fabio; Scornavacca, Celine
2017-08-01
Phylogenetic tree reconstruction is usually done by local search heuristics that explore the space of the possible tree topologies via simple rearrangements of their structure. Tree rearrangement heuristics have been used in combination with practically all optimization criteria in use, from maximum likelihood and parsimony to distance-based principles, and in a Bayesian context. Their basic components are rearrangement moves that specify all possible ways of generating alternative phylogenies from a given one, and whose fundamental property is to be able to transform, by repeated application, any phylogeny into any other phylogeny. Despite their long tradition in tree-based phylogenetics, very little research has gone into studying similar rearrangement operations for phylogenetic network-that is, phylogenies explicitly representing scenarios that include reticulate events such as hybridization, horizontal gene transfer, population admixture, and recombination. To fill this gap, we propose "horizontal" moves that ensure that every network of a certain complexity can be reached from any other network of the same complexity, and "vertical" moves that ensure reachability between networks of different complexities. When applied to phylogenetic trees, our horizontal moves-named rNNI and rSPR-reduce to the best-known moves on rooted phylogenetic trees, nearest-neighbor interchange and rooted subtree pruning and regrafting. Besides a number of reachability results-separating the contributions of horizontal and vertical moves-we prove that rNNI moves are local versions of rSPR moves, and provide bounds on the sizes of the rNNI neighborhoods. The paper focuses on the most biologically meaningful versions of phylogenetic networks, where edges are oriented and reticulation events clearly identified. Moreover, our rearrangement moves are robust to the fact that networks with higher complexity usually allow a better fit with the data. Our goal is to provide a solid basis for
Directory of Open Access Journals (Sweden)
M. Metz
2011-02-01
Full Text Available The availability of both global and regional elevation datasets acquired by modern remote sensing technologies provides an opportunity to significantly improve the accuracy of stream mapping, especially in remote, hard to reach regions. Stream extraction from digital elevation models (DEMs is based on computation of flow accumulation, a summary parameter that poses performance and accuracy challenges when applied to large, noisy DEMs generated by remote sensing technologies. Robust handling of DEM depressions is essential for reliable extraction of connected drainage networks from this type of data. The least-cost flow routing method implemented in GRASS GIS as the module r.watershed was redesigned to significantly improve its speed, functionality, and memory requirements and make it an efficient tool for stream mapping and watershed analysis from large DEMs. To evaluate its handling of large depressions, typical for remote sensing derived DEMs, three different methods were compared: traditional sink filling, impact reduction approach, and least-cost path search. The comparison was performed using the Shuttle Radar Topographic Mission (SRTM and Interferometric Synthetic Aperture Radar for Elevation (IFSARE datasets covering central Panama at 90 m and 10 m resolutions, respectively. The accuracy assessment was based on ground control points acquired by GPS and reference points digitized from Landsat imagery along segments of selected Panamanian rivers. The results demonstrate that the new implementation of the least-cost path method is significantly faster than the original version, can cope with massive datasets, and provides the most accurate results in terms of stream locations validated against reference points.
Memorability in Context: A Heuristic Story.
Geurten, Marie; Meulemans, Thierry; Willems, Sylvie
2015-01-01
We examined children's ability to employ a metacognitive heuristic based on memorability expectations to reduce false recognitions, and explored whether these expectations depend on the context in which the items are presented. Specifically, 4-, 6-, and 9-year-old children were presented with high-, medium-, and low-memorability words, either mixed together (Experiment 1) or separated into two different lists (Experiment 2). Results revealed that only children with a higher level of executive functioning (9-year-olds) used the memorability-based heuristic when all types of items were presented within the same list. However, all children, regardless of age or executive level, implemented the metacognitive rule when high- and low-memorability words were presented in two separate lists. Moreover, the results of Experiment 2 showed that participants processed medium-memorability words more conservatively when they were presented in a low- than in a high-memorability list, suggesting that children's memorability expectations are sensitive to list-context effects.
Tan, Maxine; Pu, Jiantao; Zheng, Bin
2014-01-01
In the field of computer-aided mammographic mass detection, many different features and classifiers have been tested. Frequently, the relevant features and optimal topology for the artificial neural network (ANN)-based approaches at the classification stage are unknown, and thus determined by trial-and-error experiments. In this study, we analyzed a classifier that evolves ANNs using genetic algorithms (GAs), which combines feature selection with the learning task. The classifier named "Phased Searching with NEAT in a Time-Scaled Framework" was analyzed using a dataset with 800 malignant and 800 normal tissue regions in a 10-fold cross-validation framework. The classification performance measured by the area under a receiver operating characteristic (ROC) curve was 0.856 ± 0.029. The result was also compared with four other well-established classifiers that include fixed-topology ANNs, support vector machines (SVMs), linear discriminant analysis (LDA), and bagged decision trees. The results show that Phased Searching outperformed the LDA and bagged decision tree classifiers, and was only significantly outperformed by SVM. Furthermore, the Phased Searching method required fewer features and discarded superfluous structure or topology, thus incurring a lower feature computational and training and validation time requirement. Analyses performed on the network complexities evolved by Phased Searching indicate that it can evolve optimal network topologies based on its complexification and simplification parameter selection process. From the results, the study also concluded that the three classifiers - SVM, fixed-topology ANN, and Phased Searching with NeuroEvolution of Augmenting Topologies (NEAT) in a Time-Scaled Framework - are performing comparably well in our mammographic mass detection scheme.
Directory of Open Access Journals (Sweden)
Luhur H.S.
2014-03-01
Full Text Available This paper is focusing on the development of a mobile application for searching restaurants and promotions with location based and social networking features. The main function of the application is to search restaurant information. Other functions are also available in this application such as add restaurant, add promotion, add photo, add food review, and other features including social networking features. The restaurant and promotion searching application will be developed under Android platform. Upon completion of this paper, heuristic evaluation and usability testing have been conducted. The result of both testing shows that the application is highly usable. Even though there are some usability problems discovered, the problems can be eliminated immediately by implementing the recommendations from the expert evaluators and the users as the testers of the application. Further improvement made to the application will ensure that the application can really be beneficial for the users of the application.
Luhur, H. S.; Widjaja, N. D.
2014-03-01
This paper is focusing on the development of a mobile application for searching restaurants and promotions with location based and social networking features. The main function of the application is to search restaurant information. Other functions are also available in this application such as add restaurant, add promotion, add photo, add food review, and other features including social networking features. The restaurant and promotion searching application will be developed under Android platform. Upon completion of this paper, heuristic evaluation and usability testing have been conducted. The result of both testing shows that the application is highly usable. Even though there are some usability problems discovered, the problems can be eliminated immediately by implementing the recommendations from the expert evaluators and the users as the testers of the application. Further improvement made to the application will ensure that the application can really be beneficial for the users of the application.
Aungkulanon, P.; Luangpaiboon, P.
2010-10-01
Nowadays, the engineering problem systems are large and complicated. An effective finite sequence of instructions for solving these problems can be categorised into optimisation and meta-heuristic algorithms. Though the best decision variable levels from some sets of available alternatives cannot be done, meta-heuristics is an alternative for experience-based techniques that rapidly help in problem solving, learning and discovery in the hope of obtaining a more efficient or more robust procedure. All meta-heuristics provide auxiliary procedures in terms of their own tooled box functions. It has been shown that the effectiveness of all meta-heuristics depends almost exclusively on these auxiliary functions. In fact, the auxiliary procedure from one can be implemented into other meta-heuristics. Well-known meta-heuristics of harmony search (HSA) and shuffled frog-leaping algorithms (SFLA) are compared with their hybridisations. HSA is used to produce a near optimal solution under a consideration of the perfect state of harmony of the improvisation process of musicians. A meta-heuristic of the SFLA, based on a population, is a cooperative search metaphor inspired by natural memetics. It includes elements of local search and global information exchange. This study presents solution procedures via constrained and unconstrained problems with different natures of single and multi peak surfaces including a curved ridge surface. Both meta-heuristics are modified via variable neighbourhood search method (VNSM) philosophy including a modified simplex method (MSM). The basic idea is the change of neighbourhoods during searching for a better solution. The hybridisations proceed by a descent method to a local minimum exploring then, systematically or at random, increasingly distant neighbourhoods of this local solution. The results show that the variant of HSA with VNSM and MSM seems to be better in terms of the mean and variance of design points and yields.
A genetic algorithm selection perturbative hyper-heuristic for solving ...
African Journals Online (AJOL)
The benefit of incorporating hill-climbing into operators for school timetabling is evident from previous research in this domain [3, 13, 15, 33].Versions of these ...... One of the disadvantages of hyper-heuristics is the higher runtimes as a result of having to construct a solution to evaluate each heuristic combination. This is ...
Heuristic methods for shared backup path protection planning
DEFF Research Database (Denmark)
Haahr, Jørgen Thorlund; Stidsen, Thomas Riis; Zachariasen, Martin
2012-01-01
present heuristic algorithms and lower bound methods for the SBPP planning problem. Experimental results show that the heuristic algorithms are able to find good quality solutions in minutes. A solution gap of less than 3.5% was achieved for more than half of the benchmark instances (and a gap of less...
HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN
While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...
On the empirical performance of (T,s,S) heuristics
Babai, M. Zied; Syntetos, Aris A.; Teunter, Ruud
2010-01-01
The periodic (T,s,S) policies have received considerable attention from the academic literature. Determination of the optimal parameters is computationally prohibitive, and a number of heuristic procedures have been put forward. However, these heuristics have never been compared in an extensive
Providing Automatic Support for Heuristic Rules of Methods
Tekinerdogan, B.; Aksit, Mehmet; Demeyer, Serge; Bosch, H.G.P.; Bosch, Jan
In method-based software development, software engineers create artifacts based on the heuristic rules of the adopted method. Most CASE tools, however, do not actively assist software engineers in applying the heuristic rules. To provide an active support, the rules must be formalized, implemented