Optimization of single channel glazed photovoltaic thermal (PVT) array using Evolutionary Algorithm (EA) and carbon credit earned by the optimized array

International Nuclear Information System (INIS)

Singh, Sonveer; Agrawal, Sanjay; Gadh, Rajit

2015-01-01

Highlights: • Optimization of SCGPVT array using Evolutionary Algorithm. • The overall exergy gain is maximized with an Evolutionary Algorithm. • Annual Performance has been evaluated for New Delhi (India). • There are improvement in results than the model given in literature. • Carbon credit analysis has been done. - Abstract: In this paper, work is carried out in three steps. In the first step, optimization of single channel glazed photovoltaic thermal (SCGPVT) array has been done with an Evolutionary Algorithm (EA) keeping the overall exergy gain is an objective function of the SCGPVT array. For maximization of overall exergy gain, total seven design variables have been optimized such as length of the channel (L), mass flow rate of flowing fluid (m_F), velocity of flowing fluid (V_F), convective heat transfer coefficient through the tedlar (U_T), overall heat transfer coefficient between solar cell to ambient through glass cover (U_S_C_A_G), overall back loss heat transfer coefficient from flowing fluid to ambient (U_F_A) and convective heat transfer coefficient of tedlar (h_T). It has been observed that the instant overall exergy gain obtained from optimized system is 1.42 kW h, which is 87.86% more than the overall exergy gain of a un-optimized system given in literature. In the second step, overall exergy gain and overall thermal gain of SCGPVT array has been evaluated annually and there are 69.52% and 88.05% improvement in annual overall exergy gain and annual overall thermal gain respectively than the un-optimized system for the same input irradiance and ambient temperature. In the third step, carbon credit earned by the optimized SCGPVT array has also been evaluated as per norms of Kyoto Protocol Bangalore climatic conditions.

Synthesizing multi-objective H2/H-infinity dynamic controller using evolutionary algorithms

DEFF Research Database (Denmark)

Pedersen, Gerulf; Langballe, A.S.; Wisniewski, Rafal

This paper covers the design of an Evolutionary Algorithm (EA), which should be able to synthesize a mixed H2/H-infinity. It will be shown how a system can be expressed as Matrix Inequalities (MI) and these will then be used in the design of the EA. The main objective is to examine whether a mixed...... H2/H-infinity controller is feasible, and if so, how the optimal mixed controller might befound....

Physical Properties and Evolutionary States of EA-type Eclipsing Binaries Observed by LAMOST

Science.gov (United States)

Qian, S.-B.; Zhang, J.; He, J.-J.; Zhu, L.-Y.; Zhao, E.-G.; Shi, X.-D.; Zhou, X.; Han, Z.-T.

2018-03-01

About 3196 EA-type binaries (EAs) were observed by LAMOST by 2017 June 16 and their spectral types were derived. Meanwhile, the stellar atmospheric parameters of 2020 EAs were determined. In this paper, those EAs are cataloged and their physical properties and evolutionary states are investigated. The period distribution of EAs suggests that the period limit of tidal locking for the close binaries is about 6 days. It is found that the metallicity of EAs is higher than that of EW-type binaries (EWs), indicating that EAs are generally younger than EWs and they are the progenitors of EWs. The metallicities of long-period EWs (0.4values of Log (g) are usually smaller than those of EAs. These support the evolutionary process that EAs evolve into long-period EWs through the combination of angular momentum loss (AML) via magnetic braking and case A mass transfer. For short-period EWs, their metallicities are lower than those of EAs, while their gravitational accelerations are higher. These reveal that they may be formed from cool short-period EAs through AML via magnetic braking with little mass transfer. For some EWs with high metallicities, they may be contaminated by material from the evolution of unseen neutron stars and black holes or they have third bodies that may help them to form rapidly through a short timescale of pre-contact evolution. The present investigation suggests that the modern EW populations may have formed through a combination of these mechanisms.

A Comparative Study of Differential Evolution, Particle Swarm Optimization, and Evolutionary Algorithms on Numerical Benchmark Problems

DEFF Research Database (Denmark)

Vesterstrøm, Jacob Svaneborg; Thomsen, Rene

2004-01-01

Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance...... in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally...... outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA....

Performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches in VQ codebook generation for image compression

Science.gov (United States)

Tsai, Jinn-Tsong; Chou, Ping-Yi; Chou, Jyh-Horng

2015-11-01

The aim of this study is to generate vector quantisation (VQ) codebooks by integrating principle component analysis (PCA) algorithm, Linde-Buzo-Gray (LBG) algorithm, and evolutionary algorithms (EAs). The EAs include genetic algorithm (GA), particle swarm optimisation (PSO), honey bee mating optimisation (HBMO), and firefly algorithm (FF). The study is to provide performance comparisons between PCA-EA-LBG and PCA-LBG-EA approaches. The PCA-EA-LBG approaches contain PCA-GA-LBG, PCA-PSO-LBG, PCA-HBMO-LBG, and PCA-FF-LBG, while the PCA-LBG-EA approaches contain PCA-LBG, PCA-LBG-GA, PCA-LBG-PSO, PCA-LBG-HBMO, and PCA-LBG-FF. All training vectors of test images are grouped according to PCA. The PCA-EA-LBG used the vectors grouped by PCA as initial individuals, and the best solution gained by the EAs was given for LBG to discover a codebook. The PCA-LBG approach is to use the PCA to select vectors as initial individuals for LBG to find a codebook. The PCA-LBG-EA used the final result of PCA-LBG as an initial individual for EAs to find a codebook. The search schemes in PCA-EA-LBG first used global search and then applied local search skill, while in PCA-LBG-EA first used local search and then employed global search skill. The results verify that the PCA-EA-LBG indeed gain superior results compared to the PCA-LBG-EA, because the PCA-EA-LBG explores a global area to find a solution, and then exploits a better one from the local area of the solution. Furthermore the proposed PCA-EA-LBG approaches in designing VQ codebooks outperform existing approaches shown in the literature.

Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

Science.gov (United States)

Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

2010-01-01

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

An evolutionary algorithm technique for intelligence, surveillance, and reconnaissance plan optimization

Science.gov (United States)

Langton, John T.; Caroli, Joseph A.; Rosenberg, Brad

2008-04-01

To support an Effects Based Approach to Operations (EBAO), Intelligence, Surveillance, and Reconnaissance (ISR) planners must optimize collection plans within an evolving battlespace. A need exists for a decision support tool that allows ISR planners to rapidly generate and rehearse high-performing ISR plans that balance multiple objectives and constraints to address dynamic collection requirements for assessment. To meet this need we have designed an evolutionary algorithm (EA)-based "Integrated ISR Plan Analysis and Rehearsal System" (I2PARS) to support Effects-based Assessment (EBA). I2PARS supports ISR mission planning and dynamic replanning to coordinate assets and optimize their routes, allocation and tasking. It uses an evolutionary algorithm to address the large parametric space of route-finding problems which is sometimes discontinuous in the ISR domain because of conflicting objectives such as minimizing asset utilization yet maximizing ISR coverage. EAs are uniquely suited for generating solutions in dynamic environments and also allow user feedback. They are therefore ideal for "streaming optimization" and dynamic replanning of ISR mission plans. I2PARS uses the Non-dominated Sorting Genetic Algorithm (NSGA-II) to automatically generate a diverse set of high performing collection plans given multiple objectives, constraints, and assets. Intended end users of I2PARS include ISR planners in the Combined Air Operations Centers and Joint Intelligence Centers. Here we show the feasibility of applying the NSGA-II algorithm and EAs in general to the ISR planning domain. Unique genetic representations and operators for optimization within the ISR domain are presented along with multi-objective optimization criteria for ISR planning. Promising results of the I2PARS architecture design, early software prototype, and limited domain testing of the new algorithm are discussed. We also present plans for future research and development, as well as technology

Taxon ordering in phylogenetic trees by means of evolutionary algorithms

Directory of Open Access Journals (Sweden)

Cerutti Francesco

2011-07-01

Full Text Available Abstract Background In in a typical "left-to-right" phylogenetic tree, the vertical order of taxa is meaningless, as only the branch path between them reflects their degree of similarity. To make unresolved trees more informative, here we propose an innovative Evolutionary Algorithm (EA method to search the best graphical representation of unresolved trees, in order to give a biological meaning to the vertical order of taxa. Methods Starting from a West Nile virus phylogenetic tree, in a (1 + 1-EA we evolved it by randomly rotating the internal nodes and selecting the tree with better fitness every generation. The fitness is a sum of genetic distances between the considered taxon and the r (radius next taxa. After having set the radius to the best performance, we evolved the trees with (λ + μ-EAs to study the influence of population on the algorithm. Results The (1 + 1-EA consistently outperformed a random search, and better results were obtained setting the radius to 8. The (λ + μ-EAs performed as well as the (1 + 1, except the larger population (1000 + 1000. Conclusions The trees after the evolution showed an improvement both of the fitness (based on a genetic distance matrix, then close taxa are actually genetically close, and of the biological interpretation. Samples collected in the same state or year moved close each other, making the tree easier to interpret. Biological relationships between samples are also easier to observe.

Multilevel Evolutionary Algorithm that Optimizes the Structure of Scale-Free Networks for the Promotion of Cooperation in the Prisoner's Dilemma game.

Science.gov (United States)

Liu, Penghui; Liu, Jing

2017-06-28

Understanding the emergence of cooperation has long been a challenge across disciplines. Even if network reciprocity reflected the importance of population structure in promoting cooperation, it remains an open question how population structures can be optimized, thereby enhancing cooperation. In this paper, we attempt to apply the evolutionary algorithm (EA) to solve this highly complex problem. However, as it is hard to evaluate the fitness (cooperation level) of population structures, simply employing the canonical evolutionary algorithm (EA) may fail in optimization. Thus, we propose a new EA variant named mlEA-C PD -SFN to promote the cooperation level of scale-free networks (SFNs) in the Prisoner's Dilemma Game (PDG). Meanwhile, to verify the preceding conclusions may not be applied to this problem, we also provide the optimization results of the comparative experiment (EA cluster ), which optimizes the clustering coefficient of structures. Even if preceding research concluded that highly clustered scale-free networks enhance cooperation, we find EA cluster does not perform desirably, while mlEA-C PD -SFN performs efficiently in different optimization environments. We hope that mlEA-C PD -SFN may help promote the structure of species in nature and that more general properties that enhance cooperation can be learned from the output structures.

Models for Evolutionary Algorithms and Their Applications in System Identification and Control Optimization

DEFF Research Database (Denmark)

Ursem, Rasmus Kjær

population and many generations, which essentially turns the problem into a series of related static problems. To our surprise, the control problem could easily be solved when optimized like this. To further examine this, we compared the EA with a particle swarm and a local search approach, which we...... simulate an evolutionary process where the goal is to evolve solutions by means of crossover, mutation, and selection based on their quality (fitness) with respect to the optimization problem at hand. Evolutionary algorithms (EAs) are highly relevant for industrial applications, because they are capable...... of handling problems with non-linear constraints, multiple objectives, and dynamic components – properties that frequently appear in real-world problems. This thesis presents research in three fundamental areas of EC; fitness function design, methods for parameter control, and techniques for multimodal...

Application of evolutionary algorithms for multi-objective optimization in VLSI and embedded systems

CERN Document Server

2015-01-01

This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design. Many complex engineering optimization problems can be modelled as multi-objective formulations. This book provides an introduction to multi-objective optimization using meta-heuristic algorithms, GA and PSO, and how they can be applied to problems like hardware/software partitioning in embedded systems, circuit partitioning in VLSI, design of operational amplifiers in analog VLSI, design space exploration in high-level synthesis, delay fault testing in VLSI testing, and scheduling in heterogeneous distributed systems. It is shown how, in each case, the various aspects of the EA, namely its representation, and operators like crossover, mutation, etc. can be separately formulated to solve these problems. This book is intended for design engineers and researchers in the field ...

Selfish Gene Algorithm Vs Genetic Algorithm: A Review

Science.gov (United States)

Ariff, Norharyati Md; Khalid, Noor Elaiza Abdul; Hashim, Rathiah; Noor, Noorhayati Mohamed

2016-11-01

Evolutionary algorithm is one of the algorithms inspired by the nature. Within little more than a decade hundreds of papers have reported successful applications of EAs. In this paper, the Selfish Gene Algorithms (SFGA), as one of the latest evolutionary algorithms (EAs) inspired from the Selfish Gene Theory which is an interpretation of Darwinian Theory ideas from the biologist Richards Dawkins on 1989. In this paper, following a brief introduction to the Selfish Gene Algorithm (SFGA), the chronology of its evolution is presented. It is the purpose of this paper is to present an overview of the concepts of Selfish Gene Algorithm (SFGA) as well as its opportunities and challenges. Accordingly, the history, step involves in the algorithm are discussed and its different applications together with an analysis of these applications are evaluated.

Design of problem-specific evolutionary algorithm/mixed-integer programming hybrids: two-stage stochastic integer programming applied to chemical batch scheduling

Science.gov (United States)

Urselmann, Maren; Emmerich, Michael T. M.; Till, Jochen; Sand, Guido; Engell, Sebastian

2007-07-01

Engineering optimization often deals with large, mixed-integer search spaces with a rigid structure due to the presence of a large number of constraints. Metaheuristics, such as evolutionary algorithms (EAs), are frequently suggested as solution algorithms in such cases. In order to exploit the full potential of these algorithms, it is important to choose an adequate representation of the search space and to integrate expert-knowledge into the stochastic search operators, without adding unnecessary bias to the search. Moreover, hybridisation with mathematical programming techniques such as mixed-integer programming (MIP) based on a problem decomposition can be considered for improving algorithmic performance. In order to design problem-specific EAs it is desirable to have a set of design guidelines that specify properties of search operators and representations. Recently, a set of guidelines has been proposed that gives rise to so-called Metric-based EAs (MBEAs). Extended by the minimal moves mutation they allow for a generalization of EA with self-adaptive mutation strength in discrete search spaces. In this article, a problem-specific EA for process engineering task is designed, following the MBEA guidelines and minimal moves mutation. On the background of the application, the usefulness of the design framework is discussed, and further extensions and corrections proposed. As a case-study, a two-stage stochastic programming problem in chemical batch process scheduling is considered. The algorithm design problem can be viewed as the choice of a hierarchical decision structure, where on different layers of the decision process symmetries and similarities can be exploited for the design of minimal moves. After a discussion of the design approach and its instantiation for the case-study, the resulting problem-specific EA/MIP is compared to a straightforward application of a canonical EA/MIP and to a monolithic mathematical programming algorithm. In view of the

Algorithmic Mechanism Design of Evolutionary Computation.

Science.gov (United States)

Pei, Yan

2015-01-01

We consider algorithmic design, enhancement, and improvement of evolutionary computation as a mechanism design problem. All individuals or several groups of individuals can be considered as self-interested agents. The individuals in evolutionary computation can manipulate parameter settings and operations by satisfying their own preferences, which are defined by an evolutionary computation algorithm designer, rather than by following a fixed algorithm rule. Evolutionary computation algorithm designers or self-adaptive methods should construct proper rules and mechanisms for all agents (individuals) to conduct their evolution behaviour correctly in order to definitely achieve the desired and preset objective(s). As a case study, we propose a formal framework on parameter setting, strategy selection, and algorithmic design of evolutionary computation by considering the Nash strategy equilibrium of a mechanism design in the search process. The evaluation results present the efficiency of the framework. This primary principle can be implemented in any evolutionary computation algorithm that needs to consider strategy selection issues in its optimization process. The final objective of our work is to solve evolutionary computation design as an algorithmic mechanism design problem and establish its fundamental aspect by taking this perspective. This paper is the first step towards achieving this objective by implementing a strategy equilibrium solution (such as Nash equilibrium) in evolutionary computation algorithm.

Diversity-Guided Evolutionary Algorithms

DEFF Research Database (Denmark)

Ursem, Rasmus Kjær

2002-01-01

Population diversity is undoubtably a key issue in the performance of evolutionary algorithms. A common hypothesis is that high diversity is important to avoid premature convergence and to escape local optima. Various diversity measures have been used to analyze algorithms, but so far few...... algorithms have used a measure to guide the search. The diversity-guided evolutionary algorithm (DGEA) uses the wellknown distance-to-average-point measure to alternate between phases of exploration (mutation) and phases of exploitation (recombination and selection). The DGEA showed remarkable results...

Combining evolutionary algorithms with oblique decision trees to detect bent-double galaxies

Science.gov (United States)

Cantu-Paz, Erick; Kamath, Chandrika

2000-10-01

Decision tress have long been popular in classification as they use simple and easy-to-understand tests at each node. Most variants of decision trees test a single attribute at a node, leading to axis- parallel trees, where the test results in a hyperplane which is parallel to one of the dimensions in the attribute space. These trees can be rather large and inaccurate in cases where the concept to be learned is best approximated by oblique hyperplanes. In such cases, it may be more appropriate to use an oblique decision tree, where the decision at each node is a linear combination of the attributes. Oblique decision trees have not gained wide popularity in part due to the complexity of constructing good oblique splits and the tendency of existing splitting algorithms to get stuck in local minima. Several alternatives have been proposed to handle these problems including randomization in conjunction wiht deterministic hill-climbing and the use of simulated annealing. In this paper, we use evolutionary algorithms (EAs) to determine the split. EAs are well suited for this problem because of their global search properties, their tolerance to noisy fitness evaluations, and their scalability to large dimensional search spaces. We demonstrate our technique on a synthetic data set, and then we apply it to a practical problem from astronomy, namely, the classification of galaxies with a bent-double morphology. In addition, we describe our experiences with several split evaluation criteria. Our results suggest that, in some cases, the evolutionary approach is faster and more accurate than existing oblique decision tree algorithms. However, for our astronomical data, the accuracy is not significantly different than the axis-parallel trees.

Convex hull ranking algorithm for multi-objective evolutionary algorithms

NARCIS (Netherlands)

Davoodi Monfrared, M.; Mohades, A.; Rezaei, J.

2012-01-01

Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity

Evolutionary algorithms for the optimal management of coastal groundwater: A comparative study toward future challenges

Science.gov (United States)

Ketabchi, Hamed; Ataie-Ashtiani, Behzad

2015-01-01

This paper surveys the literature associated with the application of evolutionary algorithms (EAs) in coastal groundwater management problems (CGMPs). This review demonstrates that previous studies were mostly relied on the application of limited and particular EAs, mainly genetic algorithm (GA) and its variants, to a number of specific problems. The exclusive investigation of these problems is often not the representation of the variety of feasible processes may be occurred in coastal aquifers. In this study, eight EAs are evaluated for CGMPs. The considered EAs are: GA, continuous ant colony optimization (CACO), particle swarm optimization (PSO), differential evolution (DE), artificial bee colony optimization (ABC), harmony search (HS), shuffled complex evolution (SCE), and simplex simulated annealing (SIMPSA). The first application of PSO, ABC, HS, and SCE in CGMPs is reported here. Moreover, the four benchmark problems with different degree of difficulty and variety are considered to address the important issues of groundwater resources in coastal regions. Hence, the wide ranges of popular objective functions and constraints with the number of decision variables ranging from 4 to 15 are included. These benchmark problems are applied in the combined simulation-optimization model to examine the optimization scenarios. Some preliminary experiments are performed to select the most efficient parameters values for EAs to set a fair comparison. The specific capabilities of each EA toward CGMPs in terms of results quality and required computational time are compared. The evaluation of the results highlights EA's applicability in CGMPs, besides the remarkable strengths and weaknesses of them. The comparisons show that SCE, CACO, and PSO yield superior solutions among the EAs according to the quality of solutions whereas ABC presents the poor performance. CACO provides the better solutions (up to 17%) than the worst EA (ABC) for the problem with the highest decision

Runtime analysis of the (1+1) EA on computing unique input output sequences

DEFF Research Database (Denmark)

Lehre, Per Kristian; Yao, Xin

2010-01-01

Computing unique input output (UIO) sequences is a fundamental and hard problem in conformance testing of finite state machines (FSM). Previous experimental research has shown that evolutionary algorithms (EAs) can be applied successfully to find UIOs for some FSMs. However, before EAs can...... in the theoretical analysis, and the variability of the runtime. The numerical results fit well with the theoretical results, even for small problem instance sizes. Together, these results provide a first theoretical characterisation of the potential and limitations of the (1 + 1) EA on the problem of computing UIOs....

Double-layer evolutionary algorithm for distributed optimization of particle detection on the Grid

International Nuclear Information System (INIS)

Padée, Adam; Zaremba, Krzysztof; Kurek, Krzysztof

2013-01-01

Reconstruction of particle tracks from information collected by position-sensitive detectors is an important procedure in HEP experiments. It is usually controlled by a set of numerical parameters which have to be manually optimized. This paper proposes an automatic approach to this task by utilizing evolutionary algorithm (EA) operating on both real-valued and binary representations. Because of computational complexity of the task a special distributed architecture of the algorithm is proposed, designed to be run in grid environment. It is two-level hierarchical hybrid utilizing asynchronous master-slave EA on the level of clusters and island model EA on the level of the grid. The technical aspects of usage of production grid infrastructure are covered, including communication protocols on both levels. The paper deals also with the problem of heterogeneity of the resources, presenting efficiency tests on a benchmark function. These tests confirm that even relatively small islands (clusters) can be beneficial to the optimization process when connected to the larger ones. Finally a real-life usage example is presented, which is an optimization of track reconstruction in Large Angle Spectrometer of NA-58 COMPASS experiment held at CERN, using a sample of Monte Carlo simulated data. The overall reconstruction efficiency gain, achieved by the proposed method, is more than 4%, compared to the manually optimized parameters

A Hybrid Chaotic Quantum Evolutionary Algorithm

DEFF Research Database (Denmark)

Cai, Y.; Zhang, M.; Cai, H.

2010-01-01

A hybrid chaotic quantum evolutionary algorithm is proposed to reduce amount of computation, speed up convergence and restrain premature phenomena of quantum evolutionary algorithm. The proposed algorithm adopts the chaotic initialization method to generate initial population which will form a pe...... tests. The presented algorithm is applied to urban traffic signal timing optimization and the effect is satisfied....

Species co-evolutionary algorithm: a novel evolutionary algorithm based on the ecology and environments for optimization

DEFF Research Database (Denmark)

Li, Wuzhao; Wang, Lei; Cai, Xingjuan

2015-01-01

and affect each other in many ways. The relationships include competition, predation, parasitism, mutualism and pythogenesis. In this paper, we consider the five relationships between solutions to propose a co-evolutionary algorithm termed species co-evolutionary algorithm (SCEA). In SCEA, five operators...

Industrial Applications of Evolutionary Algorithms

CERN Document Server

Sanchez, Ernesto; Tonda, Alberto

2012-01-01

This book is intended as a reference both for experienced users of evolutionary algorithms and for researchers that are beginning to approach these fascinating optimization techniques. Experienced users will find interesting details of real-world problems, and advice on solving issues related to fitness computation, modeling and setting appropriate parameters to reach optimal solutions. Beginners will find a thorough introduction to evolutionary computation, and a complete presentation of all evolutionary algorithms exploited to solve different problems. The book could fill the gap between the

The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators

Directory of Open Access Journals (Sweden)

Dazhi Jiang

2015-01-01

Full Text Available At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.

Comparison of evolutionary computation algorithms for solving bi ...

Indian Academy of Sciences (India)

failure probability. Multiobjective Evolutionary Computation algorithms (MOEAs) are well-suited for Multiobjective task scheduling on heterogeneous environment. The two Multi-Objective Evolutionary Algorithms such as Multiobjective Genetic. Algorithm (MOGA) and Multiobjective Evolutionary Programming (MOEP) with.

Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems

DEFF Research Database (Denmark)

Lissovoi, Andrei

the dynamic optimum for finite alphabets up to size μ, while MMAS is able to do so for any finite alphabet size. Parallel Evolutionary Algorithms on Maze. We prove that while a (1 + λ) EA is unable to track the optimum of the dynamic fitness function Maze for offspring population size up to λ = O(n1-ε......This thesis presents new running time analyses of nature-inspired algorithms on various dynamic problems. It aims to identify and analyse the features of algorithms and problem classes which allow efficient optimization to occur in the presence of dynamic behaviour. We consider the following...... settings: λ-MMAS on Dynamic Shortest Path Problems. We investigate how in-creasing the number of ants simulated per iteration may help an ACO algorithm to track optimum in a dynamic problem. It is shown that while a constant number of ants per-vertex is sufficient to track some oscillations, there also...

A Note on Evolutionary Algorithms and Its Applications

Science.gov (United States)

Bhargava, Shifali

2013-01-01

This paper introduces evolutionary algorithms with its applications in multi-objective optimization. Here elitist and non-elitist multiobjective evolutionary algorithms are discussed with their advantages and disadvantages. We also discuss constrained multiobjective evolutionary algorithms and their applications in various areas.

Optimization of heat transfer utilizing graph based evolutionary algorithms

International Nuclear Information System (INIS)

Bryden, Kenneth M.; Ashlock, Daniel A.; McCorkle, Douglas S.; Urban, Gregory L.

2003-01-01

This paper examines the use of graph based evolutionary algorithms (GBEAs) for optimization of heat transfer in a complex system. The specific case examined in this paper is the optimization of heat transfer in a biomass cookstove utilizing three-dimensional computational fluid dynamics to generate the fitness function. In this stove hot combustion gases are used to heat a cooking surface. The goal is to provide an even spatial temperature distribution on the cooking surface by redirecting the flow of combustion gases with baffles. The variables in the optimization are the position and size of the baffles, which are described by integer values. GBEAs are a novel type of EA in which a topology or geography is imposed on an evolving population of solutions. The choice of graph controls the rate at which solutions can spread within the population, impacting the diversity of solutions and convergence rate of the EAs. In this study, the choice of graph in the GBEAs changes the number of mating events required for convergence by a factor of approximately 2.25 and the diversity of the population by a factor of 2. These results confirm that by tuning the graph and parameters in GBEAs, computational time can be significantly reduced

An efficient hybrid evolutionary algorithm based on PSO and HBMO algorithms for multi-objective Distribution Feeder Reconfiguration

Energy Technology Data Exchange (ETDEWEB)

Niknam, Taher [Electronic and Electrical Engineering Department, Shiraz University of Technology, Shiraz (Iran)

2009-08-15

This paper introduces a robust searching hybrid evolutionary algorithm to solve the multi-objective Distribution Feeder Reconfiguration (DFR). The main objective of the DFR is to minimize the real power loss, deviation of the nodes' voltage, the number of switching operations, and balance the loads on the feeders. Because of the fact that the objectives are different and no commensurable, it is difficult to solve the problem by conventional approaches that may optimize a single objective. This paper presents a new approach based on norm3 for the DFR problem. In the proposed method, the objective functions are considered as a vector and the aim is to maximize the distance (norm2) between the objective function vector and the worst objective function vector while the constraints are met. Since the proposed DFR is a multi objective and non-differentiable optimization problem, a new hybrid evolutionary algorithm (EA) based on the combination of the Honey Bee Mating Optimization (HBMO) and the Discrete Particle Swarm Optimization (DPSO), called DPSO-HBMO, is implied to solve it. The results of the proposed reconfiguration method are compared with the solutions obtained by other approaches, the original DPSO and HBMO over different distribution test systems. (author)

Shrimp Feed Formulation via Evolutionary Algorithm with Power Heuristics for Handling Constraints

Directory of Open Access Journals (Sweden)

Rosshairy Abd. Rahman

2017-01-01

Full Text Available Formulating feed for shrimps represents a challenge to farmers and industry partners. Most previous studies selected from only a small number of ingredients due to cost pressures, even though hundreds of potential ingredients could be used in the shrimp feed mix. Even with a limited number of ingredients, the best combination of the most appropriate ingredients is still difficult to obtain due to various constraint requirements, such as nutrition value and cost. This paper proposes a new operator which we call Power Heuristics, as part of an Evolutionary Algorithm (EA, which acts as a constraint handling technique for the shrimp feed or diet formulation. The operator is able to choose and discard certain ingredients by utilising a specialized search mechanism. The aim is to achieve the most appropriate combination of ingredients. Power Heuristics are embedded in the EA at the early stage of a semirandom initialization procedure. The resulting combination of ingredients, after fulfilling all the necessary constraints, shows that this operator is useful in discarding inappropriate ingredients when a crucial constraint is violated.

Comparing Evolutionary Strategies on a Biobjective Cultural Algorithm

Directory of Open Access Journals (Sweden)

Carolina Lagos

2014-01-01

Full Text Available Evolutionary algorithms have been widely used to solve large and complex optimisation problems. Cultural algorithms (CAs are evolutionary algorithms that have been used to solve both single and, to a less extent, multiobjective optimisation problems. In order to solve these optimisation problems, CAs make use of different strategies such as normative knowledge, historical knowledge, circumstantial knowledge, and among others. In this paper we present a comparison among CAs that make use of different evolutionary strategies; the first one implements a historical knowledge, the second one considers a circumstantial knowledge, and the third one implements a normative knowledge. These CAs are applied on a biobjective uncapacitated facility location problem (BOUFLP, the biobjective version of the well-known uncapacitated facility location problem. To the best of our knowledge, only few articles have applied evolutionary multiobjective algorithms on the BOUFLP and none of those has focused on the impact of the evolutionary strategy on the algorithm performance. Our biobjective cultural algorithm, called BOCA, obtains important improvements when compared to other well-known evolutionary biobjective optimisation algorithms such as PAES and NSGA-II. The conflicting objective functions considered in this study are cost minimisation and coverage maximisation. Solutions obtained by each algorithm are compared using a hypervolume S metric.

An Evolutionary Formulation of the Crossing Number Problem

Directory of Open Access Journals (Sweden)

Che Sheng Gan

2009-01-01

Full Text Available A graph drawing algorithm is presented which results in complete graphs having minimum crossings equal to that of Guy's conjecture. It is then generalized and formulated in an evolutionary algorithm (EA to perform constrained search for the crossing numbers. The main objective of this work is to present a suitable two-dimensional scheme which can greatly reduce the complexity of finding crossing numbers by using computer. Program performance criteria are presented and discussed. It is shown that the EA implementation provides good confirmation of the predicted crossing numbers.

Massively parallel evolutionary computation on GPGPUs

CERN Document Server

Tsutsui, Shigeyoshi

2013-01-01

Evolutionary algorithms (EAs) are metaheuristics that learn from natural collective behavior and are applied to solve optimization problems in domains such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with high-speed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively parallel cluster machines, or grid computing environments. Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened u

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Directory of Open Access Journals (Sweden)

Biaobiao Zhang

2011-01-01

Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

Infrastructure system restoration planning using evolutionary algorithms

Science.gov (United States)

Corns, Steven; Long, Suzanna K.; Shoberg, Thomas G.

2016-01-01

This paper presents an evolutionary algorithm to address restoration issues for supply chain interdependent critical infrastructure. Rapid restoration of infrastructure after a large-scale disaster is necessary to sustaining a nation's economy and security, but such long-term restoration has not been investigated as thoroughly as initial rescue and recovery efforts. A model of the Greater Saint Louis Missouri area was created and a disaster scenario simulated. An evolutionary algorithm is used to determine the order in which the bridges should be repaired based on indirect costs. Solutions were evaluated based on the reduction of indirect costs and the restoration of transportation capacity. When compared to a greedy algorithm, the evolutionary algorithm solution reduced indirect costs by approximately 12.4% by restoring automotive travel routes for workers and re-establishing the flow of commodities across the three rivers in the Saint Louis area.

Multi-objective mixture-based iterated density estimation evolutionary algorithms

NARCIS (Netherlands)

Thierens, D.; Bosman, P.A.N.

2001-01-01

We propose an algorithm for multi-objective optimization using a mixture-based iterated density estimation evolutionary algorithm (MIDEA). The MIDEA algorithm is a prob- abilistic model building evolutionary algo- rithm that constructs at each generation a mixture of factorized probability

Evaluation of models generated via hybrid evolutionary algorithms ...

African Journals Online (AJOL)

2016-04-02

Apr 2, 2016 ... Evaluation of models generated via hybrid evolutionary algorithms for the prediction of Microcystis ... evolutionary algorithms (HEA) proved to be highly applica- ble to the hypertrophic reservoirs of South Africa. .... discovered and optimised using a large-scale parallel computational device and relevant soft-.

A Double Evolutionary Pool Memetic Algorithm for Examination Timetabling Problems

Directory of Open Access Journals (Sweden)

Yu Lei

2014-01-01

Full Text Available A double evolutionary pool memetic algorithm is proposed to solve the examination timetabling problem. To improve the performance of the proposed algorithm, two evolutionary pools, that is, the main evolutionary pool and the secondary evolutionary pool, are employed. The genetic operators have been specially designed to fit the examination timetabling problem. A simplified version of the simulated annealing strategy is designed to speed the convergence of the algorithm. A clonal mechanism is introduced to preserve population diversity. Extensive experiments carried out on 12 benchmark examination timetabling instances show that the proposed algorithm is able to produce promising results for the uncapacitated examination timetabling problem.

Parallel Evolutionary Optimization Algorithms for Peptide-Protein Docking

Science.gov (United States)

Poluyan, Sergey; Ershov, Nikolay

2018-02-01

In this study we examine the possibility of using evolutionary optimization algorithms in protein-peptide docking. We present the main assumptions that reduce the docking problem to a continuous global optimization problem and provide a way of using evolutionary optimization algorithms. The Rosetta all-atom force field was used for structural representation and energy scoring. We describe the parallelization scheme and MPI/OpenMP realization of the considered algorithms. We demonstrate the efficiency and the performance for some algorithms which were applied to a set of benchmark tests.

Evolutionary Algorithms for Boolean Queries Optimization

Czech Academy of Sciences Publication Activity Database

Húsek, Dušan; Snášel, Václav; Neruda, Roman; Owais, S.S.J.; Krömer, P.

2006-01-01

Roč. 3, č. 1 (2006), s. 15-20 ISSN 1790-0832 R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary algorithms * genetic algorithms * information retrieval * Boolean query Subject RIV: BA - General Mathematics

Solving Partial Differential Equations Using a New Differential Evolution Algorithm

Directory of Open Access Journals (Sweden)

Natee Panagant

2014-01-01

Full Text Available This paper proposes an alternative meshless approach to solve partial differential equations (PDEs. With a global approximate function being defined, a partial differential equation problem is converted into an optimisation problem with equality constraints from PDE boundary conditions. An evolutionary algorithm (EA is employed to search for the optimum solution. For this approach, the most difficult task is the low convergence rate of EA which consequently results in poor PDE solution approximation. However, its attractiveness remains due to the nature of a soft computing technique in EA. The algorithm can be used to tackle almost any kind of optimisation problem with simple evolutionary operation, which means it is mathematically simpler to use. A new efficient differential evolution (DE is presented and used to solve a number of the partial differential equations. The results obtained are illustrated and compared with exact solutions. It is shown that the proposed method has a potential to be a future meshless tool provided that the search performance of EA is greatly enhanced.

Synthesis of logic circuits with evolutionary algorithms

Energy Technology Data Exchange (ETDEWEB)

JONES,JAKE S.; DAVIDSON,GEORGE S.

2000-01-26

In the last decade there has been interest and research in the area of designing circuits with genetic algorithms, evolutionary algorithms, and genetic programming. However, the ability to design circuits of the size and complexity required by modern engineering design problems, simply by specifying required outputs for given inputs has as yet eluded researchers. This paper describes current research in the area of designing logic circuits using an evolutionary algorithm. The goal of the research is to improve the effectiveness of this method and make it a practical aid for design engineers. A novel method of implementing the algorithm is introduced, and results are presented for various multiprocessing systems. In addition to evolving standard arithmetic circuits, work in the area of evolving circuits that perform digital signal processing tasks is described.

Analog Circuit Design Optimization Based on Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Mansour Barari

2014-01-01

Full Text Available This paper investigates an evolutionary-based designing system for automated sizing of analog integrated circuits (ICs. Two evolutionary algorithms, genetic algorithm and PSO (Parswal particle swarm optimization algorithm, are proposed to design analog ICs with practical user-defined specifications. On the basis of the combination of HSPICE and MATLAB, the system links circuit performances, evaluated through specific electrical simulation, to the optimization system in the MATLAB environment, for the selected topology. The system has been tested by typical and hard-to-design cases, such as complex analog blocks with stringent design requirements. The results show that the design specifications are closely met. Comparisons with available methods like genetic algorithms show that the proposed algorithm offers important advantages in terms of optimization quality and robustness. Moreover, the algorithm is shown to be efficient.

Evolutionary Algorithms Application Analysis in Biometric Systems

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N. Goranin

2010-01-01

Full Text Available Wide usage of biometric information for person identity verification purposes, terrorist acts prevention measures and authenticationprocess simplification in computer systems has raised significant attention to reliability and efficiency of biometricsystems. Modern biometric systems still face many reliability and efficiency related issues such as reference databasesearch speed, errors while recognizing of biometric information or automating biometric feature extraction. Current scientificinvestigations show that application of evolutionary algorithms may significantly improve biometric systems. In thisarticle we provide a comprehensive review of main scientific research done in sphere of evolutionary algorithm applicationfor biometric system parameter improvement.

A backtracking evolutionary algorithm for power systems

Directory of Open Access Journals (Sweden)

Chiou Ji-Pyng

2017-01-01

Full Text Available This paper presents a backtracking variable scaling hybrid differential evolution, called backtracking VSHDE, for solving the optimal network reconfiguration problems for power loss reduction in distribution systems. The concepts of the backtracking, variable scaling factor, migrating, accelerated, and boundary control mechanism are embedded in the original differential evolution (DE to form the backtracking VSHDE. The concepts of the backtracking and boundary control mechanism can increase the population diversity. And, according to the convergence property of the population, the scaling factor is adjusted based on the 1/5 success rule of the evolution strategies (ESs. A larger population size must be used in the evolutionary algorithms (EAs to maintain the population diversity. To overcome this drawback, two operations, acceleration operation and migrating operation, are embedded into the proposed method. The feeder reconfiguration of distribution systems is modelled as an optimization problem which aims at achieving the minimum loss subject to voltage and current constraints. So, the proper system topology that reduces the power loss according to a load pattern is an important issue. Mathematically, the network reconfiguration system is a nonlinear programming problem with integer variables. One three-feeder network reconfiguration system from the literature is researched by the proposed backtracking VSHDE method and simulated annealing (SA. Numerical results show that the perfrmance of the proposed method outperformed the SA method.

Towards Automatic Controller Design using Multi-Objective Evolutionary Algorithms

DEFF Research Database (Denmark)

Pedersen, Gerulf

of evolutionary computation, a choice was made to use multi-objective algorithms for the purpose of aiding in automatic controller design. More specifically, the choice was made to use the Non-dominated Sorting Genetic Algorithm II (NSGAII), which is one of the most potent algorithms currently in use...... for automatic controller design. However, because the field of evolutionary computation is relatively unknown in the field of control engineering, this thesis also includes a comprehensive introduction to the basic field of evolutionary computation as well as a description of how the field has previously been......In order to design the controllers of tomorrow, a need has risen for tools that can aid in the design of these. A desire to use evolutionary computation as a tool to achieve that goal is what gave inspiration for the work contained in this thesis. After having studied the foundations...

Evolutionary Algorithm for Optimal Vaccination Scheme

International Nuclear Information System (INIS)

Parousis-Orthodoxou, K J; Vlachos, D S

2014-01-01

The following work uses the dynamic capabilities of an evolutionary algorithm in order to obtain an optimal immunization strategy in a user specified network. The produced algorithm uses a basic genetic algorithm with crossover and mutation techniques, in order to locate certain nodes in the inputted network. These nodes will be immunized in an SIR epidemic spreading process, and the performance of each immunization scheme, will be evaluated by the level of containment that provides for the spreading of the disease

Economic modeling using evolutionary algorithms : the effect of binary encoding of strategies

NARCIS (Netherlands)

Waltman, L.R.; Eck, van N.J.; Dekker, Rommert; Kaymak, U.

2011-01-01

We are concerned with evolutionary algorithms that are employed for economic modeling purposes. We focus in particular on evolutionary algorithms that use a binary encoding of strategies. These algorithms, commonly referred to as genetic algorithms, are popular in agent-based computational economics

A New DG Multiobjective Optimization Method Based on an Improved Evolutionary Algorithm

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Wanxing Sheng

2013-01-01

Full Text Available A distribution generation (DG multiobjective optimization method based on an improved Pareto evolutionary algorithm is investigated in this paper. The improved Pareto evolutionary algorithm, which introduces a penalty factor in the objective function constraints, uses an adaptive crossover and a mutation operator in the evolutionary process and combines a simulated annealing iterative process. The proposed algorithm is utilized to the optimize DG injection models to maximize DG utilization while minimizing system loss and environmental pollution. A revised IEEE 33-bus system with multiple DG units was used to test the multiobjective optimization algorithm in a distribution power system. The proposed algorithm was implemented and compared with the strength Pareto evolutionary algorithm 2 (SPEA2, a particle swarm optimization (PSO algorithm, and nondominated sorting genetic algorithm II (NGSA-II. The comparison of the results demonstrates the validity and practicality of utilizing DG units in terms of economic dispatch and optimal operation in a distribution power system.

Development of antibiotic regimens using graph based evolutionary algorithms.

Science.gov (United States)

Corns, Steven M; Ashlock, Daniel A; Bryden, Kenneth M

2013-12-01

This paper examines the use of evolutionary algorithms in the development of antibiotic regimens given to production animals. A model is constructed that combines the lifespan of the animal and the bacteria living in the animal's gastro-intestinal tract from the early finishing stage until the animal reaches market weight. This model is used as the fitness evaluation for a set of graph based evolutionary algorithms to assess the impact of diversity control on the evolving antibiotic regimens. The graph based evolutionary algorithms have two objectives: to find an antibiotic treatment regimen that maintains the weight gain and health benefits of antibiotic use and to reduce the risk of spreading antibiotic resistant bacteria. This study examines different regimens of tylosin phosphate use on bacteria populations divided into Gram positive and Gram negative types, with a focus on Campylobacter spp. Treatment regimens were found that provided decreased antibiotic resistance relative to conventional methods while providing nearly the same benefits as conventional antibiotic regimes. By using a graph to control the information flow in the evolutionary algorithm, a variety of solutions along the Pareto front can be found automatically for this and other multi-objective problems. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Hybrid Projected Gradient-Evolutionary Search Algorithm for Mixed Integer Nonlinear Optimization Problems

National Research Council Canada - National Science Library

Homaifar, Abdollah; Esterline, Albert; Kimiaghalam, Bahram

2005-01-01

The Hybrid Projected Gradient-Evolutionary Search Algorithm (HPGES) algorithm uses a specially designed evolutionary-based global search strategy to efficiently create candidate solutions in the solution space...

The concept of ageing in evolutionary algorithms: Discussion and inspirations for human ageing.

Science.gov (United States)

Dimopoulos, Christos; Papageorgis, Panagiotis; Boustras, George; Efstathiades, Christodoulos

2017-04-01

This paper discusses the concept of ageing as this applies to the operation of Evolutionary Algorithms, and examines its relationship to the concept of ageing as this is understood for human beings. Evolutionary Algorithms constitute a family of search algorithms which base their operation on an analogy from the evolution of species in nature. The paper initially provides the necessary knowledge on the operation of Evolutionary Algorithms, focusing on the use of ageing strategies during the implementation of the evolutionary process. Background knowledge on the concept of ageing, as this is defined scientifically for biological systems, is subsequently presented. Based on this information, the paper provides a comparison between the two ageing concepts, and discusses the philosophical inspirations which can be drawn for human ageing based on the operation of Evolutionary Algorithms. Copyright © 2017 Elsevier B.V. All rights reserved.

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

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Rafał Dreżewski

2017-08-01

Full Text Available Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

Simplified Drift Analysis for Proving Lower Bounds in Evolutionary Computation

DEFF Research Database (Denmark)

Oliveto, Pietro S.; Witt, Carsten

2011-01-01

Drift analysis is a powerful tool used to bound the optimization time of evolutionary algorithms (EAs). Various previous works apply a drift theorem going back to Hajek in order to show exponential lower bounds on the optimization time of EAs. However, this drift theorem is tedious to read...... and to apply since it requires two bounds on the moment-generating (exponential) function of the drift. A recent work identifies a specialization of this drift theorem that is much easier to apply. Nevertheless, it is not as simple and not as general as possible. The present paper picks up Hajek’s line...

Multiobjective RFID Network Optimization Using Multiobjective Evolutionary and Swarm Intelligence Approaches

Directory of Open Access Journals (Sweden)

Hanning Chen

2014-01-01

Full Text Available The development of radio frequency identification (RFID technology generates the most challenging RFID network planning (RNP problem, which needs to be solved in order to operate the large-scale RFID network in an optimal fashion. RNP involves many objectives and constraints and has been proven to be a NP-hard multi-objective problem. The application of evolutionary algorithm (EA and swarm intelligence (SI for solving multiobjective RNP (MORNP has gained significant attention in the literature, but these algorithms always transform multiple objectives into a single objective by weighted coefficient approach. In this paper, we use multiobjective EA and SI algorithms to find all the Pareto optimal solutions and to achieve the optimal planning solutions by simultaneously optimizing four conflicting objectives in MORNP, instead of transforming multiobjective functions into a single objective function. The experiment presents an exhaustive comparison of three successful multiobjective EA and SI, namely, the recently developed multiobjective artificial bee colony algorithm (MOABC, the nondominated sorting genetic algorithm II (NSGA-II, and the multiobjective particle swarm optimization (MOPSO, on MORNP instances of different nature, namely, the two-objective and three-objective MORNP. Simulation results show that MOABC proves to be more superior for planning RFID networks than NSGA-II and MOPSO in terms of optimization accuracy and computation robustness.

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

Science.gov (United States)

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case

Exploitation of linkage learning in evolutionary algorithms

CERN Document Server

Chen, Ying-ping

2010-01-01

The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.

Optimizing Transmission Network Expansion Planning With The Mean Of Chaotic Differential Evolution Algorithm

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.

An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems

Directory of Open Access Journals (Sweden)

Fuqing Zhao

2016-01-01

Full Text Available A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms.

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Richard Lamb

2015-09-01

Full Text Available Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a “virtual” student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning. Comparison of the STAC-M and the STAC-M with inclusion of the Multiobjective Evolutionary Algorithm shows greater success in solving the Piagetian science-tasks post cognitive retraining with the Multiobjective Evolutionary Algorithm. This illustrates the potential uses of cognitive and neuropsychological computational modeling in educational research. The authors also outline the limitations and assumptions of computational modeling.

Multi-Working Modes Product-Color Planning Based on Evolutionary Algorithms and Swarm Intelligence

Directory of Open Access Journals (Sweden)

Man Ding

2010-01-01

Full Text Available In order to assist designer in color planning during product development, a novel synthesized evaluation method is presented to evaluate color-combination schemes of multi-working modes products (MMPs. The proposed evaluation method considers color-combination images in different working modes as evaluating attributes, to which the corresponding weights are assigned for synthesized evaluation. Then a mathematical model is developed to search for optimal color-combination schemes of MMP based on the proposed evaluation method and two powerful search techniques known as Evolution Algorithms (EAs and Swarm Intelligence (SI. In the experiments, we present a comparative study for two EAs, namely, Genetic Algorithm (GA and Difference Evolution (DE, and one SI algorithm, namely, Particle Swarm Optimization (PSO, on searching for color-combination schemes of MMP problem. All of the algorithms are evaluated against a test scenario, namely, an Arm-type aerial work platform, which has two working modes. The results show that the DE obtains the superior solution than the other two algorithms for color-combination scheme searching problem in terms of optimization accuracy and computation robustness. Simulation results demonstrate that the proposed method is feasible and efficient.

Analysis for Performance of Symbiosis Co-evolutionary Algorithm

OpenAIRE

根路銘, もえ子; 遠藤, 聡志; 山田, 孝治; 宮城, 隼夫; Nerome, Moeko; Endo, Satoshi; Yamada, Koji; Miyagi, Hayao

2000-01-01

In this paper, we analyze the behavior of symbiotic evolution algorithm for the N-Queens problem as benchmark problem for search methods in the field of aritificial intelligence. It is shown that this algorithm improves the ability of evolutionary search method. When the problem is solved by Genetic Algorithms (GAs), an ordinal representation is often used as one of gene conversion methods which convert from phenotype to genotype and reconvert. The representation can hinder occurrence of leth...

A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization

DEFF Research Database (Denmark)

Lissovoi, Andrei; Witt, Carsten

2017-01-01

A simple island model with (Formula presented.) islands and migration occurring after every (Formula presented.) iterations is studied on the dynamic fitness function Maze. This model is equivalent to a (Formula presented.) EA if (Formula presented.), i. e., migration occurs during every iteratio.......). The relationship of (Formula presented.), and the ability of the island model to track the optimum is then investigated more closely. Finally, experiments are performed to supplement the asymptotic results, and investigate the impact of the migration topology.......A simple island model with (Formula presented.) islands and migration occurring after every (Formula presented.) iterations is studied on the dynamic fitness function Maze. This model is equivalent to a (Formula presented.) EA if (Formula presented.), i. e., migration occurs during every iteration....... It is proved that even for an increased offspring population size up to (Formula presented.), the (Formula presented.) EA is still not able to track the optimum of Maze. If the migration interval is chosen carefully, the algorithm is able to track the optimum even for logarithmic (Formula presented...

Applications of Evolutionary Algorithms to Electromagnetic Materials Characterization and Design Problems

Science.gov (United States)

Frasch, Jonathan Lemoine

Determining the electrical permittivity and magnetic permeability of materials is an important task in electromagnetics research. The method using reflection and transmission scattering parameters to determine these constants has been widely employed for many years, ever since the work of Nicolson, Ross, and Weir in the 1970's. For general materials that are homogeneous, linear, and isotropic, the method they developed (the NRW method) works very well and provides an analytical solution. For materials which possess a metal backing or are applied as a coating to a metal surface, it can be difficult or even impossible to obtain a transmission measurement, especially when the coating is thin. In such a circumstance, it is common to resort to a method which uses two reflection type measurements. There are several such methods for free-space measurements, using multiple angles or polarizations for example. For waveguide measurements, obtaining two independent sources of information from which to extract two complex parameters can be a challenge. This dissertation covers three different topics. Two of these involve different techniques to characterize conductor-backed materials, and the third proposes a method for designing synthetic validation standards for use with standard NRW measurements. All three of these topics utilize modal expansions of electric and magnetic fields to analyze propagation in stepped rectangular waveguides. Two of the projects utilize evolutionary algorithms (EA) to design waveguide structures. These algorithms were developed specifically for these projects and utilize fairly recent innovations within the optimization community. The first characterization technique uses two different versions of a single vertical step in the waveguide. Samples to be tested lie inside the steps with the conductor reflection plane behind them. If the two reflection measurements are truly independent it should be possible to recover the values of two complex

Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

Science.gov (United States)

2009-03-10

xfar by xint. Else, generate a new individual, using the Sobol pseudo- random sequence generator within the upper and lower bounds of the variables...12. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons. 2002. 13. Sobol , I. M., "Uniformly Distributed Sequences

Sounds unheard of evolutionary algorithms as creative tools for the contemporary composer

DEFF Research Database (Denmark)

Dahlstedt, Palle

2004-01-01

Evolutionary algorithms are studied as tools for generating novel musical material in the form of musical scores and synthesized sounds. The choice of genetic representation defines a space of potential music. This space is explored using evolutionary algorithms, in search of useful musical mater...... composed with the tools described in the thesis are presented....

Constrained Optimization Based on Hybrid Evolutionary Algorithm and Adaptive Constraint-Handling Technique

DEFF Research Database (Denmark)

Wang, Yong; Cai, Zixing; Zhou, Yuren

2009-01-01

A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...

Food processing optimization using evolutionary algorithms | Enitan ...

African Journals Online (AJOL)

Evolutionary algorithms are widely used in single and multi-objective optimization. They are easy to use and provide solution(s) in one simulation run. They are used in food processing industries for decision making. Food processing presents constrained and unconstrained optimization problems. This paper reviews the ...

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Lvjiang Yin

2016-12-01

Full Text Available Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T, cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II, Strength Pareto Evolutionary Algorithm 2 (SPEA2, Multiobjective Particle Swarm Optimization (OMOPSO, and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D. Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

Performance comparison of some evolutionary algorithms on job shop scheduling problems

Science.gov (United States)

Mishra, S. K.; Rao, C. S. P.

2016-09-01

Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.

Prospective Algorithms for Quantum Evolutionary Computation

OpenAIRE

Sofge, Donald A.

2008-01-01

This effort examines the intersection of the emerging field of quantum computing and the more established field of evolutionary computation. The goal is to understand what benefits quantum computing might offer to computational intelligence and how computational intelligence paradigms might be implemented as quantum programs to be run on a future quantum computer. We critically examine proposed algorithms and methods for implementing computational intelligence paradigms, primarily focused on ...

Academic Training: Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms - Lecture series

CERN Multimedia

Françoise Benz

2004-01-01

ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on natural annealing processes or Evolutionary Computation, based on biological evolution processes. Geneti...

Nash evolutionary algorithms : Testing problem size in reconstruction problems in frame structures

OpenAIRE

Greiner, D.; Periaux, Jacques; Emperador, J.M.; Galván, B.; Winter, G.

2016-01-01

The use of evolutionary algorithms has been enhanced in recent years for solving real engineering problems, where the requirements of intense computational calculations are needed, especially when computational engineering simulations are involved (use of finite element method, boundary element method, etc). The coupling of game-theory concepts in evolutionary algorithms has been a recent line of research which could enhance the efficiency of the optimum design procedure and th...

Packets Distributing Evolutionary Algorithm Based on PSO for Ad Hoc Network

Science.gov (United States)

Xu, Xiao-Feng

2018-03-01

Wireless communication network has such features as limited bandwidth, changeful channel and dynamic topology, etc. Ad hoc network has lots of difficulties in accessing control, bandwidth distribution, resource assign and congestion control. Therefore, a wireless packets distributing Evolutionary algorithm based on PSO (DPSO)for Ad Hoc Network is proposed. Firstly, parameters impact on performance of network are analyzed and researched to obtain network performance effective function. Secondly, the improved PSO Evolutionary Algorithm is used to solve the optimization problem from local to global in the process of network packets distributing. The simulation results show that the algorithm can ensure fairness and timeliness of network transmission, as well as improve ad hoc network resource integrated utilization efficiency.

Strength Pareto Evolutionary Algorithm using Self-Organizing Data Analysis Techniques

Directory of Open Access Journals (Sweden)

Ionut Balan

2015-03-01

Full Text Available Multiobjective optimization is widely used in problems solving from a variety of areas. To solve such problems there was developed a set of algorithms, most of them based on evolutionary techniques. One of the algorithms from this class, which gives quite good results is SPEA2, method which is the basis of the proposed algorithm in this paper. Results from this paper are obtained by running these two algorithms on a flow-shop problem.

International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems

CERN Document Server

Dash, Subhransu; Panigrahi, Bijaya

2015-01-01

  The book is a collection of high-quality peer-reviewed research papers presented in Proceedings of International Conference on Artificial Intelligence and Evolutionary Algorithms in Engineering Systems (ICAEES 2014) held at Noorul Islam Centre for Higher Education, Kumaracoil, India. These research papers provide the latest developments in the broad area of use of artificial intelligence and evolutionary algorithms in engineering systems. The book discusses wide variety of industrial, engineering and scientific applications of the emerging techniques. It presents invited papers from the inventors/originators of new applications and advanced technologies.

An Evolutionary Algorithm to Mine High-Utility Itemsets

Directory of Open Access Journals (Sweden)

Jerry Chun-Wei Lin

2015-01-01

Full Text Available High-utility itemset mining (HUIM is a critical issue in recent years since it can be used to reveal the profitable products by considering both the quantity and profit factors instead of frequent itemset mining (FIM of association rules (ARs. In this paper, an evolutionary algorithm is presented to efficiently mine high-utility itemsets (HUIs based on the binary particle swarm optimization. A maximal pattern (MP-tree strcutrue is further designed to solve the combinational problem in the evolution process. Substantial experiments on real-life datasets show that the proposed binary PSO-based algorithm has better results compared to the state-of-the-art GA-based algorithm.

Evolutionary optimization and game strategies for advanced multi-disciplinary design applications to aeronautics and UAV design

CERN Document Server

Periaux, Jacques; Lee, Dong Seop Chris

2015-01-01

Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies. This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization and hierarchical topology which allows different models including precise, intermediate and approximate models with each node belonging to the different hierarchical layer handled by a different Evolutionary Algorithm. The efficiency of evolutionary algorithms for both single and multi-objective optimization problems are significantly improved by the coupling of EAs with games and in particular by a new dynamic methodology named “Hybridized Nash-Pareto games”. Multi objective Optimization techniques and robust design problems taking into account uncertainties are introduced and explained in detail. Several applications dealing with c...

Use of multiple objective evolutionary algorithms in optimizing surveillance requirements

International Nuclear Information System (INIS)

Martorell, S.; Carlos, S.; Villanueva, J.F.; Sanchez, A.I; Galvan, B.; Salazar, D.; Cepin, M.

2006-01-01

This paper presents the development and application of a double-loop Multiple Objective Evolutionary Algorithm that uses a Multiple Objective Genetic Algorithm to perform the simultaneous optimization of periodic Test Intervals (TI) and Test Planning (TP). It takes into account the time-dependent effect of TP performed on stand-by safety-related equipment. TI and TP are part of the Surveillance Requirements within Technical Specifications at Nuclear Power Plants. It addresses the problem of multi-objective optimization in the space of dependable variables, i.e. TI and TP, using a novel flexible structure of the optimization algorithm. Lessons learnt from the cases of application of the methodology to optimize TI and TP for the High-Pressure Injection System are given. The results show that the double-loop Multiple Objective Evolutionary Algorithm is able to find the Pareto set of solutions that represents a surface of non-dominated solutions that satisfy all the constraints imposed on the objective functions and decision variables. Decision makers can adopt then the best solution found depending on their particular preference, e.g. minimum cost, minimum unavailability

Designing synthetic networks in silico: a generalised evolutionary algorithm approach.

Science.gov (United States)

Smith, Robert W; van Sluijs, Bob; Fleck, Christian

2017-12-02

Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

Physical Mapping Using Simulated Annealing and Evolutionary Algorithms

DEFF Research Database (Denmark)

Vesterstrøm, Jacob Svaneborg

2003-01-01

optimization method when searching for an ordering of the fragments in PM. In this paper, we applied an evolutionary algorithm to the problem, and compared its performance to that of SA and local search on simulated PM data, in order to determine the important factors in finding a good ordering of the segments....... The analysis highlights the importance of a good PM model, a well-correlated fitness function, and high quality hybridization data. We suggest that future work in PM should focus on design of more reliable fitness functions and on developing error-screening algorithms....

Multi-objective optimization of a vertical ground source heat pump using evolutionary algorithm

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Amlashi, Emad Hadaddi; Amidpour, Majid

2009-01-01

Thermodynamic and thermoeconomic optimization of a vertical ground source heat pump system has been studied. A model based on the energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. The proposed vertical ground source heat pump system including eight decision variables is considered for optimization. An artificial intelligence technique known as evolutionary algorithm (EA) has been utilized as an optimization method. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective, thermoeconomic single objective and multi-objective optimizations are performed. In Multi-objective optimization, both thermodynamic and thermoeconomic objectives are considered, simultaneously. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from available optimal points on Pareto frontier is presented. The results obtained using the various optimization approaches are compared and discussed. Further, the sensitivity of optimized systems to the interest rate, to the annual number of operating hours and to the electricity cost are studied in detail.

Evolutionary optimization methods for accelerator design

Science.gov (United States)

Poklonskiy, Alexey A.

Many problems from the fields of accelerator physics and beam theory can be formulated as optimization problems and, as such, solved using optimization methods. Despite growing efficiency of the optimization methods, the adoption of modern optimization techniques in these fields is rather limited. Evolutionary Algorithms (EAs) form a relatively new and actively developed optimization methods family. They possess many attractive features such as: ease of the implementation, modest requirements on the objective function, a good tolerance to noise, robustness, and the ability to perform a global search efficiently. In this work we study the application of EAs to problems from accelerator physics and beam theory. We review the most commonly used methods of unconstrained optimization and describe the GATool, evolutionary algorithm and the software package, used in this work, in detail. Then we use a set of test problems to assess its performance in terms of computational resources, quality of the obtained result, and the tradeoff between them. We justify the choice of GATool as a heuristic method to generate cutoff values for the COSY-GO rigorous global optimization package for the COSY Infinity scientific computing package. We design the model of their mutual interaction and demonstrate that the quality of the result obtained by GATool increases as the information about the search domain is refined, which supports the usefulness of this model. We Giscuss GATool's performance on the problems suffering from static and dynamic noise and study useful strategies of GATool parameter tuning for these and other difficult problems. We review the challenges of constrained optimization with EAs and methods commonly used to overcome them. We describe REPA, a new constrained optimization method based on repairing, in exquisite detail, including the properties of its two repairing techniques: REFIND and REPROPT. We assess REPROPT's performance on the standard constrained

An Extensible Component-Based Multi-Objective Evolutionary Algorithm Framework

DEFF Research Database (Denmark)

Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

2017-01-01

The ability to easily modify the problem definition is currently missing in Multi-Objective Evolutionary Algorithms (MOEA). Existing MOEA frameworks do not support dynamic addition and extension of the problem formulation. The existing frameworks require a re-specification of the problem definition...

Evolutionary Algorithms for Boolean Functions in Diverse Domains of Cryptography.

Science.gov (United States)

Picek, Stjepan; Carlet, Claude; Guilley, Sylvain; Miller, Julian F; Jakobovic, Domagoj

2016-01-01

The role of Boolean functions is prominent in several areas including cryptography, sequences, and coding theory. Therefore, various methods for the construction of Boolean functions with desired properties are of direct interest. New motivations on the role of Boolean functions in cryptography with attendant new properties have emerged over the years. There are still many combinations of design criteria left unexplored and in this matter evolutionary computation can play a distinct role. This article concentrates on two scenarios for the use of Boolean functions in cryptography. The first uses Boolean functions as the source of the nonlinearity in filter and combiner generators. Although relatively well explored using evolutionary algorithms, it still presents an interesting goal in terms of the practical sizes of Boolean functions. The second scenario appeared rather recently where the objective is to find Boolean functions that have various orders of the correlation immunity and minimal Hamming weight. In both these scenarios we see that evolutionary algorithms are able to find high-quality solutions where genetic programming performs the best.

Implementation of an evolutionary algorithm in planning investment in a power distribution system

Directory of Open Access Journals (Sweden)

Carlos Andrés García Montoya

2011-06-01

Full Text Available The definition of an investment plan to implement in a distribution power system, is a task that constantly faced by utilities. This work presents a methodology for determining the investment plan for a distribution power system under a shortterm, using as a criterion for evaluating investment projects, associated costs and customers benefit from its implementation. Given the number of projects carried out annually on the system, the definition of an investment plan requires the use of computational tools to evaluate, a set of possibilities, the one that best suits the needs of the present system and better results. That is why in the job, implementing a multi objective evolutionary algorithm SPEA (Strength Pareto Evolutionary Algorithm, which, based on the principles of Pareto optimality, it deliver to the planning expert, the best solutions found in the optimization process. The performance of the algorithm is tested using a set of projects to determine the best among the possible plans. We analyze also the effect of operators on the performance of evolutionary algorithm and results.

Fast stochastic algorithm for simulating evolutionary population dynamics

Science.gov (United States)

Tsimring, Lev; Hasty, Jeff; Mather, William

2012-02-01

Evolution and co-evolution of ecological communities are stochastic processes often characterized by vastly different rates of reproduction and mutation and a coexistence of very large and very small sub-populations of co-evolving species. This creates serious difficulties for accurate statistical modeling of evolutionary dynamics. In this talk, we introduce a new exact algorithm for fast fully stochastic simulations of birth/death/mutation processes. It produces a significant speedup compared to the direct stochastic simulation algorithm in a typical case when the total population size is large and the mutation rates are much smaller than birth/death rates. We illustrate the performance of the algorithm on several representative examples: evolution on a smooth fitness landscape, NK model, and stochastic predator-prey system.

Estimating the ratios of the stationary distribution values for Markov chains modeling evolutionary algorithms.

Science.gov (United States)

Mitavskiy, Boris; Cannings, Chris

2009-01-01

The evolutionary algorithm stochastic process is well-known to be Markovian. These have been under investigation in much of the theoretical evolutionary computing research. When the mutation rate is positive, the Markov chain modeling of an evolutionary algorithm is irreducible and, therefore, has a unique stationary distribution. Rather little is known about the stationary distribution. In fact, the only quantitative facts established so far tell us that the stationary distributions of Markov chains modeling evolutionary algorithms concentrate on uniform populations (i.e., those populations consisting of a repeated copy of the same individual). At the same time, knowing the stationary distribution may provide some information about the expected time it takes for the algorithm to reach a certain solution, assessment of the biases due to recombination and selection, and is of importance in population genetics to assess what is called a "genetic load" (see the introduction for more details). In the recent joint works of the first author, some bounds have been established on the rates at which the stationary distribution concentrates on the uniform populations. The primary tool used in these papers is the "quotient construction" method. It turns out that the quotient construction method can be exploited to derive much more informative bounds on ratios of the stationary distribution values of various subsets of the state space. In fact, some of the bounds obtained in the current work are expressed in terms of the parameters involved in all the three main stages of an evolutionary algorithm: namely, selection, recombination, and mutation.

Fixed Parameter Evolutionary Algorithms and Maximum Leaf Spanning Trees: A Matter of Mutations

DEFF Research Database (Denmark)

Kratsch, Stefan; Lehre, Per Kristian; Neumann, Frank

2011-01-01

Evolutionary algorithms have been shown to be very successful for a wide range of NP-hard combinatorial optimization problems. We investigate the NP-hard problem of computing a spanning tree that has a maximal number of leaves by evolutionary algorithms in the context of fixed parameter tractabil...... two common mutation operators, we show that an operator related to spanning tree problems leads to an FPT running time in contrast to a general mutation operator that does not have this property....

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

Directory of Open Access Journals (Sweden)

Sho Fukuda

2014-12-01

Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

Evolutionary Algorithms For Neural Networks Binary And Real Data Classification

Directory of Open Access Journals (Sweden)

Dr. Hanan A.R. Akkar

2015-08-01

Full Text Available Artificial neural networks are complex networks emulating the way human rational neurons process data. They have been widely used generally in prediction clustering classification and association. The training algorithms that used to determine the network weights are almost the most important factor that influence the neural networks performance. Recently many meta-heuristic and Evolutionary algorithms are employed to optimize neural networks weights to achieve better neural performance. This paper aims to use recently proposed algorithms for optimizing neural networks weights comparing these algorithms performance with other classical meta-heuristic algorithms used for the same purpose. However to evaluate the performance of such algorithms for training neural networks we examine such algorithms to classify four opposite binary XOR clusters and classification of continuous real data sets such as Iris and Ecoli.

Analog Group Delay Equalizers Design Based on Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

M. Laipert

2006-04-01

Full Text Available This paper deals with a design method of the analog all-pass filter designated for equalization of the group delay frequency response of the analog filter. This method is based on usage of evolutionary algorithm, the Differential Evolution algorithm in particular. We are able to design such equalizers to be obtained equal-ripple group delay frequency response in the pass-band of the low-pass filter. The procedure works automatically without an input estimation. The method is presented on solving practical examples.

Reconfiguration of distribution networks to minimize loss and disruption costs using genetic algorithms

Energy Technology Data Exchange (ETDEWEB)

Cebrian, Juan Carlos; Kagan, Nelson [Department of Electrical Engineering, University of Sao Paulo, Escola Politecnica, Av. Prof. Luciano Gualberto, travessa 3 n 380 - CEP - 05508-970 - Sao Paulo (Brazil)

2010-01-15

In this paper a computational implementation of an evolutionary algorithm (EA) is shown in order to tackle the problem of reconfiguring radial distribution systems. The developed module considers power quality indices such as long duration interruptions and customer process disruptions due to voltage sags, by using the Monte Carlo simulation method. Power quality costs are modeled into the mathematical problem formulation, which are added to the cost of network losses. As for the EA codification proposed, a decimal representation is used. The EA operators, namely selection, recombination and mutation, which are considered for the reconfiguration algorithm, are herein analyzed. A number of selection procedures are analyzed, namely tournament, elitism and a mixed technique using both elitism and tournament. The recombination operator was developed by considering a chromosome structure representation that maps the network branches and system radiality, and another structure that takes into account the network topology and feasibility of network operation to exchange genetic material. The topologies regarding the initial population are randomly produced so as radial configurations are produced through the Prim and Kruskal algorithms that rapidly build minimum spanning trees. (author)

Sum-of-squares-based fuzzy controller design using quantum-inspired evolutionary algorithm

Science.gov (United States)

Yu, Gwo-Ruey; Huang, Yu-Chia; Cheng, Chih-Yung

2016-07-01

In the field of fuzzy control, control gains are obtained by solving stabilisation conditions in linear-matrix-inequality-based Takagi-Sugeno fuzzy control method and sum-of-squares-based polynomial fuzzy control method. However, the optimal performance requirements are not considered under those stabilisation conditions. In order to handle specific performance problems, this paper proposes a novel design procedure with regard to polynomial fuzzy controllers using quantum-inspired evolutionary algorithms. The first contribution of this paper is a combination of polynomial fuzzy control and quantum-inspired evolutionary algorithms to undertake an optimal performance controller design. The second contribution is the proposed stability condition derived from the polynomial Lyapunov function. The proposed design approach is dissimilar to the traditional approach, in which control gains are obtained by solving the stabilisation conditions. The first step of the controller design uses the quantum-inspired evolutionary algorithms to determine the control gains with the best performance. Then, the stability of the closed-loop system is analysed under the proposed stability conditions. To illustrate effectiveness and validity, the problem of balancing and the up-swing of an inverted pendulum on a cart is used.

EFFICIENT MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR JOB SHOP SCHEDULING

Institute of Scientific and Technical Information of China (English)

Lei Deming; Wu Zhiming

2005-01-01

A new representation method is first presented based on priority rules. According to this method, each entry in the chromosome indicates that in the procedure of the Giffler and Thompson (GT) algorithm, the conflict occurring in the corresponding machine is resolved by the corresponding priority rule. Then crowding-measure multi-objective evolutionary algorithm (CMOEA) is designed,in which both archive maintenance and fitness assignment use crowding measure. Finally the comparisons between CMOEA and SPEA in solving 15 scheduling problems demonstrate that CMOEA is suitable to job shop scheduling.

Modelling and multi-objective optimization of a variable valve-timing spark-ignition engine using polynomial neural networks and evolutionary algorithms

International Nuclear Information System (INIS)

Atashkari, K.; Nariman-Zadeh, N.; Goelcue, M.; Khalkhali, A.; Jamali, A.

2007-01-01

The main reason for the efficiency decrease at part load conditions for four-stroke spark-ignition (SI) engines is the flow restriction at the cross-sectional area of the intake system. Traditionally, valve-timing has been designed to optimize operation at high engine-speed and wide open throttle conditions. Several investigations have demonstrated that improvements at part load conditions in engine performance can be accomplished if the valve-timing is variable. Controlling valve-timing can be used to improve the torque and power curve as well as to reduce fuel consumption and emissions. In this paper, a group method of data handling (GMDH) type neural network and evolutionary algorithms (EAs) are firstly used for modelling the effects of intake valve-timing (V t ) and engine speed (N) of a spark-ignition engine on both developed engine torque (T) and fuel consumption (Fc) using some experimentally obtained training and test data. Using such obtained polynomial neural network models, a multi-objective EA (non-dominated sorting genetic algorithm, NSGA-II) with a new diversity preserving mechanism are secondly used for Pareto based optimization of the variable valve-timing engine considering two conflicting objectives such as torque (T) and fuel consumption (Fc). The comparison results demonstrate the superiority of the GMDH type models over feedforward neural network models in terms of the statistical measures in the training data, testing data and the number of hidden neurons. Further, it is shown that some interesting and important relationships, as useful optimal design principles, involved in the performance of the variable valve-timing four-stroke spark-ignition engine can be discovered by the Pareto based multi-objective optimization of the polynomial models. Such important optimal principles would not have been obtained without the use of both the GMDH type neural network modelling and the multi-objective Pareto optimization approach

Expert-guided evolutionary algorithm for layout design of complex space stations

Science.gov (United States)

Qian, Zhiqin; Bi, Zhuming; Cao, Qun; Ju, Weiguo; Teng, Hongfei; Zheng, Yang; Zheng, Siyu

2017-08-01

The layout of a space station should be designed in such a way that different equipment and instruments are placed for the station as a whole to achieve the best overall performance. The station layout design is a typical nondeterministic polynomial problem. In particular, how to manage the design complexity to achieve an acceptable solution within a reasonable timeframe poses a great challenge. In this article, a new evolutionary algorithm has been proposed to meet such a challenge. It is called as the expert-guided evolutionary algorithm with a tree-like structure decomposition (EGEA-TSD). Two innovations in EGEA-TSD are (i) to deal with the design complexity, the entire design space is divided into subspaces with a tree-like structure; it reduces the computation and facilitates experts' involvement in the solving process. (ii) A human-intervention interface is developed to allow experts' involvement in avoiding local optimums and accelerating convergence. To validate the proposed algorithm, the layout design of one-space station is formulated as a multi-disciplinary design problem, the developed algorithm is programmed and executed, and the result is compared with those from other two algorithms; it has illustrated the superior performance of the proposed EGEA-TSD.

AN EVOLUTIONARY ALGORITHM FOR FAST INTENSITY BASED IMAGE MATCHING BETWEEN OPTICAL AND SAR SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

P. Fischer

2018-04-01

Full Text Available This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

Harmonic elimination in diode-clamped multilevel inverter using evolutionary algorithms

Energy Technology Data Exchange (ETDEWEB)

Barkati, Said [Laboratoire d' analyse des Signaux et Systemes (LASS), Universite de M' sila, BP. 166, rue Ichbilia 28000 M' sila (Algeria); Baghli, Lotfi [Groupe de Recherche en Electrotechnique et Electronique de Nancy (GREEN), CNRS UMR 7030, Universite Henri Poincare Nancy 1, BP. 239, 54506 Vandoeuvre-les-Nancy (France); Berkouk, El Madjid; Boucherit, Mohamed-Seghir [Laboratoire de Commande des Processus (LCP), Ecole Nationale Polytechnique, BP. 182, 10 Avenue Hassen Badi, 16200 El Harrach, Alger (Algeria)

2008-10-15

This paper describes two evolutionary algorithms for the optimized harmonic stepped-waveform technique. Genetic algorithms and particle swarm optimization are applied to compute the switching angles in a three-phase seven-level inverter to produce the required fundamental voltage while, at the same time, specified harmonics are eliminated. Furthermore, these algorithms are also used to solve the starting point problem of the Newton-Raphson conventional method. This combination provides a very effective method for the harmonic elimination technique. This strategy is useful for different structures of seven-level inverters. The diode-clamped topology is considered in this study. (author)

General upper bounds on the runtime of parallel evolutionary algorithms.

Science.gov (United States)

Lässig, Jörg; Sudholt, Dirk

2014-01-01

We present a general method for analyzing the runtime of parallel evolutionary algorithms with spatially structured populations. Based on the fitness-level method, it yields upper bounds on the expected parallel runtime. This allows for a rigorous estimate of the speedup gained by parallelization. Tailored results are given for common migration topologies: ring graphs, torus graphs, hypercubes, and the complete graph. Example applications for pseudo-Boolean optimization show that our method is easy to apply and that it gives powerful results. In our examples the performance guarantees improve with the density of the topology. Surprisingly, even sparse topologies such as ring graphs lead to a significant speedup for many functions while not increasing the total number of function evaluations by more than a constant factor. We also identify which number of processors lead to the best guaranteed speedups, thus giving hints on how to parameterize parallel evolutionary algorithms.

Comparison of evolutionary algorithms in gene regulatory network model inference.

LENUS (Irish Health Repository)

2010-01-01

ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.

Hybrid Microgrid Configuration Optimization with Evolutionary Algorithms

Science.gov (United States)

Lopez, Nicolas

This dissertation explores the Renewable Energy Integration Problem, and proposes a Genetic Algorithm embedded with a Monte Carlo simulation to solve large instances of the problem that are impractical to solve via full enumeration. The Renewable Energy Integration Problem is defined as finding the optimum set of components to supply the electric demand to a hybrid microgrid. The components considered are solar panels, wind turbines, diesel generators, electric batteries, connections to the power grid and converters, which can be inverters and/or rectifiers. The methodology developed is explained as well as the combinatorial formulation. In addition, 2 case studies of a single objective optimization version of the problem are presented, in order to minimize cost and to minimize global warming potential (GWP) followed by a multi-objective implementation of the offered methodology, by utilizing a non-sorting Genetic Algorithm embedded with a monte Carlo Simulation. The method is validated by solving a small instance of the problem with known solution via a full enumeration algorithm developed by NREL in their software HOMER. The dissertation concludes that the evolutionary algorithms embedded with Monte Carlo simulation namely modified Genetic Algorithms are an efficient form of solving the problem, by finding approximate solutions in the case of single objective optimization, and by approximating the true Pareto front in the case of multiple objective optimization of the Renewable Energy Integration Problem.

Designing a parallel evolutionary algorithm for inferring gene networks on the cloud computing environment.

Science.gov (United States)

Lee, Wei-Po; Hsiao, Yu-Ting; Hwang, Wei-Che

2014-01-16

To improve the tedious task of reconstructing gene networks through testing experimentally the possible interactions between genes, it becomes a trend to adopt the automated reverse engineering procedure instead. Some evolutionary algorithms have been suggested for deriving network parameters. However, to infer large networks by the evolutionary algorithm, it is necessary to address two important issues: premature convergence and high computational cost. To tackle the former problem and to enhance the performance of traditional evolutionary algorithms, it is advisable to use parallel model evolutionary algorithms. To overcome the latter and to speed up the computation, it is advocated to adopt the mechanism of cloud computing as a promising solution: most popular is the method of MapReduce programming model, a fault-tolerant framework to implement parallel algorithms for inferring large gene networks. This work presents a practical framework to infer large gene networks, by developing and parallelizing a hybrid GA-PSO optimization method. Our parallel method is extended to work with the Hadoop MapReduce programming model and is executed in different cloud computing environments. To evaluate the proposed approach, we use a well-known open-source software GeneNetWeaver to create several yeast S. cerevisiae sub-networks and use them to produce gene profiles. Experiments have been conducted and the results have been analyzed. They show that our parallel approach can be successfully used to infer networks with desired behaviors and the computation time can be largely reduced. Parallel population-based algorithms can effectively determine network parameters and they perform better than the widely-used sequential algorithms in gene network inference. These parallel algorithms can be distributed to the cloud computing environment to speed up the computation. By coupling the parallel model population-based optimization method and the parallel computational framework, high

A kNN method that uses a non-natural evolutionary algorithm for ...

African Journals Online (AJOL)

We used this algorithm for component selection of a kNN (k Nearest Neighbor) method for breast cancer prognosis. Results with the UCI prognosis data set show that we can find components that help improve the accuracy of kNN by almost 3%, raising it above 79%. Keywords: kNN; classification; evolutionary algorithm; ...

Low emittance lattice optimization using a multi-objective evolutionary algorithm

International Nuclear Information System (INIS)

Gao Weiwei; Wang Lin; Li Weimin; He Duohui

2011-01-01

A low emittance lattice design and optimization procedure are systematically studied with a non-dominated sorting-based multi-objective evolutionary algorithm which not only globally searches the low emittance lattice, but also optimizes some beam quantities such as betatron tunes, momentum compaction factor and dispersion function simultaneously. In this paper the detailed algorithm and lattice design procedure are presented. The Hefei light source upgrade project storage ring lattice, with fixed magnet layout, is designed to illustrate this optimization procedure. (authors)

Parameterless evolutionary algorithm applied to the nuclear reload problem

International Nuclear Information System (INIS)

Caldas, Gustavo Henrique Flores; Schirru, Roberto

2008-01-01

In this work, an evolutionary algorithm with no parameters called FPBIL (parameter free PBIL) is developed based on PBIL (population-based incremental learning). Moreover, the analysis reveals how the parameters from PBIL can be replaced by self-adaptable mechanisms which appear from the radically different form by which the evolution is processed. Despite the advantages, the FPBIL reveals itself compact and relatively modest in the use of computational resources. The FPBIL is then applied to the nuclear reload problem. The experimental results observed are compared to those of other works and corroborate to affirm the superiority of the new algorithm

Identification of Water Diffusivity of Inorganic Porous Materials Using Evolutionary Algorithms

Czech Academy of Sciences Publication Activity Database

Kočí, J.; Maděra, J.; Jerman, M.; Keppert, M.; Svora, Petr; Černý, R.

2016-01-01

Roč. 113, č. 1 (2016), s. 51-66 ISSN 0169-3913 Institutional support: RVO:61388980 Keywords : Evolutionary algorithms * Water transport * Inorganic porous materials * Inverse analysis Subject RIV: CA - Inorganic Chemistry Impact factor: 2.205, year: 2016

An encoding technique for multiobjective evolutionary algorithms applied to power distribution system reconfiguration.

Science.gov (United States)

Guardado, J L; Rivas-Davalos, F; Torres, J; Maximov, S; Melgoza, E

2014-01-01

Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.

Comparison of some evolutionary algorithms for optimization of the path synthesis problem

Science.gov (United States)

Grabski, Jakub Krzysztof; Walczak, Tomasz; Buśkiewicz, Jacek; Michałowska, Martyna

2018-01-01

The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study.

The mixing evolutionary algorithm : indepedent selection and allocation of trials

NARCIS (Netherlands)

C.H.M. van Kemenade

1997-01-01

textabstractWhen using an evolutionary algorithm to solve a problem involving building blocks we have to grow the building blocks and then mix these building blocks to obtain the (optimal) solution. Finding a good balance between the growing and the mixing process is a prerequisite to get a reliable

DNA evolutionary algorithm (DNAEA) for source term identification in convection-diffusion equation

International Nuclear Information System (INIS)

Yang, X-H; Hu, X-X; Shen, Z-Y

2008-01-01

The source identification problem is changed into an optimization problem in this paper. This is a complicated nonlinear optimization problem. It is very intractable with traditional optimization methods. So DNA evolutionary algorithm (DNAEA) is presented to solve the discussed problem. In this algorithm, an initial population is generated by a chaos algorithm. With the shrinking of searching range, DNAEA gradually directs to an optimal result with excellent individuals obtained by DNAEA. The position and intensity of pollution source are well found with DNAEA. Compared with Gray-coded genetic algorithm and pure random search algorithm, DNAEA has rapider convergent speed and higher calculation precision

Large-Scale Portfolio Optimization Using Multiobjective Evolutionary Algorithms and Preselection Methods

Directory of Open Access Journals (Sweden)

B. Y. Qu

2017-01-01

Full Text Available Portfolio optimization problems involve selection of different assets to invest in order to maximize the overall return and minimize the overall risk simultaneously. The complexity of the optimal asset allocation problem increases with an increase in the number of assets available to select from for investing. The optimization problem becomes computationally challenging when there are more than a few hundreds of assets to select from. To reduce the complexity of large-scale portfolio optimization, two asset preselection procedures that consider return and risk of individual asset and pairwise correlation to remove assets that may not potentially be selected into any portfolio are proposed in this paper. With these asset preselection methods, the number of assets considered to be included in a portfolio can be increased to thousands. To test the effectiveness of the proposed methods, a Normalized Multiobjective Evolutionary Algorithm based on Decomposition (NMOEA/D algorithm and several other commonly used multiobjective evolutionary algorithms are applied and compared. Six experiments with different settings are carried out. The experimental results show that with the proposed methods the simulation time is reduced while return-risk trade-off performances are significantly improved. Meanwhile, the NMOEA/D is able to outperform other compared algorithms on all experiments according to the comparative analysis.

Evolutionary algorithms for the Vehicle Routing Problem with Time Windows

NARCIS (Netherlands)

Bräysy, Olli; Dullaert, Wout; Gendreau, Michel

2004-01-01

This paper surveys the research on evolutionary algorithms for the Vehicle Routing Problem with Time Windows (VRPTW). The VRPTW can be described as the problem of designing least cost routes from a single depot to a set of geographically scattered points. The routes must be designed in such a way

A Novel Evolutionary Algorithm Inspired by Beans Dispersal

Directory of Open Access Journals (Sweden)

Xiaoming Zhang

2013-02-01

Full Text Available Inspired by the transmission of beans in nature, a novel evolutionary algorithm-Bean Optimization Algorithm (BOA is proposed in this paper. BOA is mainly based on the normal distribution which is an important continuous probability distribution of quantitative phenomena. Through simulating the self-adaptive phenomena of plant, BOA is designed for solving continuous optimization problems. We also analyze the global convergence of BOA by using the Solis and Wetsarsquo; research results. The conclusion is that BOA can converge to the global optimization solution with probability one. In order to validate its effectiveness, BOA is tested against benchmark functions. And its performance is also compared with that of particle swarm optimization (PSO algorithm. The experimental results show that BOA has competitive performance to PSO in terms of accuracy and convergence speed on the explored tests and stands out as a promising alternative to existing optimization methods for engineering designs or applications.

XTALOPT: An open-source evolutionary algorithm for crystal structure prediction

Science.gov (United States)

Lonie, David C.; Zurek, Eva

2011-02-01

The implementation and testing of XTALOPT, an evolutionary algorithm for crystal structure prediction, is outlined. We present our new periodic displacement (ripple) operator which is ideally suited to extended systems. It is demonstrated that hybrid operators, which combine two pure operators, reduce the number of duplicate structures in the search. This allows for better exploration of the potential energy surface of the system in question, while simultaneously zooming in on the most promising regions. A continuous workflow, which makes better use of computational resources as compared to traditional generation based algorithms, is employed. Various parameters in XTALOPT are optimized using a novel benchmarking scheme. XTALOPT is available under the GNU Public License, has been interfaced with various codes commonly used to study extended systems, and has an easy to use, intuitive graphical interface. Program summaryProgram title:XTALOPT Catalogue identifier: AEGX_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGX_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: GPL v2.1 or later [1] No. of lines in distributed program, including test data, etc.: 36 849 No. of bytes in distributed program, including test data, etc.: 1 149 399 Distribution format: tar.gz Programming language: C++ Computer: PCs, workstations, or clusters Operating system: Linux Classification: 7.7 External routines: QT [2], OpenBabel [3], AVOGADRO [4], SPGLIB [8] and one of: VASP [5], PWSCF [6], GULP [7]. Nature of problem: Predicting the crystal structure of a system from its stoichiometry alone remains a grand challenge in computational materials science, chemistry, and physics. Solution method: Evolutionary algorithms are stochastic search techniques which use concepts from biological evolution in order to locate the global minimum on their potential energy surface. Our evolutionary algorithm, XTALOPT, is freely

An efficient non-dominated sorting method for evolutionary algorithms.

Science.gov (United States)

Fang, Hongbing; Wang, Qian; Tu, Yi-Cheng; Horstemeyer, Mark F

2008-01-01

We present a new non-dominated sorting algorithm to generate the non-dominated fronts in multi-objective optimization with evolutionary algorithms, particularly the NSGA-II. The non-dominated sorting algorithm used by NSGA-II has a time complexity of O(MN(2)) in generating non-dominated fronts in one generation (iteration) for a population size N and M objective functions. Since generating non-dominated fronts takes the majority of total computational time (excluding the cost of fitness evaluations) of NSGA-II, making this algorithm faster will significantly improve the overall efficiency of NSGA-II and other genetic algorithms using non-dominated sorting. The new non-dominated sorting algorithm proposed in this study reduces the number of redundant comparisons existing in the algorithm of NSGA-II by recording the dominance information among solutions from their first comparisons. By utilizing a new data structure called the dominance tree and the divide-and-conquer mechanism, the new algorithm is faster than NSGA-II for different numbers of objective functions. Although the number of solution comparisons by the proposed algorithm is close to that of NSGA-II when the number of objectives becomes large, the total computational time shows that the proposed algorithm still has better efficiency because of the adoption of the dominance tree structure and the divide-and-conquer mechanism.

An Encoding Technique for Multiobjective Evolutionary Algorithms Applied to Power Distribution System Reconfiguration

Directory of Open Access Journals (Sweden)

J. L. Guardado

2014-01-01

Full Text Available Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2 and the Nondominated Sorting Genetic Algorithm II (NSGA-II. The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.

Optimal Mutation Rates for the (1+lambda) EA on OneMax Through Asymptotically Tight Drift Analysis

DEFF Research Database (Denmark)

Gießen, Christian; Witt, Carsten

2018-01-01

We study the (1+) EA, a classical population-based evolutionary algorithm, with mutation probability c / n, where and are constant, on the benchmark function OneMax, which counts the number of 1-bits in a bitstring. We improve a well-established result that allows to determine the first hitting t...... that mutation rates up to 10% larger than the asymptotically optimal rate 1 / n minimize the expected runtime. However, in absolute terms the expected runtime does not change by much when replacing 1 / n with the optimal mutation rate....... drift is known. This reduces the analysis of expected optimization time to finding an exact expression for the drift. We then give an exact closed-form expression for the drift and develop a method to approximate it very efficiently, enabling us to determine approximate optimal mutation rates for the (1......+) EA for various parameter settings of c and and also for moderate sizes of n. This makes the need for potentially lengthy and costly experiments in order to optimize c for fixed n and for the optimization of OneMax unnecessary. Interestingly, even for moderate n and not too small it turns out...

A New Evolutionary Algorithm Based on Bacterial Evolution and Its Application for Scheduling A Flexible Manufacturing System

Directory of Open Access Journals (Sweden)

Chandramouli Anandaraman

2012-01-01

Full Text Available A new evolutionary computation algorithm, Superbug algorithm, which simulates evolution of bacteria in a culture, is proposed. The algorithm is developed for solving large scale optimization problems such as scheduling, transportation and assignment problems. In this work, the algorithm optimizes machine schedules in a Flexible Manufacturing System (FMS by minimizing makespan. The FMS comprises of four machines and two identical Automated Guided Vehicles (AGVs. AGVs are used for carrying jobs between the Load/Unload (L/U station and the machines. Experimental results indicate the efficiency of the proposed algorithm in its optimization performance in scheduling is noticeably superior to other evolutionary algorithms when compared to the best results reported in the literature for FMS Scheduling.

Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms.

Science.gov (United States)

Bianchi, Emanuela; Doppelbauer, Günther; Filion, Laura; Dijkstra, Marjolein; Kahl, Gerhard

2012-06-07

We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the isobaric-isothermal ensemble and (ii) an optimization technique based on ideas of evolutionary algorithms. We show that the two methods are equally successful and provide consistent results on crystalline phases of patchy particle systems.

The Application of Fitness Sharing Method in Evolutionary Algorithm to Optimizing the Travelling Salesman Problem (TSP

Directory of Open Access Journals (Sweden)

Nurmaulidar Nurmaulidar

2015-04-01

Full Text Available Travelling Salesman Problem (TSP is one of complex optimization problem that is difficult to be solved, and require quite a long time for a large number of cities. Evolutionary algorithm is a precise algorithm used in solving complex optimization problem as it is part of heuristic method. Evolutionary algorithm, like many other algorithms, also experiences a premature convergence phenomenon, whereby variation is eliminated from a population of fairly fit individuals before a complete solution is achieved. Therefore it requires a method to delay the convergence. A specific method of fitness sharing called phenotype fitness sharing has been used in this research. The aim of this research is to find out whether fitness sharing in evolutionary algorithm is able to optimize TSP. There are two concepts of evolutionary algorithm being used in this research. the first one used single elitism and the other one used federated solution. The two concepts had been tested to the method of fitness sharing by using the threshold of 0.25, 0.50 and 0.75. The result was then compared to a non fitness sharing method. The result in this study indicated that by using single elitism concept, fitness sharing was able to give a more optimum result for the data of 100-1000 cities. On the other hand, by using federation solution concept, fitness sharing can yield a more optimum result for the data above 1000 cities, as well as a better solution of data-spreading compared to the method without fitness sharing.

Swarm, genetic and evolutionary programming algorithms applied to multiuser detection

Directory of Open Access Journals (Sweden)

Paul Jean Etienne Jeszensky

2005-02-01

Full Text Available In this paper, the particles swarm optimization technique, recently published in the literature, and applied to Direct Sequence/Code Division Multiple Access systems (DS/CDMA with multiuser detection (MuD is analyzed, evaluated and compared. The Swarm algorithm efficiency when applied to the DS-CDMA multiuser detection (Swarm-MuD is compared through the tradeoff performance versus computational complexity, being the complexity expressed in terms of the number of necessary operations in order to reach the performance obtained through the optimum detector or the Maximum Likelihood detector (ML. The comparison is accomplished among the genetic algorithm, evolutionary programming with cloning and Swarm algorithm under the same simulation basis. Additionally, it is proposed an heuristics-MuD complexity analysis through the number of computational operations. Finally, an analysis is carried out for the input parameters of the Swarm algorithm in the attempt to find the optimum parameters (or almost-optimum for the algorithm applied to the MuD problem.

Virus evolutionary genetic algorithm for task collaboration of logistics distribution

Science.gov (United States)

Ning, Fanghua; Chen, Zichen; Xiong, Li

2005-12-01

In order to achieve JIT (Just-In-Time) level and clients' maximum satisfaction in logistics collaboration, a Virus Evolutionary Genetic Algorithm (VEGA) was put forward under double constraints of logistics resource and operation sequence. Based on mathematic description of a multiple objective function, the algorithm was designed to schedule logistics tasks with different due dates and allocate them to network members. By introducing a penalty item, make span and customers' satisfaction were expressed in fitness function. And a dynamic adaptive probability of infection was used to improve performance of local search. Compared to standard Genetic Algorithm (GA), experimental result illustrates the performance superiority of VEGA. So the VEGA can provide a powerful decision-making technique for optimizing resource configuration in logistics network.

Self-organized modularization in evolutionary algorithms.

Science.gov (United States)

Dauscher, Peter; Uthmann, Thomas

2005-01-01

The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularity as an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularity in comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).

A Comparison of Evolutionary Algorithms for Tracking Time-Varying Recursive Systems

Directory of Open Access Journals (Sweden)

White Michael S

2003-01-01

Full Text Available A comparison is made of the behaviour of some evolutionary algorithms in time-varying adaptive recursive filter systems. Simulations show that an algorithm including random immigrants outperforms a more conventional algorithm using the breeder genetic algorithm as the mutation operator when the time variation is discontinuous, but neither algorithm performs well when the time variation is rapid but smooth. To meet this deficit, a new hybrid algorithm which uses a hill climber as an additional genetic operator, applied for several steps at each generation, is introduced. A comparison is made of the effect of applying the hill climbing operator a few times to all members of the population or a larger number of times solely to the best individual; it is found that applying to the whole population yields the better results, substantially improved compared with those obtained using earlier methods.

A Novel Evolutionary Algorithm for Designing Robust Analog Filters

Directory of Open Access Journals (Sweden)

Shaobo Li

2018-03-01

Full Text Available Designing robust circuits that withstand environmental perturbation and device degradation is critical for many applications. Traditional robust circuit design is mainly done by tuning parameters to improve system robustness. However, the topological structure of a system may set a limit on the robustness achievable through parameter tuning. This paper proposes a new evolutionary algorithm for robust design that exploits the open-ended topological search capability of genetic programming (GP coupled with bond graph modeling. We applied our GP-based robust design (GPRD algorithm to evolve robust lowpass and highpass analog filters. Compared with a traditional robust design approach based on a state-of-the-art real-parameter genetic algorithm (GA, our GPRD algorithm with a fitness criterion rewarding robustness, with respect to parameter perturbations, can evolve more robust filters than what was achieved through parameter tuning alone. We also find that inappropriate GA tuning may mislead the search process and that multiple-simulation and perturbed fitness evaluation methods for evolving robustness have complementary behaviors with no absolute advantage of one over the other.

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

Directory of Open Access Journals (Sweden)

Leilei Cao

2016-01-01

Full Text Available A Guiding Evolutionary Algorithm (GEA with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

Comparing the Robustness of Evolutionary Algorithms on the Basis of Benchmark Functions

Directory of Open Access Journals (Sweden)

DENIZ ULKER, E.

2013-05-01

Full Text Available In real-world optimization problems, even though the solution quality is of great importance, the robustness of the solution is also an important aspect. This paper investigates how the optimization algorithms are sensitive to the variations of control parameters and to the random initialization of the solution set for fixed control parameters. The comparison is performed of three well-known evolutionary algorithms which are Particle Swarm Optimization (PSO algorithm, Differential Evolution (DE algorithm and the Harmony Search (HS algorithm. Various benchmark functions with different characteristics are used for the evaluation of these algorithms. The experimental results show that the solution quality of the algorithms is not directly related to their robustness. In particular, the algorithm that is highly robust can have a low solution quality, or the algorithm that has a high quality of solution can be quite sensitive to the parameter variations.

A Multiagent Evolutionary Algorithm for the Resource-Constrained Project Portfolio Selection and Scheduling Problem

Directory of Open Access Journals (Sweden)

Yongyi Shou

2014-01-01

Full Text Available A multiagent evolutionary algorithm is proposed to solve the resource-constrained project portfolio selection and scheduling problem. The proposed algorithm has a dual level structure. In the upper level a set of agents make decisions to select appropriate project portfolios. Each agent selects its project portfolio independently. The neighborhood competition operator and self-learning operator are designed to improve the agent’s energy, that is, the portfolio profit. In the lower level the selected projects are scheduled simultaneously and completion times are computed to estimate the expected portfolio profit. A priority rule-based heuristic is used by each agent to solve the multiproject scheduling problem. A set of instances were generated systematically from the widely used Patterson set. Computational experiments confirmed that the proposed evolutionary algorithm is effective for the resource-constrained project portfolio selection and scheduling problem.

Multi-objective optimization of HVAC system with an evolutionary computation algorithm

International Nuclear Information System (INIS)

Kusiak, Andrew; Tang, Fan; Xu, Guanglin

2011-01-01

A data-mining approach for the optimization of a HVAC (heating, ventilation, and air conditioning) system is presented. A predictive model of the HVAC system is derived by data-mining algorithms, using a dataset collected from an experiment conducted at a research facility. To minimize the energy while maintaining the corresponding IAQ (indoor air quality) within a user-defined range, a multi-objective optimization model is developed. The solutions of this model are set points of the control system derived with an evolutionary computation algorithm. The controllable input variables - supply air temperature and supply air duct static pressure set points - are generated to reduce the energy use. The results produced by the evolutionary computation algorithm show that the control strategy saves energy by optimizing operations of an HVAC system. -- Highlights: → A data-mining approach for the optimization of a heating, ventilation, and air conditioning (HVAC) system is presented. → The data used in the project has been collected from an experiment conducted at an energy research facility. → The approach presented in the paper leads to accomplishing significant energy savings without compromising the indoor air quality. → The energy savings are accomplished by computing set points for the supply air temperature and the supply air duct static pressure.

Academic Training: Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms - Lecture serie

CERN Multimedia

Françoise Benz

2004-01-01

ENSEIGNEMENT ACADEMIQUE ACADEMIC TRAINING Françoise Benz 73127 academic.training@cern.ch ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on nat...

An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints

Directory of Open Access Journals (Sweden)

Jinmo Sung

2014-01-01

Full Text Available Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments.

Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem

DEFF Research Database (Denmark)

Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

2015-01-01

Non-trivial real world decision-making processes usually involve multiple parties having potentially conflicting interests over a set of issues. State-of-the-art multi-objective evolutionary algorithms (MOEA) are well known to solve this class of complex real-world problems. In this paper, we...... compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...... of all aforementioned algorithms is assessed and compared using performance indicators to evaluate proximity, diversity and consistency. Our insights to this comparative study enhanced our understanding of MOEAs performance in order to solve a non-linear complex climate control problem. The empirical...

An Analytical Framework for Runtime of a Class of Continuous Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Yushan Zhang

2015-01-01

Full Text Available Although there have been many studies on the runtime of evolutionary algorithms in discrete optimization, relatively few theoretical results have been proposed on continuous optimization, such as evolutionary programming (EP. This paper proposes an analysis of the runtime of two EP algorithms based on Gaussian and Cauchy mutations, using an absorbing Markov chain. Given a constant variation, we calculate the runtime upper bound of special Gaussian mutation EP and Cauchy mutation EP. Our analysis reveals that the upper bounds are impacted by individual number, problem dimension number n, searching range, and the Lebesgue measure of the optimal neighborhood. Furthermore, we provide conditions whereby the average runtime of the considered EP can be no more than a polynomial of n. The condition is that the Lebesgue measure of the optimal neighborhood is larger than a combinatorial calculation of an exponential and the given polynomial of n.

Optimal Scheduling for Retrieval Jobs in Double-Deep AS/RS by Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Kuo-Yang Wu

2013-01-01

Full Text Available We investigate the optimal scheduling of retrieval jobs for double-deep type Automated Storage and Retrieval Systems (AS/RS in the Flexible Manufacturing System (FMS used in modern industrial production. Three types of evolutionary algorithms, the Genetic Algorithm (GA, the Immune Genetic Algorithm (IGA, and the Particle Swarm Optimization (PSO algorithm, are implemented to obtain the optimal assignments. The objective is to minimize the working distance, that is, the shortest retrieval time travelled by the Storage and Retrieval (S/R machine. Simulation results and comparisons show the advantages and feasibility of the proposed methods.

Chaotic Multiobjective Evolutionary Algorithm Based on Decomposition for Test Task Scheduling Problem

Directory of Open Access Journals (Sweden)

Hui Lu

2014-01-01

Full Text Available Test task scheduling problem (TTSP is a complex optimization problem and has many local optima. In this paper, a hybrid chaotic multiobjective evolutionary algorithm based on decomposition (CMOEA/D is presented to avoid becoming trapped in local optima and to obtain high quality solutions. First, we propose an improving integrated encoding scheme (IES to increase the efficiency. Then ten chaotic maps are applied into the multiobjective evolutionary algorithm based on decomposition (MOEA/D in three phases, that is, initial population and crossover and mutation operators. To identify a good approach for hybrid MOEA/D and chaos and indicate the effectiveness of the improving IES several experiments are performed. The Pareto front and the statistical results demonstrate that different chaotic maps in different phases have different effects for solving the TTSP especially the circle map and ICMIC map. The similarity degree of distribution between chaotic maps and the problem is a very essential factor for the application of chaotic maps. In addition, the experiments of comparisons of CMOEA/D and variable neighborhood MOEA/D (VNM indicate that our algorithm has the best performance in solving the TTSP.

Base Station Placement Algorithm for Large-Scale LTE Heterogeneous Networks.

Science.gov (United States)

Lee, Seungseob; Lee, SuKyoung; Kim, Kyungsoo; Kim, Yoon Hyuk

2015-01-01

Data traffic demands in cellular networks today are increasing at an exponential rate, giving rise to the development of heterogeneous networks (HetNets), in which small cells complement traditional macro cells by extending coverage to indoor areas. However, the deployment of small cells as parts of HetNets creates a key challenge for operators' careful network planning. In particular, massive and unplanned deployment of base stations can cause high interference, resulting in highly degrading network performance. Although different mathematical modeling and optimization methods have been used to approach various problems related to this issue, most traditional network planning models are ill-equipped to deal with HetNet-specific characteristics due to their focus on classical cellular network designs. Furthermore, increased wireless data demands have driven mobile operators to roll out large-scale networks of small long term evolution (LTE) cells. Therefore, in this paper, we aim to derive an optimum network planning algorithm for large-scale LTE HetNets. Recently, attempts have been made to apply evolutionary algorithms (EAs) to the field of radio network planning, since they are characterized as global optimization methods. Yet, EA performance often deteriorates rapidly with the growth of search space dimensionality. To overcome this limitation when designing optimum network deployments for large-scale LTE HetNets, we attempt to decompose the problem and tackle its subcomponents individually. Particularly noting that some HetNet cells have strong correlations due to inter-cell interference, we propose a correlation grouping approach in which cells are grouped together according to their mutual interference. Both the simulation and analytical results indicate that the proposed solution outperforms the random-grouping based EA as well as an EA that detects interacting variables by monitoring the changes in the objective function algorithm in terms of system

An improved shuffled frog leaping algorithm based evolutionary framework for currency exchange rate prediction

Science.gov (United States)

Dash, Rajashree

2017-11-01

Forecasting purchasing power of one currency with respect to another currency is always an interesting topic in the field of financial time series prediction. Despite the existence of several traditional and computational models for currency exchange rate forecasting, there is always a need for developing simpler and more efficient model, which will produce better prediction capability. In this paper, an evolutionary framework is proposed by using an improved shuffled frog leaping (ISFL) algorithm with a computationally efficient functional link artificial neural network (CEFLANN) for prediction of currency exchange rate. The model is validated by observing the monthly prediction measures obtained for three currency exchange data sets such as USD/CAD, USD/CHF, and USD/JPY accumulated within same period of time. The model performance is also compared with two other evolutionary learning techniques such as Shuffled frog leaping algorithm and Particle Swarm optimization algorithm. Practical analysis of results suggest that, the proposed model developed using the ISFL algorithm with CEFLANN network is a promising predictor model for currency exchange rate prediction compared to other models included in the study.

A Novel Multiobjective Evolutionary Algorithm Based on Regression Analysis

Directory of Open Access Journals (Sweden)

Zhiming Song

2015-01-01

Full Text Available As is known, the Pareto set of a continuous multiobjective optimization problem with m objective functions is a piecewise continuous (m-1-dimensional manifold in the decision space under some mild conditions. However, how to utilize the regularity to design multiobjective optimization algorithms has become the research focus. In this paper, based on this regularity, a model-based multiobjective evolutionary algorithm with regression analysis (MMEA-RA is put forward to solve continuous multiobjective optimization problems with variable linkages. In the algorithm, the optimization problem is modelled as a promising area in the decision space by a probability distribution, and the centroid of the probability distribution is (m-1-dimensional piecewise continuous manifold. The least squares method is used to construct such a model. A selection strategy based on the nondominated sorting is used to choose the individuals to the next generation. The new algorithm is tested and compared with NSGA-II and RM-MEDA. The result shows that MMEA-RA outperforms RM-MEDA and NSGA-II on the test instances with variable linkages. At the same time, MMEA-RA has higher efficiency than the other two algorithms. A few shortcomings of MMEA-RA have also been identified and discussed in this paper.

A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain-machine interface systems

Science.gov (United States)

Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

2018-04-01

Objective. Considering the importance and the near-future development of noninvasive brain-machine interface (BMI) systems, this paper presents a comprehensive theoretical-experimental survey on the classification and evolutionary methods for BMI-based systems in which EEG signals are used. Approach. The paper is divided into two main parts. In the first part, a wide range of different types of the base and combinatorial classifiers including boosting and bagging classifiers and evolutionary algorithms are reviewed and investigated. In the second part, these classifiers and evolutionary algorithms are assessed and compared based on two types of relatively widely used BMI systems, sensory motor rhythm-BMI and event-related potentials-BMI. Moreover, in the second part, some of the improved evolutionary algorithms as well as bi-objective algorithms are experimentally assessed and compared. Main results. In this study two databases are used, and cross-validation accuracy (CVA) and stability to data volume (SDV) are considered as the evaluation criteria for the classifiers. According to the experimental results on both databases, regarding the base classifiers, linear discriminant analysis and support vector machines with respect to CVA evaluation metric, and naive Bayes with respect to SDV demonstrated the best performances. Among the combinatorial classifiers, four classifiers, Bagg-DT (bagging decision tree), LogitBoost, and GentleBoost with respect to CVA, and Bagging-LR (bagging logistic regression) and AdaBoost (adaptive boosting) with respect to SDV had the best performances. Finally, regarding the evolutionary algorithms, single-objective invasive weed optimization (IWO) and bi-objective nondominated sorting IWO algorithms demonstrated the best performances. Significance. We present a general survey on the base and the combinatorial classification methods for EEG signals (sensory motor rhythm and event-related potentials) as well as their optimization methods

Analysis of Various Multi-Objective Optimization Evolutionary Algorithms for Monte Carlo Treatment Planning System

CERN Document Server

Tydrichova, Magdalena

2017-01-01

In this project, various available multi-objective optimization evolutionary algorithms were compared considering their performance and distribution of solutions. The main goal was to select the most suitable algorithms for applications in cancer hadron therapy planning. For our purposes, a complex testing and analysis software was developed. Also, many conclusions and hypothesis have been done for the further research.

Synthesis of Steered Flat-top Beam Pattern Using Evolutionary Algorithm

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D. Mandal

2016-12-01

Full Text Available In this paper a pattern synthesis method based on Evolutionary Algorithm is presented. A Flat-top beam pattern has been generated from a concentric ring array of isotropic elements by finding out the optimum set of elements amplitudes and phases using Differential Evolution algorithm. The said pattern is generated in three predefined azimuth planes instate of a single phi plane and also verified for a range of azimuth plane for the same optimum excitations. The main beam is steered to an elevation angle of 30 degree with lower peak SLL and ripple. Dynamic range ratio (DRR is also being improved by eliminating the weakly excited array elements, which simplify the design complexity of feed networks.

A new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting

International Nuclear Information System (INIS)

Wang, Bo; Tai, Neng-ling; Zhai, Hai-qing; Ye, Jian; Zhu, Jia-dong; Qi, Liang-bo

2008-01-01

In this paper, a new ARMAX model based on evolutionary algorithm and particle swarm optimization for short-term load forecasting is proposed. Auto-regressive (AR) and moving average (MA) with exogenous variables (ARMAX) has been widely applied in the load forecasting area. Because of the nonlinear characteristics of the power system loads, the forecasting function has many local optimal points. The traditional method based on gradient searching may be trapped in local optimal points and lead to high error. While, the hybrid method based on evolutionary algorithm and particle swarm optimization can solve this problem more efficiently than the traditional ways. It takes advantage of evolutionary strategy to speed up the convergence of particle swarm optimization (PSO), and applies the crossover operation of genetic algorithm to enhance the global search ability. The new ARMAX model for short-term load forecasting has been tested based on the load data of Eastern China location market, and the results indicate that the proposed approach has achieved good accuracy. (author)

ANTQ evolutionary algorithm applied to nuclear fuel reload problem

International Nuclear Information System (INIS)

Machado, Liana; Schirru, Roberto

2000-01-01

Nuclear fuel reload optimization is a NP-complete combinatorial optimization problem where the aim is to find fuel rods' configuration that maximizes burnup or minimizes the power peak factor. For decades this problem was solved exclusively using an expert's knowledge. From the eighties, however, there have been efforts to automatize fuel reload. The first relevant effort used Simulated Annealing, but more recent publications show Genetic Algorithm's (GA) efficiency on this problem's solution. Following this direction, our aim is to optimize nuclear fuel reload using Ant-Q, a reinforcement learning algorithm based on the Cellular Computing paradigm. Ant-Q's results on the Travelling Salesmen Problem, which is conceptually similar to fuel reload, are better than the GA's ones. Ant-Q was tested on fuel reload by the simulation of the first cycle in-out reload of Bibils, a 193 fuel element PWR. Comparing An-Q's result with the GA's ones, it can b seen that even without a local heuristics, the former evolutionary algorithm can be used to solve the nuclear fuel reload problem. (author)

An evolutionary algorithm for model selection

Energy Technology Data Exchange (ETDEWEB)

Bicker, Karl [CERN, Geneva (Switzerland); Chung, Suh-Urk; Friedrich, Jan; Grube, Boris; Haas, Florian; Ketzer, Bernhard; Neubert, Sebastian; Paul, Stephan; Ryabchikov, Dimitry [Technische Univ. Muenchen (Germany)

2013-07-01

When performing partial-wave analyses of multi-body final states, the choice of the fit model, i.e. the set of waves to be used in the fit, can significantly alter the results of the partial wave fit. Traditionally, the models were chosen based on physical arguments and by observing the changes in log-likelihood of the fits. To reduce possible bias in the model selection process, an evolutionary algorithm was developed based on a Bayesian goodness-of-fit criterion which takes into account the model complexity. Starting from systematically constructed pools of waves which contain significantly more waves than the typical fit model, the algorithm yields a model with an optimal log-likelihood and with a number of partial waves which is appropriate for the number of events in the data. Partial waves with small contributions to the total intensity are penalized and likely to be dropped during the selection process, as are models were excessive correlations between single waves occur. Due to the automated nature of the model selection, a much larger part of the model space can be explored than would be possible in a manual selection. In addition the method allows to assess the dependence of the fit result on the fit model which is an important contribution to the systematic uncertainty.

Evolutionary Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory

Institute of Scientific and Technical Information of China (English)

Sheng-Zhi Du; Zeng-Qiang Chen; Zhu-Zhi Yuan

2005-01-01

This paper analyzes the sensitivity to noise in BAM (Bidirectional Associative Memory), and then proves the noise immunity of BAM relates not only to the minimum absolute value of net inputs (MAV) but also to the variance of weights associated with synapse connections. In fact, it is a positive monotonically increasing function of the quotient of MAV divided by the variance of weights. Besides, the performance of pseudo-relaxation method depends on learning parameters (λ and ζ), but the relation of them is not linear. So it is hard to find a best combination of λ and ζ which leads to the best BAM performance. And it is obvious that pseudo-relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. In this paper, a novel learning algorithm EPRBAM (evolutionary psendo-relaxation learning algorithm for bidirectional association memory) employing genetic algorithm and pseudo-relaxation method is proposed to get feasible solution of BAM weight matrix. This algorithm uses the quotient as the fitness of each individual and employs pseudo-relaxation method to adjust individual solution when it does not satisfy constraining condition any more after genetic operation. Experimental results show this algorithm improves noise immunity of BAM greatly. At the same time, EPRBAM does not depend on learning parameters and can get global optimal solution.

A Gaze-Driven Evolutionary Algorithm to Study Aesthetic Evaluation of Visual Symmetry

Directory of Open Access Journals (Sweden)

Alexis D. J. Makin

2016-03-01

Full Text Available Empirical work has shown that people like visual symmetry. We used a gaze-driven evolutionary algorithm technique to answer three questions about symmetry preference. First, do people automatically evaluate symmetry without explicit instruction? Second, is perfect symmetry the best stimulus, or do people prefer a degree of imperfection? Third, does initial preference for symmetry diminish after familiarity sets in? Stimuli were generated as phenotypes from an algorithmic genotype, with genes for symmetry (coded as deviation from a symmetrical template, deviation–symmetry, DS gene and orientation (0° to 90°, orientation, ORI gene. An eye tracker identified phenotypes that were good at attracting and retaining the gaze of the observer. Resulting fitness scores determined the genotypes that passed to the next generation. We recorded changes to the distribution of DS and ORI genes over 20 generations. When participants looked for symmetry, there was an increase in high-symmetry genes. When participants looked for the patterns they preferred, there was a smaller increase in symmetry, indicating that people tolerated some imperfection. Conversely, there was no increase in symmetry during free viewing, and no effect of familiarity or orientation. This work demonstrates the viability of the evolutionary algorithm approach as a quantitative measure of aesthetic preference.

NOAA Weather Radio - EAS Description

Science.gov (United States)

Non-Zero All Hazards Logo Emergency Alert Description Event Codes Fact Sheet FAQ Organization Search Search For Go NWS All NOAA Emergency Alert System (EAS) List of EAS Event Codes NWS EAS fact sheet What Management Agency (FEMA) and the NWS, implements the EAS at the federal level. The EAS is the nation's public

A new evolutionary algorithm with LQV learning for combinatorial problems optimization

International Nuclear Information System (INIS)

Machado, Marcelo Dornellas; Schirru, Roberto

2000-01-01

Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for combinatorial problems optimization. In this work, a new learning mode, to be used by the population-based incremental learning algorithm, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process known as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors, in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problems. Due to the fact that the reload problem is a combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)

Improved multilayer OLED architecture using evolutionary genetic algorithm

International Nuclear Information System (INIS)

Quirino, W.G.; Teixeira, K.C.; Legnani, C.; Calil, V.L.; Messer, B.; Neto, O.P. Vilela; Pacheco, M.A.C.; Cremona, M.

2009-01-01

Organic light-emitting diodes (OLEDs) constitute a new class of emissive devices, which present high efficiency and low voltage operation, among other advantages over current technology. Multilayer architecture (M-OLED) is generally used to optimize these devices, specially overcoming the suppression of light emission due to the exciton recombination near the metal layers. However, improvement in recombination, transport and charge injection can also be achieved by blending electron and hole transporting layers into the same one. Graded emissive region devices can provide promising results regarding quantum and power efficiency and brightness, as well. The massive number of possible model configurations, however, suggests that a search algorithm would be more suitable for this matter. In this work, multilayer OLEDs were simulated and fabricated using Genetic Algorithms (GAs) as evolutionary strategy to improve their efficiency. Genetic Algorithms are stochastic algorithms based on genetic inheritance and Darwinian strife to survival. In our simulations, it was assumed a 50 nm width graded region, divided into five equally sized layers. The relative concentrations of the materials within each layer were optimized to obtain the lower V/J 0.5 ratio, where V is the applied voltage and J the current density. The best M-OLED architecture obtained by genetic algorithm presented a V/J 0.5 ratio nearly 7% lower than the value reported in the literature. In order to check the experimental validity of the improved results obtained in the simulations, two M-OLEDs with different architectures were fabricated by thermal deposition in high vacuum environment. The results of the comparison between simulation and some experiments are presented and discussed.

Evolutionary design assistants for architecture

Directory of Open Access Journals (Sweden)

N. Onur Sönmez

2015-04-01

existing literature and the proposals and applications of the thesis; secondly, proposals for descriptive and prescriptive models, mappings, summary illustrations, task structures, decomposition schemes, and integratory frameworks; and finally, experimental applications of these proposals. This tripartite progression allows an evaluation of each proposal both conceptually and practically; thereby, enabling a progressive improvement of the understanding regarding the research question, while producing concrete outputs on the way. Besides theoretical and interpretative examinations, the thesis investigates its subject through a set of practical and speculative proposals, which function as both research instruments and the outputs of the study. The first main output of the study is the “design_proxy” approach (d_p, which is an integrated approach for draft making design assistants. It is an outcome of both theoretical examinations and experimental applications, and proposes an integration of, (1 flexible and relaxed task definitions and representations (instead of strict formalisms, (2 intuitive interfaces that make use of usual design media, (3 evaluation of solution proposals through their similarity to given examples, and (4 a dynamic evolutionary approach for solution generation. The design_proxy approach may be useful for AD researchers that aim at developing practical design assistants, as has been examined and demonstrated with the two applications, i.e., design_proxy.graphics and design_proxy.layout. The second main output, the “Interleaved Evolutionary Algorithm” (IEA, or Interleaved EA is a novel evolutionary algorithm proposed and used as the underlying generative mechanism of design_proxybased design assistants. The Interleaved EA is a dynamic, adaptive, and multi-objective EA, in which one of the objectives leads the evolution until its fitness progression stagnates; in the sense that the settings and fitness values of this objective is used for most

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

Directory of Open Access Journals (Sweden)

Afnizanfaizal Abdullah

Full Text Available The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

Science.gov (United States)

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

Preventive maintenance scheduling by variable dimension evolutionary algorithms

International Nuclear Information System (INIS)

Limbourg, Philipp; Kochs, Hans-Dieter

2006-01-01

Black box optimization strategies have been proven to be useful tools for solving complex maintenance optimization problems. There has been a considerable amount of research on the right choice of optimization strategies for finding optimal preventive maintenance schedules. Much less attention is turned to the representation of the schedule to the algorithm. Either the search space is represented as a binary string leading to highly complex combinatorial problem or maintenance operations are defined by regular intervals which may restrict the search space to suboptimal solutions. An adequate representation however is vitally important for result quality. This work presents several nonstandard input representations and compares them to the standard binary representation. An evolutionary algorithm with extensions to handle variable length genomes is used for the comparison. The results demonstrate that two new representations perform better than the binary representation scheme. A second analysis shows that the performance may be even more increased using modified genetic operators. Thus, the choice of alternative representations leads to better results in the same amount of time and without any loss of accuracy

Post-Starburst Galaxies At The End of The E+A Phase

Science.gov (United States)

Liu, Charles; Marinelli, Mariarosa; Chang, Madeleine; Lyczko, Camilla; Vega Orozco, Cecilia; SDSS-IV Collaboration

2018-06-01

Post-starburst galaxies, once thought to be rare curiosities, are now recognized to represent a key phase in the galaxy evolution. The post-starburst, or E+A phase, should however not be considered as a single, short-lived phenomenon; rather, it is an extended evolutionary process that occurs a galaxy transitions from an actively star-forming system into a quiescent one. We present a study of nearby galaxies at or near the end of the E+A phase, wherein all star formation has been quenched, the fossilized stellar population of the most recent starburst is highly localized, and the remainder of the galaxy's stellar population is old and quiescent. The luminosity and stellar age distribution of these "end-phase E+As" can provide insights into the evolution of galaxies onto and within the red sequence, from active to passive systems. This work is supported by National Science Foundation grants to CUNY College of Staten Island and the American Museum of Natural History; the College of Staten Island Office of Academic Affairs; the Sherman Fairchild Science Pathways Scholars Program (SP^2) at Barnard College; and the Alfred P. Sloan Foundation.

A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling

Directory of Open Access Journals (Sweden)

Qianwang Deng

2017-01-01

Full Text Available Flexible job-shop scheduling problem (FJSP is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II for multiobjective FJSP (MO-FJSP with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N, in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling.

Science.gov (United States)

Deng, Qianwang; Gong, Guiliang; Gong, Xuran; Zhang, Like; Liu, Wei; Ren, Qinghua

2017-01-01

Flexible job-shop scheduling problem (FJSP) is an NP-hard puzzle which inherits the job-shop scheduling problem (JSP) characteristics. This paper presents a bee evolutionary guiding nondominated sorting genetic algorithm II (BEG-NSGA-II) for multiobjective FJSP (MO-FJSP) with the objectives to minimize the maximal completion time, the workload of the most loaded machine, and the total workload of all machines. It adopts a two-stage optimization mechanism during the optimizing process. In the first stage, the NSGA-II algorithm with T iteration times is first used to obtain the initial population N , in which a bee evolutionary guiding scheme is presented to exploit the solution space extensively. In the second stage, the NSGA-II algorithm with GEN iteration times is used again to obtain the Pareto-optimal solutions. In order to enhance the searching ability and avoid the premature convergence, an updating mechanism is employed in this stage. More specifically, its population consists of three parts, and each of them changes with the iteration times. What is more, numerical simulations are carried out which are based on some published benchmark instances. Finally, the effectiveness of the proposed BEG-NSGA-II algorithm is shown by comparing the experimental results and the results of some well-known algorithms already existed.

Comparison of absolute intensity between EAS with gamma families and general EAS at Mount Norikura

International Nuclear Information System (INIS)

Mitsumune, T.; Nakatsuka, T.; Nishikawa, K.

1985-01-01

Gamma families with total energy greater than 10 TeV, found in the EX chamber which was cooperated with the EAS array, were combined with EAS triggered by big bursts. The absolute intensity of the size spectrum of these combined EAS was compared with that of general EAS obtained by AS trigger. The EAS with sizes greater than 2x1 million were always accompanied by gamma families with sigma E sub gamma H 10 TeV, n sub gamma, H 2 and Emin=3 TeV, although the rate of EAS accompaning such gamma families decreases rapidly as their sizes decrease

Part E: Evolutionary Computation

DEFF Research Database (Denmark)

2015-01-01

of Computational Intelligence. First, comprehensive surveys of genetic algorithms, genetic programming, evolution strategies, parallel evolutionary algorithms are presented, which are readable and constructive so that a large audience might find them useful and – to some extent – ready to use. Some more general...... kinds of evolutionary algorithms, have been prudently analyzed. This analysis was followed by a thorough analysis of various issues involved in stochastic local search algorithms. An interesting survey of various technological and industrial applications in mechanical engineering and design has been...... topics like the estimation of distribution algorithms, indicator-based selection, etc., are also discussed. An important problem, from a theoretical and practical point of view, of learning classifier systems is presented in depth. Multiobjective evolutionary algorithms, which constitute one of the most...

δ-Similar Elimination to Enhance Search Performance of Multiobjective Evolutionary Algorithms

Science.gov (United States)

Aguirre, Hernán; Sato, Masahiko; Tanaka, Kiyoshi

In this paper, we propose δ-similar elimination to improve the search performance of multiobjective evolutionary algorithms in combinatorial optimization problems. This method eliminates similar individuals in objective space to fairly distribute selection among the different regions of the instantaneous Pareto front. We investigate four eliminating methods analyzing their effects using NSGA-II. In addition, we compare the search performance of NSGA-II enhanced by our method and NSGA-II enhanced by controlled elitism.

THE APPLICATION OF AN EVOLUTIONARY ALGORITHM TO THE OPTIMIZATION OF A MESOSCALE METEOROLOGICAL MODEL

Energy Technology Data Exchange (ETDEWEB)

Werth, D.; O' Steen, L.

2008-02-11

We show that a simple evolutionary algorithm can optimize a set of mesoscale atmospheric model parameters with respect to agreement between the mesoscale simulation and a limited set of synthetic observations. This is illustrated using the Regional Atmospheric Modeling System (RAMS). A set of 23 RAMS parameters is optimized by minimizing a cost function based on the root mean square (rms) error between the RAMS simulation and synthetic data (observations derived from a separate RAMS simulation). We find that the optimization can be efficient with relatively modest computer resources, thus operational implementation is possible. The optimization efficiency, however, is found to depend strongly on the procedure used to perturb the 'child' parameters relative to their 'parents' within the evolutionary algorithm. In addition, the meteorological variables included in the rms error and their weighting are found to be an important factor with respect to finding the global optimum.

Irrigation water allocation optimization using multi-objective evolutionary algorithm (MOEA) - a review

Science.gov (United States)

Fanuel, Ibrahim Mwita; Mushi, Allen; Kajunguri, Damian

2018-03-01

This paper analyzes more than 40 papers with a restricted area of application of Multi-Objective Genetic Algorithm, Non-Dominated Sorting Genetic Algorithm-II and Multi-Objective Differential Evolution (MODE) to solve the multi-objective problem in agricultural water management. The paper focused on different application aspects which include water allocation, irrigation planning, crop pattern and allocation of available land. The performance and results of these techniques are discussed. The review finds that there is a potential to use MODE to analyzed the multi-objective problem, the application is more significance due to its advantage of being simple and powerful technique than any Evolutionary Algorithm. The paper concludes with the hopeful new trend of research that demand effective use of MODE; inclusion of benefits derived from farm byproducts and production costs into the model.

A standard deviation selection in evolutionary algorithm for grouper fish feed formulation

Science.gov (United States)

Cai-Juan, Soong; Ramli, Razamin; Rahman, Rosshairy Abdul

2016-10-01

Malaysia is one of the major producer countries for fishery production due to its location in the equatorial environment. Grouper fish is one of the potential markets in contributing to the income of the country due to its desirable taste, high demand and high price. However, the demand of grouper fish is still insufficient from the wild catch. Therefore, there is a need to farm grouper fish to cater to the market demand. In order to farm grouper fish, there is a need to have prior knowledge of the proper nutrients needed because there is no exact data available. Therefore, in this study, primary data and secondary data are collected even though there is a limitation of related papers and 30 samples are investigated by using standard deviation selection in Evolutionary algorithm. Thus, this study would unlock frontiers for an extensive research in respect of grouper fish feed formulation. Results shown that the fitness of standard deviation selection in evolutionary algorithm is applicable. The feasible and low fitness, quick solution can be obtained. These fitness can be further predicted to minimize cost in farming grouper fish.

Minimizing the symbol-error-rate for amplify-and-forward relaying systems using evolutionary algorithms

KAUST Repository

Ahmed, Qasim Zeeshan

2015-02-01

In this paper, a new detector is proposed for an amplify-and-forward (AF) relaying system. The detector is designed to minimize the symbol-error-rate (SER) of the system. The SER surface is non-linear and may have multiple minimas, therefore, designing an SER detector for cooperative communications becomes an optimization problem. Evolutionary based algorithms have the capability to find the global minima, therefore, evolutionary algorithms such as particle swarm optimization (PSO) and differential evolution (DE) are exploited to solve this optimization problem. The performance of proposed detectors is compared with the conventional detectors such as maximum likelihood (ML) and minimum mean square error (MMSE) detector. In the simulation results, it can be observed that the SER performance of the proposed detectors is less than 2 dB away from the ML detector. Significant improvement in SER performance is also observed when comparing with the MMSE detector. The computational complexity of the proposed detector is much less than the ML and MMSE algorithms. Moreover, in contrast to ML and MMSE detectors, the computational complexity of the proposed detectors increases linearly with respect to the number of relays.

Evaluating and Improving Automatic Sleep Spindle Detection by Using Multi-Objective Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Min-Yin Liu

2017-05-01

Full Text Available Sleep spindles are brief bursts of brain activity in the sigma frequency range (11–16 Hz measured by electroencephalography (EEG mostly during non-rapid eye movement (NREM stage 2 sleep. These oscillations are of great biological and clinical interests because they potentially play an important role in identifying and characterizing the processes of various neurological disorders. Conventionally, sleep spindles are identified by expert sleep clinicians via visual inspection of EEG signals. The process is laborious and the results are inconsistent among different experts. To resolve the problem, numerous computerized methods have been developed to automate the process of sleep spindle identification. Still, the performance of these automated sleep spindle detection methods varies inconsistently from study to study. There are two reasons: (1 the lack of common benchmark databases, and (2 the lack of commonly accepted evaluation metrics. In this study, we focus on tackling the second problem by proposing to evaluate the performance of a spindle detector in a multi-objective optimization context and hypothesize that using the resultant Pareto fronts for deriving evaluation metrics will improve automatic sleep spindle detection. We use a popular multi-objective evolutionary algorithm (MOEA, the Strength Pareto Evolutionary Algorithm (SPEA2, to optimize six existing frequency-based sleep spindle detection algorithms. They include three Fourier, one continuous wavelet transform (CWT, and two Hilbert-Huang transform (HHT based algorithms. We also explore three hybrid approaches. Trained and tested on open-access DREAMS and MASS databases, two new hybrid methods of combining Fourier with HHT algorithms show significant performance improvement with F1-scores of 0.726–0.737.

A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks

Directory of Open Access Journals (Sweden)

Guoqiang Chen

2013-01-01

Full Text Available Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.

Incorporation of an evolutionary algorithm to estimate transfer-functions for a parameter regionalization scheme of a rainfall-runoff model

Science.gov (United States)

Klotz, Daniel; Herrnegger, Mathew; Schulz, Karsten

2016-04-01

This contribution presents a framework, which enables the use of an Evolutionary Algorithm (EA) for the calibration and regionalization of the hydrological model COSEROreg. COSEROreg uses an updated version of the HBV-type model COSERO (Kling et al. 2014) for the modelling of hydrological processes and is embedded in a parameter regionalization scheme based on Samaniego et al. (2010). The latter uses subscale-information to estimate model via a-priori chosen transfer functions (often derived from pedotransfer functions). However, the transferability of the regionalization scheme to different model-concepts and the integration of new forms of subscale information is not straightforward. (i) The usefulness of (new) single sub-scale information layers is unknown beforehand. (ii) Additionally, the establishment of functional relationships between these (possibly meaningless) sub-scale information layers and the distributed model parameters remain a central challenge in the implementation of a regionalization procedure. The proposed method theoretically provides a framework to overcome this challenge. The implementation of the EA encompasses the following procedure: First, a formal grammar is specified (Ryan et al., 1998). The construction of the grammar thereby defines the set of possible transfer functions and also allows to incorporate hydrological domain knowledge into the search itself. The EA iterates over the given space by combining parameterized basic functions (e.g. linear- or exponential functions) and sub-scale information layers into transfer functions, which are then used in COSEROreg. However, a pre-selection model is applied beforehand to sort out unfeasible proposals by the EA and to reduce the necessary model runs. A second optimization routine is used to optimize the parameters of the transfer functions proposed by the EA. This concept, namely using two nested optimization loops, is inspired by the idea of Lamarckian Evolution and Baldwin Effect

Nuclear fuel management optimization using adaptive evolutionary algorithms with heuristics

International Nuclear Information System (INIS)

Axmann, J.K.; Van de Velde, A.

1996-01-01

Adaptive Evolutionary Algorithms in combination with expert knowledge encoded in heuristics have proved to be a robust and powerful optimization method for the design of optimized PWR fuel loading pattern. Simple parallel algorithmic structures coupled with a low amount of communications between computer processor units in use makes it possible for workstation clusters to be employed efficiently. The extension of classic evolution strategies not only by new and alternative methods but also by the inclusion of heuristics with effects on the exchange probabilities of the fuel assemblies at specific core positions leads to the RELOPAT optimization code of the Technical University of Braunschweig. In combination with the new, neutron-physical 3D nodal core simulator PRISM developed by SIEMENS the PRIMO loading pattern optimization system has been designed. Highly promising results in the recalculation of known reload plans for German PWR's new lead to a commercially usable program. (author)

An Endosymbiotic Evolutionary Algorithm for the Hub Location-Routing Problem

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Ji Ung Sun

2015-01-01

Full Text Available We consider a capacitated hub location-routing problem (HLRP which combines the hub location problem and multihub vehicle routing decisions. The HLRP not only determines the locations of the capacitated p-hubs within a set of potential hubs but also deals with the routes of the vehicles to meet the demands of customers. This problem is formulated as a 0-1 mixed integer programming model with the objective of the minimum total cost including routing cost, fixed hub cost, and fixed vehicle cost. As the HLRP has impractically demanding for the large sized problems, we develop a solution method based on the endosymbiotic evolutionary algorithm (EEA which solves hub location and vehicle routing problem simultaneously. The performance of the proposed algorithm is examined through a comparative study. The experimental results show that the proposed EEA can be a viable solution method for the supply chain network planning.

Safety management in NPPs using evolutionary algorithm

International Nuclear Information System (INIS)

Mishra, A.; Patwardhan, A.; Chauhan, A.; Verma, A.K.

2005-01-01

Technical specification and maintenance (TS and M) activities in a plant are associated with controlling risk or with satisfying requirements, and are candidates to be evaluated for their resource effectiveness in risk-informed applications. The general goal of safety management in Nuclear Power Plants (NPPs) is to make requirements and activities more risk effective and less costly. Accordingly, the risk-based analysis of Technical Specification (RBTS) is being considered in evaluating current TS. The multi objective optimization of the TS and M requirements of a NPP based on risk and cost, gives the pareto-optimal solutions, from which the utility can pick its decision variables suiting its interest. In this paper a multi objective Evolutionary Algorithm technique has been used to make a trade-off between risk and cost both at the system level and at the plant level for Loss of coolant Accident (LOCA) and Main Steam Line Break (MSLB) as initiating events. (authors)

The wind power prediction research based on mind evolutionary algorithm

Science.gov (United States)

Zhuang, Ling; Zhao, Xinjian; Ji, Tianming; Miao, Jingwen; Cui, Haina

2018-04-01

When the wind power is connected to the power grid, its characteristics of fluctuation, intermittent and randomness will affect the stability of the power system. The wind power prediction can guarantee the power quality and reduce the operating cost of power system. There were some limitations in several traditional wind power prediction methods. On the basis, the wind power prediction method based on Mind Evolutionary Algorithm (MEA) is put forward and a prediction model is provided. The experimental results demonstrate that MEA performs efficiently in term of the wind power prediction. The MEA method has broad prospect of engineering application.

Culture belief based multi-objective hybrid differential evolutionary algorithm in short term hydrothermal scheduling

International Nuclear Information System (INIS)

Zhang Huifeng; Zhou Jianzhong; Zhang Yongchuan; Lu Youlin; Wang Yongqiang

2013-01-01

Highlights: ► Culture belief is integrated into multi-objective differential evolution. ► Chaotic sequence is imported to improve evolutionary population diversity. ► The priority of convergence rate is proved in solving hydrothermal problem. ► The results show the quality and potential of proposed algorithm. - Abstract: A culture belief based multi-objective hybrid differential evolution (CB-MOHDE) is presented to solve short term hydrothermal optimal scheduling with economic emission (SHOSEE) problem. This problem is formulated for compromising thermal cost and emission issue while considering its complicated non-linear constraints with non-smooth and non-convex characteristics. The proposed algorithm integrates a modified multi-objective differential evolutionary algorithm into the computation model of culture algorithm (CA) as well as some communication protocols between population space and belief space, three knowledge structures in belief space are redefined according to these problem-solving characteristics, and in the differential evolution a chaotic factor is embedded into mutation operator for avoiding the premature convergence by enlarging the search scale when the search trajectory reaches local optima. Furthermore, a new heuristic constraint-handling technique is utilized to handle those complex equality and inequality constraints of SHOSEE problem. After the application on hydrothermal scheduling system, the efficiency and stability of the proposed CB-MOHDE is verified by its more desirable results in comparison to other method established recently, and the simulation results also reveal that CB-MOHDE can be a promising alternative for solving SHOSEE.

A Problem-Reduction Evolutionary Algorithm for Solving the Capacitated Vehicle Routing Problem

Directory of Open Access Journals (Sweden)

Wanfeng Liu

2015-01-01

Full Text Available Assessment of the components of a solution helps provide useful information for an optimization problem. This paper presents a new population-based problem-reduction evolutionary algorithm (PREA based on the solution components assessment. An individual solution is regarded as being constructed by basic elements, and the concept of acceptability is introduced to evaluate them. The PREA consists of a searching phase and an evaluation phase. The acceptability of basic elements is calculated in the evaluation phase and passed to the searching phase. In the searching phase, for each individual solution, the original optimization problem is reduced to a new smaller-size problem. With the evolution of the algorithm, the number of common basic elements in the population increases until all individual solutions are exactly the same which is supposed to be the near-optimal solution of the optimization problem. The new algorithm is applied to a large variety of capacitated vehicle routing problems (CVRP with customers up to nearly 500. Experimental results show that the proposed algorithm has the advantages of fast convergence and robustness in solution quality over the comparative algorithms.

EA Training 2.0 Newsletter #3 - EA Active, Problem Based Learning Methodology

DEFF Research Database (Denmark)

Buus, Lillian; Ryberg, Thomas; Sroga, Magdalena

2010-01-01

The main products of the project are innovative, active problem-based learning methodology for EA education and training, EA courses for university students and private and public sector employees, and an Enterprise Architecture competence ontology including a complete specification of skills...

Efficient fractal-based mutation in evolutionary algorithms from iterated function systems

Science.gov (United States)

Salcedo-Sanz, S.; Aybar-Ruíz, A.; Camacho-Gómez, C.; Pereira, E.

2018-03-01

In this paper we present a new mutation procedure for Evolutionary Programming (EP) approaches, based on Iterated Function Systems (IFSs). The new mutation procedure proposed consists of considering a set of IFS which are able to generate fractal structures in a two-dimensional phase space, and use them to modify a current individual of the EP algorithm, instead of using random numbers from different probability density functions. We test this new proposal in a set of benchmark functions for continuous optimization problems. In this case, we compare the proposed mutation against classical Evolutionary Programming approaches, with mutations based on Gaussian, Cauchy and chaotic maps. We also include a discussion on the IFS-based mutation in a real application of Tuned Mass Dumper (TMD) location and optimization for vibration cancellation in buildings. In both practical cases, the proposed EP with the IFS-based mutation obtained extremely competitive results compared to alternative classical mutation operators.

On the Runtime of Randomized Local Search and Simple Evolutionary Algorithms for Dynamic Makespan Scheduling

DEFF Research Database (Denmark)

Neumann, Frank; Witt, Carsten

2015-01-01

combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very...

Comparison of Multiobjective Evolutionary Algorithms for Operations Scheduling under Machine Availability Constraints

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M. Frutos

2013-01-01

Full Text Available Many of the problems that arise in production systems can be handled with multiobjective techniques. One of those problems is that of scheduling operations subject to constraints on the availability of machines and buffer capacity. In this paper we analyze different Evolutionary multiobjective Algorithms (MOEAs for this kind of problems. We consider an experimental framework in which we schedule production operations for four real world Job-Shop contexts using three algorithms, NSGAII, SPEA2, and IBEA. Using two performance indexes, Hypervolume and R2, we found that SPEA2 and IBEA are the most efficient for the tasks at hand. On the other hand IBEA seems to be a better choice of tool since it yields more solutions in the approximate Pareto frontier.

Multi-objective flexible job shop scheduling problem using variable neighborhood evolutionary algorithm

Science.gov (United States)

Wang, Chun; Ji, Zhicheng; Wang, Yan

2017-07-01

In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.

Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

International Nuclear Information System (INIS)

Vianna Neto, Julio Xavier; Andrade Bernert, Diego Luis de; Santos Coelho, Leandro dos

2011-01-01

The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature.

Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

Energy Technology Data Exchange (ETDEWEB)

Vianna Neto, Julio Xavier, E-mail: julio.neto@onda.com.b [Pontifical Catholic University of Parana, PUCPR, Undergraduate Program at Mechatronics Engineering, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Andrade Bernert, Diego Luis de, E-mail: dbernert@gmail.co [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Santos Coelho, Leandro dos, E-mail: leandro.coelho@pucpr.b [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil)

2011-01-15

The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature.

Improved quantum-inspired evolutionary algorithm with diversity information applied to economic dispatch problem with prohibited operating zones

Energy Technology Data Exchange (ETDEWEB)

Neto, Julio Xavier Vianna [Pontifical Catholic University of Parana, PUCPR, Undergraduate Program at Mechatronics Engineering, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil); Bernert, Diego Luis de Andrade; Coelho, Leandro dos Santos [Pontifical Catholic University of Parana, PUCPR, Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Imaculada Conceicao, 1155, Zip code 80215-901, Curitiba, Parana (Brazil)

2011-01-15

The objective of the economic dispatch problem (EDP) of electric power generation, whose characteristics are complex and highly nonlinear, is to schedule the committed generating unit outputs so as to meet the required load demand at minimum operating cost while satisfying all unit and system equality and inequality constraints. Recently, as an alternative to the conventional mathematical approaches, modern meta-heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution in EDPs. Research on merging evolutionary computation and quantum computation has been started since late 1990. Inspired on the quantum computation, this paper presented an improved quantum-inspired evolutionary algorithm (IQEA) based on diversity information of population. A classical quantum-inspired evolutionary algorithm (QEA) and the IQEA were implemented and validated for a benchmark of EDP with 15 thermal generators with prohibited operating zones. From the results for the benchmark problem, it is observed that the proposed IQEA approach provides promising results when compared to various methods available in the literature. (author)

A new hybrid evolutionary algorithm based on new fuzzy adaptive PSO and NM algorithms for Distribution Feeder Reconfiguration

International Nuclear Information System (INIS)

Niknam, Taher; Azadfarsani, Ehsan; Jabbari, Masoud

2012-01-01

Highlights: ► Network reconfiguration is a very important way to save the electrical energy. ► This paper proposes a new algorithm to solve the DFR. ► The algorithm combines NFAPSO with NM. ► The proposed algorithm is tested on two distribution test feeders. - Abstract: Network reconfiguration for loss reduction in distribution system is a very important way to save the electrical energy. This paper proposes a new hybrid evolutionary algorithm to solve the Distribution Feeder Reconfiguration problem (DFR). The algorithm is based on combination of a New Fuzzy Adaptive Particle Swarm Optimization (NFAPSO) and Nelder–Mead simplex search method (NM) called NFAPSO–NM. In the proposed algorithm, a new fuzzy adaptive particle swarm optimization includes two parts. The first part is Fuzzy Adaptive Binary Particle Swarm Optimization (FABPSO) that determines the status of tie switches (open or close) and second part is Fuzzy Adaptive Discrete Particle Swarm Optimization (FADPSO) that determines the sectionalizing switch number. In other side, due to the results of binary PSO(BPSO) and discrete PSO(DPSO) algorithms highly depends on the values of their parameters such as the inertia weight and learning factors, a fuzzy system is employed to adaptively adjust the parameters during the search process. Moreover, the Nelder–Mead simplex search method is combined with the NFAPSO algorithm to improve its performance. Finally, the proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization.

Creating ensembles of oblique decision trees with evolutionary algorithms and sampling

Science.gov (United States)

Cantu-Paz, Erick [Oakland, CA; Kamath, Chandrika [Tracy, CA

2006-06-13

A decision tree system that is part of a parallel object-oriented pattern recognition system, which in turn is part of an object oriented data mining system. A decision tree process includes the step of reading the data. If necessary, the data is sorted. A potential split of the data is evaluated according to some criterion. An initial split of the data is determined. The final split of the data is determined using evolutionary algorithms and statistical sampling techniques. The data is split. Multiple decision trees are combined in ensembles.

Scheduling for the National Hockey League Using a Multi-objective Evolutionary Algorithm

Science.gov (United States)

Craig, Sam; While, Lyndon; Barone, Luigi

We describe a multi-objective evolutionary algorithm that derives schedules for the National Hockey League according to three objectives: minimising the teams' total travel, promoting equity in rest time between games, and minimising long streaks of home or away games. Experiments show that the system is able to derive schedules that beat the 2008-9 NHL schedule in all objectives simultaneously, and that it returns a set of schedules that offer a range of trade-offs across the objectives.

Multi-stage thermal-economical optimization of compact heat exchangers: A new evolutionary-based design approach for real-world problems

International Nuclear Information System (INIS)

Yousefi, Moslem; Darus, Amer Nordin; Yousefi, Milad; Hooshyar, Danial

2015-01-01

The complicated task of design optimization of compact heat exchangers (CHEs) have been effectively performed by using evolutionary algorithms (EAs) in the recent years. However, mainly due to difficulties of handling extra variables, the design approach has been based on constant rates of heat duty in the available literature. In this paper, a new design strategy is presented where variable operating conditions, which better represent real-world problems, are considered. The proposed strategy is illustrated using a case study for design of a plate-fin heat exchanger though it can be employed for all types of heat exchangers without much change. Learning automata based particle swarm optimization (LAPSO), is employed for handling nine design variables while satisfying various equality and inequality constraints. For handling the constraints, a novel feasibility based ranking strategy (FBRS) is introduced. The numerical results indicate that the design based on variable heat duties yields in more cost savings and superior thermodynamics efficiency comparing to a conventional design approach. Furthermore, the proposed algorithm has shown a superior performance in finding the near-optimum solution for this task when it is compared to the most popular evolutionary algorithms in engineering applications, i.e. genetic algorithm (GA) and particle swarm optimization (PSO). - Highlights: • Multi-stage design of heat exchangers is presented. • Feasibility based ranking strategy is employed for constraint handling. • Learning abilities added to particle swarm optimization

Investigation of EAS cores

Directory of Open Access Journals (Sweden)

Shaulov S.B.

2017-01-01

Full Text Available The development of nuclear-electromagnetic cascade models in air in the late forties have shown informational content of the study of cores of extensive air showers (EAS. These investigations were the main goal in different experiments which were carried out over many years by a variety of methods. Outcomes of such investigations obtained in the HADRON experiment using an X-ray emulsion chamber (XREC as a core detector are considered. The Ne spectrum of EAS associated with γ-ray families, spectra of γ-rays (hadrons in EAS cores and the Ne dependence of the muon number, ⟨Nμ⟩, in EAS with γ-ray families are obtained for the first time at energies of 1015–1017 eV with this method. A number of new effects were observed, namely, an abnormal scaling violation in hadron spectra which are fundamentally different from model predictions, an excess of muon number in EAS associated with γ-ray families, and the penetrating component in EAS cores. It is supposed that the abnormal behavior of γ-ray spectra and Ne dependence of the muon number are explained by the emergence of a penetrating component in the 1st PCR spectrum ‘knee’ range. Nuclear and astrophysical explanations of the origin of the penetrating component are discussed. The necessity of considering the contribution of a single close cosmic-ray source to explain the PCR spectrum in the knee range is noted.

Global WASF-GA: An Evolutionary Algorithm in Multiobjective Optimization to Approximate the Whole Pareto Optimal Front.

Science.gov (United States)

Saborido, Rubén; Ruiz, Ana B; Luque, Mariano

2017-01-01

In this article, we propose a new evolutionary algorithm for multiobjective optimization called Global WASF-GA ( global weighting achievement scalarizing function genetic algorithm), which falls within the aggregation-based evolutionary algorithms. The main purpose of Global WASF-GA is to approximate the whole Pareto optimal front. Its fitness function is defined by an achievement scalarizing function (ASF) based on the Tchebychev distance, in which two reference points are considered (both utopian and nadir objective vectors) and the weight vector used is taken from a set of weight vectors whose inverses are well-distributed. At each iteration, all individuals are classified into different fronts. Each front is formed by the solutions with the lowest values of the ASF for the different weight vectors in the set, using the utopian vector and the nadir vector as reference points simultaneously. Varying the weight vector in the ASF while considering the utopian and the nadir vectors at the same time enables the algorithm to obtain a final set of nondominated solutions that approximate the whole Pareto optimal front. We compared Global WASF-GA to MOEA/D (different versions) and NSGA-II in two-, three-, and five-objective problems. The computational results obtained permit us to conclude that Global WASF-GA gets better performance, regarding the hypervolume metric and the epsilon indicator, than the other two algorithms in many cases, especially in three- and five-objective problems.

Comparing multi-objective non-evolutionary NLPQL and evolutionary genetic algorithm optimization of a DI diesel engine: DoE estimation and creating surrogate model

International Nuclear Information System (INIS)

Navid, Ali; Khalilarya, Shahram; Taghavifar, Hadi

2016-01-01

Highlights: • NLPQL algorithm with Latin hypercube and multi-objective GA were applied on engine. • NLPQL converge to the best solution at RunID41, MOGA introduces at RunID84. • Deeper, more encircled design gives the lowest NOx, greater radius and deeper bowl the highest IMEP. • The maximum IMEP and minimum ISFC obtained with NLPQL, the lowest NOx with MOGA. - Abstract: This study is concerned with the application of two major kinds of optimization algorithms on the baseline diesel engine in the class of evolutionary and non-evolutionary algorithms. The multi-objective genetic algorithm and non-linear programming by quadratic Lagrangian (NLPQL) method have completely different functions in optimizing and finding the global optimal design. The design variables are injection angle, half spray cone angle, inner distance of the bowl wall, and the bowl radius, while the objectives include NOx emission, spray droplet diameter, indicated mean effective pressure (IMEP), and indicated specific fuel consumption (ISFC). The restrictions were set on the objectives to distinguish between feasible designs and infeasible designs to sort those cases that cannot fulfill the demands of diesel engine designers and emission control measures. It is found that a design with deeper bowl and more encircled shape (higher swirl motion) is more suitable for NO_x emission control, whereas designs with a bigger bowl radius, and closer inner wall distance of the bowl (Di) may lead to higher engine efficiency indices. Moreover, it was revealed that the NLPQL could rapidly search for the best design at Run ID 41 compared to genetic algorithm, which is able to find the global optima at last runs (ID 84). Both techniques introduce almost the same geometrical shape of the combustion chamber with a negligible contrast in the injection system.

The Research of Disease Spots Extraction Based on Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Kangshun Li

2017-01-01

Full Text Available According to the characteristics of maize disease spot performance in the image, this paper designs two-histogram segmentation method based on evolutionary algorithm, which combined with the analysis of image of maize diseases and insect pests, with full consideration of color and texture characteristic of the lesion of pests and diseases, the chroma and gray image, composed of two tuples to build a two-dimensional histogram, solves the problem of one-dimensional histograms that cannot be clearly divided into target and background bimodal distribution and improved the traditional two-dimensional histogram application in pest damage lesion extraction. The chromosome coding suitable for the characteristics of lesion image is designed based on second segmentation of the genetic algorithm Otsu. Determining initial population with analysis results of lesion image, parallel selection, optimal preservation strategy, and adaptive mutation operator are used to improve the search efficiency. Finally, by setting the fluctuation threshold, we continue to search for the best threshold in the range of fluctuations for implementation of global search and local search.

Regular Network Class Features Enhancement Using an Evolutionary Synthesis Algorithm

Directory of Open Access Journals (Sweden)

O. G. Monahov

2014-01-01

Full Text Available This paper investigates a solution of the optimization problem concerning the construction of diameter-optimal regular networks (graphs. Regular networks are of practical interest as the graph-theoretical models of reliable communication networks of parallel supercomputer systems, as a basis of the structure in a model of small world in optical and neural networks. It presents a new class of parametrically described regular networks - hypercirculant networks (graphs. An approach that uses evolutionary algorithms for the automatic generation of parametric descriptions of optimal hypercirculant networks is developed. Synthesis of optimal hypercirculant networks is based on the optimal circulant networks with smaller degree of nodes. To construct optimal hypercirculant networks is used a template of circulant network from the known optimal families of circulant networks with desired number of nodes and with smaller degree of nodes. Thus, a generating set of the circulant network is used as a generating subset of the hypercirculant network, and the missing generators are synthesized by means of the evolutionary algorithm, which is carrying out minimization of diameter (average diameter of networks. A comparative analysis of the structural characteristics of hypercirculant, toroidal, and circulant networks is conducted. The advantage hypercirculant networks under such structural characteristics, as diameter, average diameter, and the width of bisection, with comparable costs of the number of nodes and the number of connections is demonstrated. It should be noted the advantage of hypercirculant networks of dimension three over four higher-dimensional tori. Thus, the optimization of hypercirculant networks of dimension three is more efficient than the introduction of an additional dimension for the corresponding toroidal structures. The paper also notes the best structural parameters of hypercirculant networks in comparison with iBT-networks previously

Multi-objective exergy-based optimization of a polygeneration energy system using an evolutionary algorithm

International Nuclear Information System (INIS)

Ahmadi, Pouria; Rosen, Marc A.; Dincer, Ibrahim

2012-01-01

A comprehensive thermodynamic modeling and optimization is reported of a polygeneration energy system for the simultaneous production of heating, cooling, electricity and hot water from a common energy source. This polygeneration system is composed of four major parts: gas turbine (GT) cycle, Rankine cycle, absorption cooling cycle and domestic hot water heater. A multi-objective optimization method based on an evolutionary algorithm is applied to determine the best design parameters for the system. The two objective functions utilized in the analysis are the total cost rate of the system, which is the cost associated with fuel, component purchasing and environmental impact, and the system exergy efficiency. The total cost rate of the system is minimized while the cycle exergy efficiency is maximized by using an evolutionary algorithm. To provide a deeper insight, the Pareto frontier is shown for multi-objective optimization. In addition, a closed form equation for the relationship between exergy efficiency and total cost rate is derived. Finally, a sensitivity analysis is performed to assess the effects of several design parameters on the system total exergy destruction rate, CO 2 emission and exergy efficiency.

A new stellar spectrum interpolation algorithm and its application to Yunnan-III evolutionary population synthesis models

Science.gov (United States)

Cheng, Liantao; Zhang, Fenghui; Kang, Xiaoyu; Wang, Lang

2018-05-01

In evolutionary population synthesis (EPS) models, we need to convert stellar evolutionary parameters into spectra via interpolation in a stellar spectral library. For theoretical stellar spectral libraries, the spectrum grid is homogeneous on the effective-temperature and gravity plane for a given metallicity. It is relatively easy to derive stellar spectra. For empirical stellar spectral libraries, stellar parameters are irregularly distributed and the interpolation algorithm is relatively complicated. In those EPS models that use empirical stellar spectral libraries, different algorithms are used and the codes are often not released. Moreover, these algorithms are often complicated. In this work, based on a radial basis function (RBF) network, we present a new spectrum interpolation algorithm and its code. Compared with the other interpolation algorithms that are used in EPS models, it can be easily understood and is highly efficient in terms of computation. The code is written in MATLAB scripts and can be used on any computer system. Using it, we can obtain the interpolated spectra from a library or a combination of libraries. We apply this algorithm to several stellar spectral libraries (such as MILES, ELODIE-3.1 and STELIB-3.2) and give the integrated spectral energy distributions (ISEDs) of stellar populations (with ages from 1 Myr to 14 Gyr) by combining them with Yunnan-III isochrones. Our results show that the differences caused by the adoption of different EPS model components are less than 0.2 dex. All data about the stellar population ISEDs in this work and the RBF spectrum interpolation code can be obtained by request from the first author or downloaded from http://www1.ynao.ac.cn/˜zhangfh.

Practical advantages of evolutionary computation

Science.gov (United States)

Fogel, David B.

1997-10-01

Evolutionary computation is becoming a common technique for solving difficult, real-world problems in industry, medicine, and defense. This paper reviews some of the practical advantages to using evolutionary algorithms as compared with classic methods of optimization or artificial intelligence. Specific advantages include the flexibility of the procedures, as well as their ability to self-adapt the search for optimum solutions on the fly. As desktop computers increase in speed, the application of evolutionary algorithms will become routine.

Exploiting Genomic Knowledge in Optimising Molecular Breeding Programmes: Algorithms from Evolutionary Computing

Science.gov (United States)

O'Hagan, Steve; Knowles, Joshua; Kell, Douglas B.

2012-01-01

Comparatively few studies have addressed directly the question of quantifying the benefits to be had from using molecular genetic markers in experimental breeding programmes (e.g. for improved crops and livestock), nor the question of which organisms should be mated with each other to best effect. We argue that this requires in silico modelling, an approach for which there is a large literature in the field of evolutionary computation (EC), but which has not really been applied in this way to experimental breeding programmes. EC seeks to optimise measurable outcomes (phenotypic fitnesses) by optimising in silico the mutation, recombination and selection regimes that are used. We review some of the approaches from EC, and compare experimentally, using a biologically relevant in silico landscape, some algorithms that have knowledge of where they are in the (genotypic) search space (G-algorithms) with some (albeit well-tuned ones) that do not (F-algorithms). For the present kinds of landscapes, F- and G-algorithms were broadly comparable in quality and effectiveness, although we recognise that the G-algorithms were not equipped with any ‘prior knowledge’ of epistatic pathway interactions. This use of algorithms based on machine learning has important implications for the optimisation of experimental breeding programmes in the post-genomic era when we shall potentially have access to the full genome sequence of every organism in a breeding population. The non-proprietary code that we have used is made freely available (via Supplementary information). PMID:23185279

Geomagnetic Navigation of Autonomous Underwater Vehicle Based on Multi-objective Evolutionary Algorithm.

Science.gov (United States)

Li, Hong; Liu, Mingyong; Zhang, Feihu

2017-01-01

This paper presents a multi-objective evolutionary algorithm of bio-inspired geomagnetic navigation for Autonomous Underwater Vehicle (AUV). Inspired by the biological navigation behavior, the solution was proposed without using a priori information, simply by magnetotaxis searching. However, the existence of the geomagnetic anomalies has significant influence on the geomagnetic navigation system, which often disrupts the distribution of the geomagnetic field. An extreme value region may easily appear in abnormal regions, which makes AUV lost in the navigation phase. This paper proposes an improved bio-inspired algorithm with behavior constraints, for sake of making AUV escape from the abnormal region. First, the navigation problem is considered as the optimization problem. Second, the environmental monitoring operator is introduced, to determine whether the algorithm falls into the geomagnetic anomaly region. Then, the behavior constraint operator is employed to get out of the abnormal region. Finally, the termination condition is triggered. Compared to the state-of- the-art, the proposed approach effectively overcomes the disturbance of the geomagnetic abnormal. The simulation result demonstrates the reliability and feasibility of the proposed approach in complex environments.

Analysis of Parametric Optimization of Field-Oriented Control of 3-Phase Induction Motor with Using Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Wiktor HUDY

2013-12-01

Full Text Available In this paper, the impact of regulators set and their types for the characteristic of rotational speed of induction motor was researched.. The evolutionary algorithm was used as optimization tool. Results were verified with using MATLAB/Simulink.

Evolutionary Cellular Automata for Image Segmentation and Noise Filtering Using Genetic Algorithms

Directory of Open Access Journals (Sweden)

Sihem SLATNIA

2011-01-01

Full Text Available We use an evolutionary process to seek a specialized set of rules among a wide range of rules to be used by Cellular Automata (CA for a range of tasks,extracting edges in a given gray or colour image, noise filtering applied to black-white image. This is the best set of local rules determine the future state of CA in an asynchronous way. The Genetic Algorithm (GA is applied to search the best CA rules that can realize the best edge detection and noise filtering.

Evolutionary Cellular Automata for Image Segmentation and Noise Filtering Using Genetic Algorithms

Directory of Open Access Journals (Sweden)

Okba Kazar

2011-01-01

Full Text Available We use an evolutionary process to seek a specialized set of rules among a wide range of rules to be used by Cellular Automata (CA for a range of tasks, extracting edges in a given gray or colour image, noise filtering applied to black-white image. This is the best set of local rules determine the future state of CA in an asynchronous way. The Genetic Algorithm (GA is applied to search the best CA rules that can realize the best edge detection and noise filtering.

Evolutionary algorithm for vehicle driving cycle generation.

Science.gov (United States)

Perhinschi, Mario G; Marlowe, Christopher; Tamayo, Sergio; Tu, Jun; Wayne, W Scott

2011-09-01

Modeling transit bus emissions and fuel economy requires a large amount of experimental data over wide ranges of operational conditions. Chassis dynamometer tests are typically performed using representative driving cycles defined based on vehicle instantaneous speed as sequences of "microtrips", which are intervals between consecutive vehicle stops. Overall significant parameters of the driving cycle, such as average speed, stops per mile, kinetic intensity, and others, are used as independent variables in the modeling process. Performing tests at all the necessary combinations of parameters is expensive and time consuming. In this paper, a methodology is proposed for building driving cycles at prescribed independent variable values using experimental data through the concatenation of "microtrips" isolated from a limited number of standard chassis dynamometer test cycles. The selection of the adequate "microtrips" is achieved through a customized evolutionary algorithm. The genetic representation uses microtrip definitions as genes. Specific mutation, crossover, and karyotype alteration operators have been defined. The Roulette-Wheel selection technique with elitist strategy drives the optimization process, which consists of minimizing the errors to desired overall cycle parameters. This utility is part of the Integrated Bus Information System developed at West Virginia University.

Optimization of externalities using DTM measures: a Pareto optimal multi objective optimization using the evolutionary algorithm SPEA2+

NARCIS (Netherlands)

Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, Michiel; Allkim, T.P.; van Arem, Bart

2010-01-01

Multi objective optimization of externalities of traffic is performed solving a network design problem in which Dynamic Traffic Management measures are used. The resulting Pareto optimal set is determined by employing the SPEA2+ evolutionary algorithm.

An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts.

Science.gov (United States)

Jiang, Shouyong; Yang, Shengxiang

2016-02-01

The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.

Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms

International Nuclear Information System (INIS)

Piltan, Mehdi; Shiri, Hiva; Ghaderi, S.F.

2012-01-01

Highlights: ► Investigating different fitness functions for evolutionary algorithms in energy forecasting. ► Energy forecasting of Iranian metal industry by value added, energy prices, investment and employees. ► Using real-coded instead of binary-coded genetic algorithm decreases energy forecasting error. - Abstract: Developing energy-forecasting models is known as one of the most important steps in long-term planning. In order to achieve sustainable energy supply toward economic development and social welfare, it is required to apply precise forecasting model. Applying artificial intelligent models for estimation complex economic and social functions is growing up considerably in many researches recently. In this paper, energy consumption in industrial sector as one of the critical sectors in the consumption of energy has been investigated. Two linear and three nonlinear functions have been used in order to forecast and analyze energy in the Iranian metal industry, Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) are applied to attain parameters of the models. The Real-Coded Genetic Algorithm (RCGA) has been developed based on real numbers, which is introduced as a new approach in the field of energy forecasting. In the proposed model, electricity consumption has been considered as a function of different variables such as electricity tariff, manufacturing value added, prevailing fuel prices, the number of employees, the investment in equipment and consumption in the previous years. Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD) and Mean Absolute Percent Error (MAPE) are the four functions which have been used as the fitness function in the evolutionary algorithms. The results show that the logarithmic nonlinear model using PSO algorithm with 1.91 error percentage has the best answer. Furthermore, the prediction of electricity consumption in industrial sector of Turkey and also Turkish industrial sector

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

Science.gov (United States)

Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

47 CFR 11.31 - EAS protocol.

Science.gov (United States)

2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false EAS protocol. 11.31 Section 11.31... characters. FM or TV call signs must use a slash ASCII character number 47 (/) in lieu of a dash. (c) The EAS... the State EAS Mapbook. FIPS# State: AL 01 AK 02 AZ 04 AR 05 CA 06 CO 08 CT 09 DE 10 DC 11 FL 12 GA 13...

System optimization for HVAC energy management using the robust evolutionary algorithm

International Nuclear Information System (INIS)

Fong, K.F.; Hanby, V.I.; Chow, T.T.

2009-01-01

For an installed centralized heating, ventilating and air conditioning (HVAC) system, appropriate energy management measures would achieve energy conservation targets through the optimal control and operation. The performance optimization of conventional HVAC systems may be handled by operation experience, but it may not cover different optimization scenarios and parameters in response to a variety of load and weather conditions. In this regard, it is common to apply the suitable simulation-optimization technique to model the system then determine the required operation parameters. The particular plant simulation models can be built up by either using the available simulation programs or a system of mathematical expressions. To handle the simulation models, iterations would be involved in the numerical solution methods. Since the gradient information is not easily available due to the complex nature of equations, the traditional gradient-based optimization methods are not applicable for this kind of system models. For the heuristic optimization methods, the continual search is commonly necessary, and the system function call is required for each search. The frequency of simulation function calls would then be a time-determining step, and an efficient optimization method is crucial, in order to find the solution through a number of function calls in a reasonable computational period. In this paper, the robust evolutionary algorithm (REA) is presented to tackle this nature of the HVAC simulation models. REA is based on one of the paradigms of evolutionary algorithm, evolution strategy, which is a stochastic population-based searching technique emphasized on mutation. The REA, which incorporates the Cauchy deterministic mutation, tournament selection and arithmetic recombination, would provide a synergetic effect for optimal search. The REA is effective to cope with the complex simulation models, as well as those represented by explicit mathematical expressions of

Spatial multiobjective optimization of agricultural conservation practices using a SWAT model and an evolutionary algorithm.

Science.gov (United States)

Rabotyagov, Sergey; Campbell, Todd; Valcu, Adriana; Gassman, Philip; Jha, Manoj; Schilling, Keith; Wolter, Calvin; Kling, Catherine

2012-12-09

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,(5,12,20)) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods(3,4,9,10,13-15,17-19,22,23,25). In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model(7) with a

Optimal routes scheduling for municipal waste disposal garbage trucks using evolutionary algorithm and artificial immune system

Directory of Open Access Journals (Sweden)

Bogna MRÓWCZYŃSKA

2011-01-01

Full Text Available This paper describes an application of an evolutionary algorithm and an artificial immune systems to solve a problem of scheduling an optimal route for waste disposal garbage trucks in its daily operation. Problem of an optimisation is formulated and solved using both methods. The results are presented for an area in one of the Polish cities.

The entity-to-algorithm allocation problem: Extending the analysis

CSIR Research Space (South Africa)

Grobler, J

2014-12-01

Full Text Available . HYPOTHESES ANALYSIS OF ALTERNATIVE MULTI-METHOD ALGORITHMS. HMHH EIHH EEA-SLPS HMHH NA 4− 19− 5 11− 8− 9 EIHH 5− 19− 4 NA 6− 16− 6 EEA-SLPS 9− 8− 11 6− 16− 6 NA Multi-EA 3− 4− 21 3− 1− 24 2− 3− 23 Multi-EA TOTAL HMHH 21− 4− 3 36− 3− 17 EIHH 24− 1− 3 35− 36... ANALYSIS OF THE VARIOUS ALGORITHMS VERSUS THEIR CONSTITUENT ALGORITHMS. Algorithm HMHH EIHH EEA-SLPS Multi-EA CMAES 0-3-25 4-2-22 4-2-22 2-2-24 SaNSDE 17-2-9 16-8-4 12-12-4 5-0-23 GA 22-3-3 23-2-3 23-4-1 4-5-19 GCPSO 20-1-7 20-3-5 19-3-6 8-3-17 TOTAL 55...

Safety management in NPPs using an evolutionary algorithm technique

International Nuclear Information System (INIS)

Mishra, Alok; Patwardhan, Anand; Verma, A.K.

2007-01-01

The general goal of safety management in Nuclear Power Plants (NPPs) is to make requirements and activities more risk effective and less costly. The technical specification and maintenance (TS and M) activities in a plant are associated with controlling risk or with satisfying requirements, and are candidates to be evaluated for their resource effectiveness in risk-informed applications. Accordingly, the risk-based analysis of technical specification (RBTS) is being considered in evaluating current TS. The multi-objective optimization of the TS and M requirements of a NPP based on risk and cost, gives the pareto-optimal solutions, from which the utility can pick its decision variables suiting its interest. In this paper, a multi-objective evolutionary algorithm technique has been used to make a trade-off between risk and cost both at the system level and at the plant level for loss of coolant accident (LOCA) and main steam line break (MSLB) as initiating events

Analysis of ethyl acrylate (EA) and acrylic acid (AA) residues from rat tissues following oral ea dosing

International Nuclear Information System (INIS)

Udinsky, J.R.; Frederick, C.B.

1990-01-01

Gavage dosing of rats with EA at high dose levels (100 or 200 mg/kg) has resulted in tumors at the dosing site, forestomach (FST), but no lesions of the glandular stomach (GST) or other remote tissues. Since previous in vitro studies have demonstrated that EA is very rapidly metabolized to AA and glutathione conjugates, EA and AA residues were analyzed 0-24 hr following gavage dosing of non-fasted F-344/N male rats with [1- 14 C]EA in corn oil at 10, 50, and 200 mg/kg. Analysis of total 14 C indicated that the dose solution was primarily in the FST at ≥5 min after dosing, although 14 C was detected in the GST, duodenum, and small intestine (attributed to distension of the FST and leakage from the FST to the GST). HPLC analysis of the gut contents, gut wall, liver, kidneys, lungs, and blood indicated that EA and AA could only be detected at ≥15 min in the FST and GST contents, and in the FST tissue. AA alone was detected in the GST tissue, duodenum tissue and contents, and small intestine tissue and contents. The minimum level of detection was 0.0005% of the dose. The remaining 14 C was primarily attributed to binding to the gut contents or bioincorporation of AA. The detection of EA and AA residues only in the upper gastrointestinal tract following gavage dosing is consistent with rapid detoxification of EA by hydrolysis and conjugation which prevents toxicity at sites remote form the site of dosing

An Evolutionary Algorithm for Multiobjective Fuzzy Portfolio Selection Models with Transaction Cost and Liquidity

Directory of Open Access Journals (Sweden)

Wei Yue

2015-01-01

Full Text Available The major issues for mean-variance-skewness models are the errors in estimations that cause corner solutions and low diversity in the portfolio. In this paper, a multiobjective fuzzy portfolio selection model with transaction cost and liquidity is proposed to maintain the diversity of portfolio. In addition, we have designed a multiobjective evolutionary algorithm based on decomposition of the objective space to maintain the diversity of obtained solutions. The algorithm is used to obtain a set of Pareto-optimal portfolios with good diversity and convergence. To demonstrate the effectiveness of the proposed model and algorithm, the performance of the proposed algorithm is compared with the classic MOEA/D and NSGA-II through some numerical examples based on the data of the Shanghai Stock Exchange Market. Simulation results show that our proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms and the proposed model can maintain quite well the diversity of portfolio. The purpose of this paper is to deal with portfolio problems in the weighted possibilistic mean-variance-skewness (MVS and possibilistic mean-variance-skewness-entropy (MVS-E frameworks with transaction cost and liquidity and to provide different Pareto-optimal investment strategies as diversified as possible for investors at a time, rather than one strategy for investors at a time.

Optimum oil production planning using infeasibility driven evolutionary algorithm.

Science.gov (United States)

Singh, Hemant Kumar; Ray, Tapabrata; Sarker, Ruhul

2013-01-01

In this paper, we discuss a practical oil production planning optimization problem. For oil wells with insufficient reservoir pressure, gas is usually injected to artificially lift oil, a practice commonly referred to as enhanced oil recovery (EOR). The total gas that can be used for oil extraction is constrained by daily availability limits. The oil extracted from each well is known to be a nonlinear function of the gas injected into the well and varies between wells. The problem is to identify the optimal amount of gas that needs to be injected into each well to maximize the amount of oil extracted subject to the constraint on the total daily gas availability. The problem has long been of practical interest to all major oil exploration companies as it has the potential to derive large financial benefit. In this paper, an infeasibility driven evolutionary algorithm is used to solve a 56 well reservoir problem which demonstrates its efficiency in solving constrained optimization problems. Furthermore, a multi-objective formulation of the problem is posed and solved using a number of algorithms, which eliminates the need for solving the (single objective) problem on a regular basis. Lastly, a modified single objective formulation of the problem is also proposed, which aims to maximize the profit instead of the quantity of oil. It is shown that even with a lesser amount of oil extracted, more economic benefits can be achieved through the modified formulation.

A Hybrid Quantum Evolutionary Algorithm with Improved Decoding Scheme for a Robotic Flow Shop Scheduling Problem

Directory of Open Access Journals (Sweden)

Weidong Lei

2017-01-01

Full Text Available We aim at solving the cyclic scheduling problem with a single robot and flexible processing times in a robotic flow shop, which is a well-known optimization problem in advanced manufacturing systems. The objective of the problem is to find an optimal robot move sequence such that the throughput rate is maximized. We propose a hybrid algorithm based on the Quantum-Inspired Evolutionary Algorithm (QEA and genetic operators for solving the problem. The algorithm integrates three different decoding strategies to convert quantum individuals into robot move sequences. The Q-gate is applied to update the states of Q-bits in each individual. Besides, crossover and mutation operators with adaptive probabilities are used to increase the population diversity. A repairing procedure is proposed to deal with infeasible individuals. Comparison results on both benchmark and randomly generated instances demonstrate that the proposed algorithm is more effective in solving the studied problem in terms of solution quality and computational time.

Evolutionary Computation Methods and their applications in Statistics

Directory of Open Access Journals (Sweden)

Francesco Battaglia

2013-05-01

Full Text Available A brief discussion of the genesis of evolutionary computation methods, their relationship to artificial intelligence, and the contribution of genetics and Darwin’s theory of natural evolution is provided. Then, the main evolutionary computation methods are illustrated: evolution strategies, genetic algorithms, estimation of distribution algorithms, differential evolution, and a brief description of some evolutionary behavior methods such as ant colony and particle swarm optimization. We also discuss the role of the genetic algorithm for multivariate probability distribution random generation, rather than as a function optimizer. Finally, some relevant applications of genetic algorithm to statistical problems are reviewed: selection of variables in regression, time series model building, outlier identification, cluster analysis, design of experiments.

Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

International Nuclear Information System (INIS)

Salazar, Daniel; Rocco, Claudio M.; Galvan, Blas J.

2006-01-01

This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature

Optimization of constrained multiple-objective reliability problems using evolutionary algorithms

Energy Technology Data Exchange (ETDEWEB)

Salazar, Daniel [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain) and Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: danielsalazaraponte@gmail.com; Rocco, Claudio M. [Facultad de Ingenieria, Universidad Central Venezuela, Caracas (Venezuela)]. E-mail: crocco@reacciun.ve; Galvan, Blas J. [Instituto de Sistemas Inteligentes y Aplicaciones Numericas en Ingenieria (IUSIANI), Division de Computacion Evolutiva y Aplicaciones (CEANI), Universidad de Las Palmas de Gran Canaria, Islas Canarias (Spain)]. E-mail: bgalvan@step.es

2006-09-15

This paper illustrates the use of multi-objective optimization to solve three types of reliability optimization problems: to find the optimal number of redundant components, find the reliability of components, and determine both their redundancy and reliability. In general, these problems have been formulated as single objective mixed-integer non-linear programming problems with one or several constraints and solved by using mathematical programming techniques or special heuristics. In this work, these problems are reformulated as multiple-objective problems (MOP) and then solved by using a second-generation Multiple-Objective Evolutionary Algorithm (MOEA) that allows handling constraints. The MOEA used in this paper (NSGA-II) demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker with a complete picture of the optimal solution space. Finally, the advantages of both MOP and MOEA approaches are illustrated by solving four redundancy problems taken from the literature.

Support vector machines and evolutionary algorithms for classification single or together?

CERN Document Server

Stoean, Catalin

2014-01-01

When discussing classification, support vector machines are known to be a capable and efficient technique to learn and predict with high accuracy within a quick time frame. Yet, their black box means to do so make the practical users quite circumspect about relying on it, without much understanding of the how and why of its predictions. The question raised in this book is how can this ‘masked hero’ be made more comprehensible and friendly to the public: provide a surrogate model for its hidden optimization engine, replace the method completely or appoint a more friendly approach to tag along and offer the much desired explanations? Evolutionary algorithms can do all these and this book presents such possibilities of achieving high accuracy, comprehensibility, reasonable runtime as well as unconstrained performance.

Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making

OpenAIRE

Rajesh Kumar; S.C. Kaushik; Raj Kumar; Ranjana Hans

2016-01-01

Brayton heat engine model is developed in MATLAB simulink environment and thermodynamic optimization based on finite time thermodynamic analysis along with multiple criteria is implemented. The proposed work investigates optimal values of various decision variables that simultaneously optimize power output, thermal efficiency and ecological function using evolutionary algorithm based on NSGA-II. Pareto optimal frontier between triple and dual objectives is obtained and best optimal value is s...

A possibilistic approach to rotorcraft design through a multi-objective evolutionary algorithm

Science.gov (United States)

Chae, Han Gil

Most of the engineering design processes in use today in the field may be considered as a series of successive decision making steps. The decision maker uses information at hand, determines the direction of the procedure, and generates information for the next step and/or other decision makers. However, the information is often incomplete, especially in the early stages of the design process of a complex system. As the complexity of the system increases, uncertainties eventually become unmanageable using traditional tools. In such a case, the tools and analysis values need to be "softened" to account for the designer's intuition. One of the methods that deals with issues of intuition and incompleteness is possibility theory. Through the use of possibility theory coupled with fuzzy inference, the uncertainties estimated by the intuition of the designer are quantified for design problems. By involving quantified uncertainties in the tools, the solutions can represent a possible set, instead of a crisp spot, for predefined levels of certainty. From a different point of view, it is a well known fact that engineering design is a multi-objective problem or a set of such problems. The decision maker aims to find satisfactory solutions, sometimes compromising the objectives that conflict with each other. Once the candidates of possible solutions are generated, a satisfactory solution can be found by various decision-making techniques. A number of multi-objective evolutionary algorithms (MOEAs) have been developed, and can be found in the literature, which are capable of generating alternative solutions and evaluating multiple sets of solutions in one single execution of an algorithm. One of the MOEA techniques that has been proven to be very successful for this class of problems is the strength Pareto evolutionary algorithm (SPEA) which falls under the dominance-based category of methods. The Pareto dominance that is used in SPEA, however, is not enough to account for the

Thermo-economic multi-objective optimization of solar dish-Stirling engine by implementing evolutionary algorithm

International Nuclear Information System (INIS)

Ahmadi, Mohammad H.; Sayyaadi, Hoseyn; Mohammadi, Amir H.; Barranco-Jimenez, Marco A.

2013-01-01

Highlights: • Thermo-economic multi-objective optimization of solar dish-Stirling engine is studied. • Application of the evolutionary algorithm is investigated. • Error analysis is done to find out the error through investigation. - Abstract: In the recent years, remarkable attention is drawn to Stirling engine due to noticeable advantages, for instance a lot of resources such as biomass, fossil fuels and solar energy can be applied as heat source. Great number of studies are conducted on Stirling engine and finite time thermo-economic is one of them. In the present study, the dimensionless thermo-economic objective function, thermal efficiency and dimensionless power output are optimized for a dish-Stirling system using finite time thermo-economic analysis and NSGA-II algorithm. Optimized answers are chosen from the results using three decision-making methods. Error analysis is done to find out the error through investigation

Attribute Index and Uniform Design Based Multiobjective Association Rule Mining with Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Jie Zhang

2013-01-01

Full Text Available In association rule mining, evaluating an association rule needs to repeatedly scan database to compare the whole database with the antecedent, consequent of a rule and the whole rule. In order to decrease the number of comparisons and time consuming, we present an attribute index strategy. It only needs to scan database once to create the attribute index of each attribute. Then all metrics values to evaluate an association rule do not need to scan database any further, but acquire data only by means of the attribute indices. The paper visualizes association rule mining as a multiobjective problem rather than a single objective one. In order to make the acquired solutions scatter uniformly toward the Pareto frontier in the objective space, elitism policy and uniform design are introduced. The paper presents the algorithm of attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm, abbreviated as IUARMMEA. It does not require the user-specified minimum support and minimum confidence anymore, but uses a simple attribute index. It uses a well-designed real encoding so as to extend its application scope. Experiments performed on several databases demonstrate that the proposed algorithm has excellent performance, and it can significantly reduce the number of comparisons and time consumption.

Attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm.

Science.gov (United States)

Zhang, Jie; Wang, Yuping; Feng, Junhong

2013-01-01

In association rule mining, evaluating an association rule needs to repeatedly scan database to compare the whole database with the antecedent, consequent of a rule and the whole rule. In order to decrease the number of comparisons and time consuming, we present an attribute index strategy. It only needs to scan database once to create the attribute index of each attribute. Then all metrics values to evaluate an association rule do not need to scan database any further, but acquire data only by means of the attribute indices. The paper visualizes association rule mining as a multiobjective problem rather than a single objective one. In order to make the acquired solutions scatter uniformly toward the Pareto frontier in the objective space, elitism policy and uniform design are introduced. The paper presents the algorithm of attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm, abbreviated as IUARMMEA. It does not require the user-specified minimum support and minimum confidence anymore, but uses a simple attribute index. It uses a well-designed real encoding so as to extend its application scope. Experiments performed on several databases demonstrate that the proposed algorithm has excellent performance, and it can significantly reduce the number of comparisons and time consumption.

An evolutionary algorithm for port-of-entry security optimization considering sensor thresholds

International Nuclear Information System (INIS)

Concho, Ana Lisbeth; Ramirez-Marquez, Jose Emmanuel

2010-01-01

According to the US Customs and Border Protection (CBP), the number of offloaded ship cargo containers arriving at US seaports each year amounts to more than 11 million. The costs of locating an undetonated terrorist weapon at one US port, or even worst, the cost caused by a detonated weapon of mass destruction, would amount to billions of dollars. These costs do not yet account for the devastating consequences that it would cause in the ability to keep the supply chain operating and the sociological and psychological effects. As such, this paper is concerned with developing a container inspection strategy that minimizes the total cost of inspection while maintaining a user specified detection rate for 'suspicious' containers. In this respect and based on a general decision-tree model, this paper presents a holistic evolutionary algorithm for finding the following: (1) optimal threshold values for every sensor and (2) the optimal configuration of the inspection strategy. The algorithm is under the assumption that different sensors with different reliability and cost characteristics can be used. Testing and experimentation show the proposed approach consistently finds high quality solutions in a reduced computational time.

E+A Galaxy Properties and Post-Starburst Galaxy Evolution Data through SDSS-IV MaNGA and Illustris: A Co-Analysis

Science.gov (United States)

Ojanen, Winonah; Dudley, Raymond; Edwards, Kay; Gonzalez, Andrea; Johnson, Amalya; Kerrison, Nicole; Marinelli, Mariarosa; Melchert, Nancy; Liu, Charles; Sloan Collaboration, SDSS-IV MaNGA

2018-01-01

E+A galaxies (Elliptical + A-type stars) are post-starburst galaxies that have experienced a sudden quenching phase. Using previous research methods, 39 candidates out of 2,812 galaxies observed, or 1.4%, were selected from the SDSS-IV MaNGA survey. We then identified morphological characteristics of the 39 galaxies including stellar kinematics, Gini coefficient, gas density and distribution and stellar ages. To study the origin of how E+A galaxies evolved to their present state, galaxy simulation data from the Illustris simulation was utilized to identify similar quenched post-starburst candidates. Seven post-starburst candidates were identified through star formation rate histories of Illustris simulated galaxies. The evolution of these galaxies is studied from 0 to 13.8 billion years ago to identify what caused the starburst and quenching of the Illustris candidates. Similar morphological characteristics of Illustris post-starburst candidates are pulled from before, during, and post-starburst and compared to the same morphological characteristics of the E+A galaxies from SDSS-IV MaNGA. The characteristics and properties of the Illustris galaxies are used to identify the possible evolutionary histories of the observed E+A galaxies. This work was supported by grants AST-1460860 from the National Science Foundation and SDSS FAST/SSP-483 from the Alfred P. Sloan Foundation to the CUNY College of Staten Island.

Improvements in seismic event locations in a deep western U.S. coal mine using tomographic velocity models and an evolutionary search algorithm

Energy Technology Data Exchange (ETDEWEB)

Adam Lurka; Peter Swanson [Central Mining Institute, Katowice (Poland)

2009-09-15

Methods of improving seismic event locations were investigated as part of a research study aimed at reducing ground control safety hazards. Seismic event waveforms collected with a 23-station three-dimensional sensor array during longwall coal mining provide the data set used in the analyses. A spatially variable seismic velocity model is constructed using seismic event sources in a passive tomographic method. The resulting three-dimensional velocity model is used to relocate seismic event positions. An evolutionary optimization algorithm is implemented and used in both the velocity model development and in seeking improved event location solutions. Results obtained using the different velocity models are compared. The combination of the tomographic velocity model development and evolutionary search algorithm provides improvement to the event locations. 13 refs., 5 figs., 4 tabs.

Using Self-Adaptive Evolutionary Algorithms to Evolve Dynamism-Oriented Maps for a Real Time Strategy Game

OpenAIRE

Lara-Cabrera, Raúl; Cotta, Carlos; Fernández Leiva, Antonio J.

2013-01-01

This work presents a procedural content generation system that uses an evolutionary algorithm in order to generate interesting maps for a real-time strategy game, called Planet Wars. Interestingness is here captured by the dynamism of games (i.e., the extent to which they are action-packed). We consider two different approaches to measure the dynamism of the games resulting from these generated maps, one based on fluctuations in the resources controlled by either player and another one based ...

Reconceptualizing public participation in environmental assessment as EA civics

Energy Technology Data Exchange (ETDEWEB)

Sinclair, A. John, E-mail: jsincla@umanitoba.ca [Natural Resources Institute, University of Manitoba, Winnipeg, MB R3T 2N2 (Canada); Diduck, Alan P., E-mail: a.diduck@uwinnipeg.ca [Department of Environmental Studies and Sciences, University of Winnipeg, 515 Portage Avenue, Winnipeg R3B 2E9 (Canada)

2017-01-15

Notwithstanding the considerable attention placed on creating meaningful opportunities for public participation in environmental assessment (EA), many participants and those who have reviewed participation processes often find practice sorely wanting. This reality stands in stark juxtaposition to future environmental governance needs, which will require increased openness, deliberation and transdisciplinary knowledge in order to deal with environmental change that is ever more uncertain, complex and conflictual. In this paper, our purpose was to consider how to meet those needs through reconceptualizing public participation as EA civics, founded on an active citizen base, deliberative in nature and orientated toward learning. We do this through developing a new conceptual model of next generation participation processes that is relevant at multiple spatial scales and institutional levels, is applicable to the entire assessment cycle and spans temporal scales through feedback loops. Our EA civics model builds on the “civics approach” to environmental governance and “action civics” by extending their core ideas to participation in EA. We did this by conducting an integrative literature review (including numerous papers we have contributed over the years) and reflecting on our own experiences as EA participants. We apply current thinking on public participation design to our EA civics conceptualization and highlight important design features that have received scant attention. We conclude that EA civics holds promise for fairer and more robust participation processes if all aspects of the model are considered and the actions related to each are implemented. - Highlights: • Consideration of the ‘civics approach’ and ‘action civics’ in an EA context • Conceptualization of public participation as EA civics • Reflection on the EA civics as a model of participation suitable for next generation assessment.

Reconceptualizing public participation in environmental assessment as EA civics

International Nuclear Information System (INIS)

Sinclair, A. John; Diduck, Alan P.

2017-01-01

Notwithstanding the considerable attention placed on creating meaningful opportunities for public participation in environmental assessment (EA), many participants and those who have reviewed participation processes often find practice sorely wanting. This reality stands in stark juxtaposition to future environmental governance needs, which will require increased openness, deliberation and transdisciplinary knowledge in order to deal with environmental change that is ever more uncertain, complex and conflictual. In this paper, our purpose was to consider how to meet those needs through reconceptualizing public participation as EA civics, founded on an active citizen base, deliberative in nature and orientated toward learning. We do this through developing a new conceptual model of next generation participation processes that is relevant at multiple spatial scales and institutional levels, is applicable to the entire assessment cycle and spans temporal scales through feedback loops. Our EA civics model builds on the “civics approach” to environmental governance and “action civics” by extending their core ideas to participation in EA. We did this by conducting an integrative literature review (including numerous papers we have contributed over the years) and reflecting on our own experiences as EA participants. We apply current thinking on public participation design to our EA civics conceptualization and highlight important design features that have received scant attention. We conclude that EA civics holds promise for fairer and more robust participation processes if all aspects of the model are considered and the actions related to each are implemented. - Highlights: • Consideration of the ‘civics approach’ and ‘action civics’ in an EA context • Conceptualization of public participation as EA civics • Reflection on the EA civics as a model of participation suitable for next generation assessment

Multivariable optimization of an auto-thermal ammonia synthesis reactor using genetic algorithm

Science.gov (United States)

Anh-Nga, Nguyen T.; Tuan-Anh, Nguyen; Tien-Dung, Vu; Kim-Trung, Nguyen

2017-09-01

The ammonia synthesis system is an important chemical process used in the manufacture of fertilizers, chemicals, explosives, fibers, plastics, refrigeration. In the literature, many works approaching the modeling, simulation and optimization of an auto-thermal ammonia synthesis reactor can be found. However, they just focus on the optimization of the reactor length while keeping the others parameters constant. In this study, the other parameters are also considered in the optimization problem such as the temperature of feed gas enters the catalyst zone. The optimal problem requires the maximization of a multivariable objective function which subjects to a number of equality constraints involving the solution of coupled differential equations and also inequality constraints. The solution of an optimization problem can be found through, among others, deterministic or stochastic approaches. The stochastic methods, such as evolutionary algorithm (EA), which is based on natural phenomenon, can overcome the drawbacks such as the requirement of the derivatives of the objective function and/or constraints, or being not efficient in non-differentiable or discontinuous problems. Genetic algorithm (GA) which is a class of EA, exceptionally simple, robust at numerical optimization and is more likely to find a true global optimum. In this study, the genetic algorithm is employed to find the optimum profit of the process. The inequality constraints were treated using penalty method. The coupled differential equations system was solved using Runge-Kutta 4th order method. The results showed that the presented numerical method could be applied to model the ammonia synthesis reactor. The optimum economic profit obtained from this study are also compared to the results from the literature. It suggests that the process should be operated at higher temperature of feed gas in catalyst zone and the reactor length is slightly longer.

Nature-inspired Cuckoo Search Algorithm for Side Lobe Suppression in a Symmetric Linear Antenna Array

Directory of Open Access Journals (Sweden)

K. N. Abdul Rani

2012-09-01

Full Text Available In this paper, we proposed a newly modified cuckoo search (MCS algorithm integrated with the Roulette wheel selection operator and the inertia weight controlling the search ability towards synthesizing symmetric linear array geometry with minimum side lobe level (SLL and/or nulls control. The basic cuckoo search (CS algorithm is primarily based on the natural obligate brood parasitic behavior of some cuckoo species in combination with the Levy flight behavior of some birds and fruit flies. The CS metaheuristic approach is straightforward and capable of solving effectively general N-dimensional, linear and nonlinear optimization problems. The array geometry synthesis is first formulated as an optimization problem with the goal of SLL suppression and/or null prescribed placement in certain directions, and then solved by the newly MCS algorithm for the optimum element or isotropic radiator locations in the azimuth-plane or xy-plane. The study also focuses on the four internal parameters of MCS algorithm specifically on their implicit effects in the array synthesis. The optimal inter-element spacing solutions obtained by the MCS-optimizer are validated through comparisons with the standard CS-optimizer and the conventional array within the uniform and the Dolph-Chebyshev envelope patterns using MATLABTM. Finally, we also compared the fine-tuned MCS algorithm with two popular evolutionary algorithm (EA techniques include particle swarm optimization (PSO and genetic algorithms (GA.

XTALOPT version r11: An open-source evolutionary algorithm for crystal structure prediction

Science.gov (United States)

Avery, Patrick; Falls, Zackary; Zurek, Eva

2018-01-01

Version 11 of XTALOPT, an evolutionary algorithm for crystal structure prediction, has now been made available for download from the CPC library or the XTALOPT website, http://xtalopt.github.io. Whereas the previous versions of XTALOPT were published under the Gnu Public License (GPL), the current version is made available under the 3-Clause BSD License, which is an open source license that is recognized by the Open Source Initiative. Importantly, the new version can be executed via a command line interface (i.e., it does not require the use of a Graphical User Interface). Moreover, the new version is written as a stand-alone program, rather than an extension to AVOGADRO.

Parallel optimization algorithm for drone inspection in the building industry

Science.gov (United States)

Walczyński, Maciej; BoŻejko, Wojciech; Skorupka, Dariusz

2017-07-01

In this paper we present an approach for Vehicle Routing Problem with Drones (VRPD) in case of building inspection from the air. In autonomic inspection process there is a need to determine of the optimal route for inspection drone. This is especially important issue because of the very limited flight time of modern multicopters. The method of determining solutions for Traveling Salesman Problem(TSP), described in this paper bases on Parallel Evolutionary Algorithm (ParEA)with cooperative and independent approach for communication between threads. This method described first by Bożejko and Wodecki [1] bases on the observation that if exists some number of elements on certain positions in a number of permutations which are local minima, then those elements will be in the same position in the optimal solution for TSP problem. Numerical experiments were made on BEM computational cluster with using MPI library.

Synthesis and optimization of EA-MMA copolymer emulsion%EA-MMA共聚乳液合成工艺及优化

Institute of Scientific and Technical Information of China (English)

张成芬; 武玉民

2011-01-01

以丙烯酸乙酯(EA)和甲基丙烯酸甲酯(MMA)为共聚单体、过硫酸钾(KPS)为引发剂、十二烷基硫酸钠(SDS)和壬基酚聚氧乙烯醚(OP-10)为复合乳化剂,采用乳液聚合法合成出一种稳定的EA-MMA乳液.通过单因素试验法考察了乳化剂浓度、引发剂浓度、反应温度和反应时间等因素对乳液平均粒径和单体转化率的影响,并采用正交试验法进一步优选出制备EA-MMA乳液的最佳工艺条件.结果表明:当w(乳化剂)=2.0%、w(引发剂)=0.60%、反应时间为4 h和反应温度为80℃时,制成的EA-MMA乳液较稳定,并且单体转化率相对最高.%With ethyl acrylate(EA) and methyl methacrylate( MMA) as comonomers ,potassium persulfate(KPS)as initiator,and sodium dodecyl sulfate(SDS) and nonylphenol ethoxylates(OP-10) as composite emulsifier,a stable EA-MMA emulsion was synthesized by emulsion polymerization. The influences of some factor(such as emulsifier concentration,initiator concentration,reaction temperature and reaction time) on average particle size of emulsion and monomer conversion rate were investigated by single factor experiment method,then the optimal process conditions of preparing EA-MMA emulsion were preferred by orthogonal experiment method. The results showed that the EA-MMA emulsion had better stability and relatively highest monomer conversion rate when the mass fractions of emulsifier and initiator were 2.0％ and 0.60％ respectively,the reaction time and reaction temperature were 4 h and 80 ℃ respectively.

MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS APPLIED TO MICROSTRIP ANTENNAS DESIGN ALGORITMOS EVOLUTIVOS MULTIOBJETIVO APLICADOS A LOS PROYECTOS DE ANTENAS MICROSTRIP

Directory of Open Access Journals (Sweden)

Juliano Rodrigues Brianeze

2009-12-01

Full Text Available This work presents three of the main evolutionary algorithms: Genetic Algorithm, Evolution Strategy and Evolutionary Programming, applied to microstrip antennas design. Efficiency tests were performed, considering the analysis of key physical and geometrical parameters, evolution type, numerical random generators effects, evolution operators and selection criteria. These algorithms were validated through design of microstrip antennas based on the Resonant Cavity Method, and allow multiobjective optimizations, considering bandwidth, standing wave ratio and relative material permittivity. The optimal results obtained with these optimization processes, were confirmed by CST Microwave Studio commercial package.Este trabajo presenta tres de los principales algoritmos evolutivos: Algoritmo Genético, Estrategia Evolutiva y Programación Evolutiva, aplicados al diseño de antenas de microlíneas (microstrip. Se realizaron pruebas de eficiencia de los algoritmos, considerando el análisis de los parámetros físicos y geométricos, tipo de evolución, efecto de generación de números aleatorios, operadores evolutivos y los criterios de selección. Estos algoritmos fueron validados a través del diseño de antenas de microlíneas basado en el Método de Cavidades Resonantes y permiten optimizaciones multiobjetivo, considerando ancho de banda, razón de onda estacionaria y permitividad relativa del dieléctrico. Los resultados óptimos obtenidos fueron confirmados a través del software comercial CST Microwave Studio.

An evolutionary algorithm for tomographic reconstructions in limited data sets problems

International Nuclear Information System (INIS)

Turcanu, Catrinel; Craciunescu, Teddy

2000-01-01

The paper proposes a new method for tomographic reconstructions. Unlike nuclear medicine applications, in physical science problems we are often confronted with limited data sets: constraints in the number of projections or limited angle views. The problem of image reconstruction from projections may be considered as a problem of finding an image (solution) having projections that match the experimental ones. In our approach, we choose a statistical correlation coefficient to evaluate the fitness of any potential solution. The optimization process is carried out by an evolutionary algorithm. Our algorithm has some problem-oriented characteristics. One of them is that a chromosome, representing a potential solution, is not linear but coded as a matrix of pixels corresponding to a two-dimensional image. This kind of internal representation reflects the genuine manifestation and slight differences between two points situated in the original problem space give rise to similar differences once they become coded. Another particular feature is a newly built crossover operator: the grid-based crossover, suitable for high dimension two-dimensional chromosomes. Except for the population size and the dimension of the cutting grid for the grid-based crossover, all the other parameters of the algorithm are independent of the geometry of the tomographic reconstruction. The performances of the method are evaluated in comparison with a traditional tomographic method, based on the maximization of the entropy of the image, that proved to work well with limited data sets. The test phantom is typical for an application with limited data sets: the determination of the neutron energy spectra with time resolution in case of short-pulsed neutron emission. The qualitative judgement and also the quantitative one, based on some figures of merit, point out that the proposed method ensures an improved reconstruction of shapes, sizes and resolution in the image, even in the presence of noise

Multi-objective thermodynamic optimization of an irreversible regenerative Brayton cycle using evolutionary algorithm and decision making

Directory of Open Access Journals (Sweden)

Rajesh Kumar

2016-06-01

Full Text Available Brayton heat engine model is developed in MATLAB simulink environment and thermodynamic optimization based on finite time thermodynamic analysis along with multiple criteria is implemented. The proposed work investigates optimal values of various decision variables that simultaneously optimize power output, thermal efficiency and ecological function using evolutionary algorithm based on NSGA-II. Pareto optimal frontier between triple and dual objectives is obtained and best optimal value is selected using Fuzzy, TOPSIS, LINMAP and Shannon’s entropy decision making methods. Triple objective evolutionary approach applied to the proposed model gives power output, thermal efficiency, ecological function as (53.89 kW, 0.1611, −142 kW which are 29.78%, 25.86% and 21.13% lower in comparison with reversible system. Furthermore, the present study reflects the effect of various heat capacitance rates and component efficiencies on triple objectives in graphical custom. Finally, with the aim of error investigation, average and maximum errors of obtained results are computed.

Evolutionary history of the thicket rats (genus Grammomys) mirrors the evolution of African forests since late Miocene

Czech Academy of Sciences Publication Activity Database

Bryja, J.; Šumbera, R.; Kerbis Peterhans, J. C.; Aghová, T.; Bryjová, A.; Mikula, Ondřej; Nicolas, V.; Denys, C.; Verheyen, E.

2017-01-01

Roč. 44, č. 1 (2017), s. 182-194 ISSN 0305-0270 Institutional support: RVO:67985904 Keywords : Arvicanthini * coastal forests * late Miocene Subject RIV: EA - Cell Biology OBOR OECD: Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology Impact factor: 4.248, year: 2016

Identifying irregularly shaped crime hot-spots using a multiobjective evolutionary algorithm

Science.gov (United States)

Wu, Xiaolan; Grubesic, Tony H.

2010-12-01

Spatial cluster detection techniques are widely used in criminology, geography, epidemiology, and other fields. In particular, spatial scan statistics are popular and efficient techniques for detecting areas of elevated crime or disease events. The majority of spatial scan approaches attempt to delineate geographic zones by evaluating the significance of clusters using likelihood ratio statistics tested with the Poisson distribution. While this can be effective, many scan statistics give preference to circular clusters, diminishing their ability to identify elongated and/or irregular shaped clusters. Although adjusting the shape of the scan window can mitigate some of these problems, both the significance of irregular clusters and their spatial structure must be accounted for in a meaningful way. This paper utilizes a multiobjective evolutionary algorithm to find clusters with maximum significance while quantitatively tracking their geographic structure. Crime data for the city of Cincinnati are utilized to demonstrate the advantages of the new approach and highlight its benefits versus more traditional scan statistics.

A Novel Handwritten Letter Recognizer Using Enhanced Evolutionary Neural Network

Science.gov (United States)

Mahmoudi, Fariborz; Mirzashaeri, Mohsen; Shahamatnia, Ehsan; Faridnia, Saed

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. Evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

47 CFR 11.54 - EAS operation during a National Level emergency.

Science.gov (United States)

2010-10-01

... 47 Telecommunication 1 2010-10-01 2010-10-01 false EAS operation during a National Level emergency... SYSTEM (EAS) Emergency Operations § 11.54 EAS operation during a National Level emergency. (a) The EAS Operating Handbook summarizes the procedures to be followed upon receipt of a National level EAN or EAT...

Evolutionary Statistical Procedures

CERN Document Server

Baragona, Roberto; Poli, Irene

2011-01-01

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions a

An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm.

Science.gov (United States)

Deb, Kalyanmoy; Sinha, Ankur

2010-01-01

Bilevel optimization problems involve two optimization tasks (upper and lower level), in which every feasible upper level solution must correspond to an optimal solution to a lower level optimization problem. These problems commonly appear in many practical problem solving tasks including optimal control, process optimization, game-playing strategy developments, transportation problems, and others. However, they are commonly converted into a single level optimization problem by using an approximate solution procedure to replace the lower level optimization task. Although there exist a number of theoretical, numerical, and evolutionary optimization studies involving single-objective bilevel programming problems, not many studies look at the context of multiple conflicting objectives in each level of a bilevel programming problem. In this paper, we address certain intricate issues related to solving multi-objective bilevel programming problems, present challenging test problems, and propose a viable and hybrid evolutionary-cum-local-search based algorithm as a solution methodology. The hybrid approach performs better than a number of existing methodologies and scales well up to 40-variable difficult test problems used in this study. The population sizing and termination criteria are made self-adaptive, so that no additional parameters need to be supplied by the user. The study indicates a clear niche of evolutionary algorithms in solving such difficult problems of practical importance compared to their usual solution by a computationally expensive nested procedure. The study opens up many issues related to multi-objective bilevel programming and hopefully this study will motivate EMO and other researchers to pay more attention to this important and difficult problem solving activity.

On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

Science.gov (United States)

Jacobsen, T. L.; Jørgensen, M. S.; Hammer, B.

2018-01-01

Machine learning (ML) is used to derive local stability information for density functional theory calculations of systems in relation to the recently discovered SnO2 (110 )-(4 ×1 ) reconstruction. The ML model is trained on (structure, total energy) relations collected during global minimum energy search runs with an evolutionary algorithm (EA). While being built, the ML model is used to guide the EA, thereby speeding up the overall rate by which the EA succeeds. Inspection of the local atomic potentials emerging from the model further shows chemically intuitive patterns.

Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design

International Nuclear Information System (INIS)

Toffolo, A.; Lazzaretto, A.

2002-01-01

Thermoeconomic analyses in thermal system design are always focused on the economic objective. However, knowledge of only the economic minimum may not be sufficient in the decision making process, since solutions with a higher thermodynamic efficiency, in spite of small increases in total costs, may result in much more interesting designs due to changes in energy market prices or in energy policies. This paper suggests how to perform a multi-objective optimization in order to find solutions that simultaneously satisfy exergetic and economic objectives. This corresponds to a search for the set of Pareto optimal solutions with respect to the two competing objectives. The optimization process is carried out by an evolutionary algorithm, that features a new diversity preserving mechanism using as a test case the well-known CGAM problem. (author)

Bio-inspired algorithms applied to molecular docking simulations.

Science.gov (United States)

Heberlé, G; de Azevedo, W F

2011-01-01

Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.

Optimization of operating schedule of machines in granite industry using evolutionary algorithms

International Nuclear Information System (INIS)

Loganthurai, P.; Rajasekaran, V.; Gnanambal, K.

2014-01-01

Highlights: • Operating time of machines in granite industries was studied. • Operating time has been optimized using evolutionary algorithms such as PSO, DE. • The maximum demand has been reduced. • Hence the electricity cost of the industry and feeder stress have been reduced. - Abstract: Electrical energy consumption cost plays an important role in the production cost of any industry. The electrical energy consumption cost is calculated as two part tariff, the first part is maximum demand cost and the second part is energy consumption cost or unit cost (kW h). The maximum demand cost can be reduced without affecting the production. This paper focuses on the reduction of maximum demand by proper operating schedule of major equipments. For this analysis, various granite industries are considered. The major equipments in granite industries are cutting machine, polishing machine and compressor. To reduce the maximum demand, the operating time of polishing machine is rescheduled by optimization techniques such as Differential Evolution (DE) and particle swarm optimization (PSO). The maximum demand costs are calculated before and after rescheduling. The results show that if the machines are optimally operated, the cost is reduced. Both DE and PSO algorithms reduce the maximum demand cost at the same rate for all the granite industries. However, the optimum scheduling obtained by DE reduces the feeder power flow than the PSO scheduling

EAS experiment on board the Airbus A380

International Nuclear Information System (INIS)

Capdevielle, J.N.; Cohen, F.; Jedrzejczak, K.; Szabelska, B.; Szabelski, J.; Wibig, T.

2006-01-01

We consider taking the opportunity of about 10,000 hours of test flights of the Airbus A380, and to install in the passenger area detectors for high energy cosmic ray events. The altitude of 10 km (250 g/cm 2 ) presents the opportunity to measure EAS originating from heavy primaries to energies exceeding 10 7 GeV, and also the proton component beyond the energy of the knee arriving without interaction. At the flight altitude iron originated EAS are well developed and by registering their lateral distribution in the 70 meters long cabin it will be possible to distinguish them from proton originated EAS. The hadron component of EAS (registered as number of secondary hadrons produced in the detector) would help in discrimination and energy estimation. On the other hand, the detection of high energy protons (without EAS) would enable us to measure the high energy proton spectrum. The proton energy would be estimated via hadron multiplicity in single interactions inside the detector. We propose to use about 60 modules of 0.5 m 2 of active detectors with scintillators to detect E-M component and a carbon target with lead layer to detect the hadronic component via neutron registrations

MMAS vs. Population-based EA on a family of dynamic fitness functions

DEFF Research Database (Denmark)

Lissovoi, Andrei; Witt, Carsten

2014-01-01

We study the behavior of a population-based EA and the Max-Min Ant System (MMAS) on a family of deterministically-changing fitness functions, where, in order to find the global optimum, the algorithms have to find specific local optima within each of a series of phases. In particular, we prove...... a Maze function extended over a finite alphabet of μ symbols, whereas population size μ-1 is not sufficient. Furthermore, we show that MMAS does not require additional modifications to track the optimum of the finite-alphabet Maze functions, and, using a novel drift statement to simplify the analysis...

MMAS Versus Population-Based EA on a Family of Dynamic Fitness Functions

DEFF Research Database (Denmark)

Lissovoi, Andrei; Witt, Carsten

2015-01-01

We study the behavior of a population-based EA and the Max–Min Ant System (MMAS) on a family of deterministically-changing fitness functions, where, in order to find the global optimum, the algorithms have to find specific local optima within each of a series of phases. In particular, we prove...... function extended over a finite alphabet of μ symbols, whereas population size μ−1 is not sufficient. Furthermore, we show that MMAS does not require additional modifications to track the optimum of the finite-alphabet Maze functions, and, using a novel drift statement to simplify the analysis, reduce...

EAS Telecommunications Certification Bodies (TCB)

Data.gov (United States)

Federal Communications Commission — EAS (Equipment Authorization System). A Telecommunication Certification Body (TCB) is an accredited product certification body with the authority to issue Grants of...

Test scheduling optimization for 3D network-on-chip based on cloud evolutionary algorithm of Pareto multi-objective

Science.gov (United States)

Xu, Chuanpei; Niu, Junhao; Ling, Jing; Wang, Suyan

2018-03-01

In this paper, we present a parallel test strategy for bandwidth division multiplexing under the test access mechanism bandwidth constraint. The Pareto solution set is combined with a cloud evolutionary algorithm to optimize the test time and power consumption of a three-dimensional network-on-chip (3D NoC). In the proposed method, all individuals in the population are sorted in non-dominated order and allocated to the corresponding level. Individuals with extreme and similar characteristics are then removed. To increase the diversity of the population and prevent the algorithm from becoming stuck around local optima, a competition strategy is designed for the individuals. Finally, we adopt an elite reservation strategy and update the individuals according to the cloud model. Experimental results show that the proposed algorithm converges to the optimal Pareto solution set rapidly and accurately. This not only obtains the shortest test time, but also optimizes the power consumption of the 3D NoC.

Developing a Shuffled Complex-Self Adaptive Hybrid Evolution (SC-SAHEL) Framework for Water Resources Management and Water-Energy System Optimization

Science.gov (United States)

Rahnamay Naeini, M.; Sadegh, M.; AghaKouchak, A.; Hsu, K. L.; Sorooshian, S.; Yang, T.

2017-12-01

Meta-Heuristic optimization algorithms have gained a great deal of attention in a wide variety of fields. Simplicity and flexibility of these algorithms, along with their robustness, make them attractive tools for solving optimization problems. Different optimization methods, however, hold algorithm-specific strengths and limitations. Performance of each individual algorithm obeys the "No-Free-Lunch" theorem, which means a single algorithm cannot consistently outperform all possible optimization problems over a variety of problems. From users' perspective, it is a tedious process to compare, validate, and select the best-performing algorithm for a specific problem or a set of test cases. In this study, we introduce a new hybrid optimization framework, entitled Shuffled Complex-Self Adaptive Hybrid EvoLution (SC-SAHEL), which combines the strengths of different evolutionary algorithms (EAs) in a parallel computing scheme, and allows users to select the most suitable algorithm tailored to the problem at hand. The concept of SC-SAHEL is to execute different EAs as separate parallel search cores, and let all participating EAs to compete during the course of the search. The newly developed SC-SAHEL algorithm is designed to automatically select, the best performing algorithm for the given optimization problem. This algorithm is rigorously effective in finding the global optimum for several strenuous benchmark test functions, and computationally efficient as compared to individual EAs. We benchmark the proposed SC-SAHEL algorithm over 29 conceptual test functions, and two real-world case studies - one hydropower reservoir model and one hydrological model (SAC-SMA). Results show that the proposed framework outperforms individual EAs in an absolute majority of the test problems, and can provide competitive results to the fittest EA algorithm with more comprehensive information during the search. The proposed framework is also flexible for merging additional EAs, boundary

Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Boyang Qu

2017-12-01

Full Text Available The intermittency of wind power and the large-scale integration of electric vehicles (EVs bring new challenges to the reliability and economy of power system dispatching. In this paper, a novel multi-objective dynamic economic emission dispatch (DEED model is proposed considering the EVs and uncertainties of wind power. The total fuel cost and pollutant emission are considered as the optimization objectives, and the vehicle to grid (V2G power and the conventional generator output power are set as the decision variables. The stochastic wind power is derived by Weibull probability distribution function. Under the premise of meeting the system energy and user’s travel demand, the charging and discharging behavior of the EVs are dynamically managed. Moreover, we propose a two-step dynamic constraint processing strategy for decision variables based on penalty function, and, on this basis, the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D algorithm is improved. The proposed model and approach are verified by the 10-generator system. The results demonstrate that the proposed DEED model and the improved MOEA/D algorithm are effective and reasonable.

Crossover Can Be Constructive When Computing Unique Input Output Sequences

DEFF Research Database (Denmark)

Lehre, Per Kristian; Yao, Xin

2010-01-01

Unique input output (UIO) sequences have important applications in conformance testing of finite state machines (FSMs). Previous experimental and theoretical research has shown that evolutionary algorithms (EAs) can compute UIOs efficiently on many FSM instance classes, but fail on others. However...

Multiobjective Multifactorial Optimization in Evolutionary Multitasking.

Science.gov (United States)

Gupta, Abhishek; Ong, Yew-Soon; Feng, Liang; Tan, Kay Chen

2016-05-03

In recent decades, the field of multiobjective optimization has attracted considerable interest among evolutionary computation researchers. One of the main features that makes evolutionary methods particularly appealing for multiobjective problems is the implicit parallelism offered by a population, which enables simultaneous convergence toward the entire Pareto front. While a plethora of related algorithms have been proposed till date, a common attribute among them is that they focus on efficiently solving only a single optimization problem at a time. Despite the known power of implicit parallelism, seldom has an attempt been made to multitask, i.e., to solve multiple optimization problems simultaneously. It is contended that the notion of evolutionary multitasking leads to the possibility of automated transfer of information across different optimization exercises that may share underlying similarities, thereby facilitating improved convergence characteristics. In particular, the potential for automated transfer is deemed invaluable from the standpoint of engineering design exercises where manual knowledge adaptation and reuse are routine. Accordingly, in this paper, we present a realization of the evolutionary multitasking paradigm within the domain of multiobjective optimization. The efficacy of the associated evolutionary algorithm is demonstrated on some benchmark test functions as well as on a real-world manufacturing process design problem from the composites industry.

EAS Equipment Authorization Grantee Registrations

Data.gov (United States)

Federal Communications Commission — EAS (Equipment Authorization System). Radio Frequency (RF) devices are required to be properly authorized under 47 CFR Part 2 prior to being marketed or imported...

A new evolutionary algorithm with LVQ learning for the optimization of combinatory problems as a reload of nuclear reactors

International Nuclear Information System (INIS)

Machado, Marcelo Dornellas

1999-04-01

Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for function optimization. In this work, a new learning mode, to be used by the Population-Based Incremental Learning (PBIL) algorithm, who combines mechanisms of standard genetic algorithm with simple competitive learning, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process know as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors. This problem can be interpreted as search of a load pattern to be used in the nucleus of the reactor in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)

Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Mengjun Ming

2017-05-01

Full Text Available Due to the scarcity of conventional energy resources and the greenhouse effect, renewable energies have gained more attention. This paper proposes methods for multi-objective optimal design of hybrid renewable energy system (HRES in both isolated-island and grid-connected modes. In each mode, the optimal design aims to find suitable configurations of photovoltaic (PV panels, wind turbines, batteries and diesel generators in HRES such that the system cost and the fuel emission are minimized, and the system reliability/renewable ability (corresponding to different modes is maximized. To effectively solve this multi-objective problem (MOP, the multi-objective evolutionary algorithm based on decomposition (MOEA/D using localized penalty-based boundary intersection (LPBI method is proposed. The algorithm denoted as MOEA/D-LPBI is demonstrated to outperform its competitors on the HRES model as well as a set of benchmarks. Moreover, it effectively obtains a good approximation of Pareto optimal HRES configurations. By further considering a decision maker’s preference, the most satisfied configuration of the HRES can be identified.

Design and selection of load control strategies using a multiple objective model and evolutionary algorithms

International Nuclear Information System (INIS)

Gomes, Alvaro; Antunes, Carlos Henggeler; Martins, Antonio Gomes

2005-01-01

This paper aims at presenting a multiple objective model to evaluate the attractiveness of the use of demand resources (through load management control actions) by different stakeholders and in diverse structure scenarios in electricity systems. For the sake of model flexibility, the multiple (and conflicting) objective functions of technical, economical and quality of service nature are able to capture distinct market scenarios and operating entities that may be interested in promoting load management activities. The computation of compromise solutions is made by resorting to evolutionary algorithms, which are well suited to tackle multiobjective problems of combinatorial nature herein involving the identification and selection of control actions to be applied to groups of loads. (Author)

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems.

Science.gov (United States)

Wang, Handing; Jin, Yaochu; Doherty, John

2017-09-01

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

47 CFR 90.691 - Emission mask requirements for EA-based systems.

Science.gov (United States)

2010-10-01

... 47 Telecommunication 5 2010-10-01 2010-10-01 false Emission mask requirements for EA-based systems. 90.691 Section 90.691 Telecommunication FEDERAL COMMUNICATIONS COMMISSION (CONTINUED) SAFETY AND... of Ea-Based Smr Systems in the 809-824/851-869 Mhz Band § 90.691 Emission mask requirements for EA...

Optimization of a Conical Corrugated Antenna Using Multiobjective Heuristics for Radio-Astronomy Applications

Directory of Open Access Journals (Sweden)

S. López-Ruiz

2016-01-01

Full Text Available This paper presents the design of a tree sections corrugated horn antenna with a modified linear profile, using NURBS, suitable for radio-astronomy applications. The operating band ranges from 4.5 to 8.8 GHz. The aperture efficiency is higher than 84% and the return losses are greater than 20 dB in the whole bandwidth. The antenna optimization has been carried out with multiobjective versions of an evolutionary algorithm (EA and a particle swarm optimization (PSO algorithm. We show that both techniques provide good antenna design, but the experience carried out shows that the results of the evolutionary algorithm outperform the particle swarm results.

Combining Environment-Driven Adaptation and Task-Driven Optimisation in Evolutionary Robotics

NARCIS (Netherlands)

Haasdijk, E.W.; Bredeche, Nicolas; Eiben, A.E.

2014-01-01

Embodied evolutionary robotics is a sub-field of evolutionary robotics that employs evolutionary algorithms on the robotic hardware itself, during the operational period, i.e., in an on-line fashion. This enables robotic systems that continuously adapt, and are therefore capable of (re-)adjusting

Differential evolution optimization combined with chaotic sequences for image contrast enhancement

Energy Technology Data Exchange (ETDEWEB)

Santos Coelho, Leandro dos [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: leandro.coelho@pucpr.br; Sauer, Joao Guilherme [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: joao.sauer@gmail.com; Rudek, Marcelo [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: marcelo.rudek@pucpr.br

2009-10-15

Evolutionary Algorithms (EAs) are stochastic and robust meta-heuristics of evolutionary computation field useful to solve optimization problems in image processing applications. Recently, as special mechanism to avoid being trapped in local minimum, the ergodicity property of chaotic sequences has been used in various designs of EAs. Three differential evolution approaches based on chaotic sequences using logistic equation for image enhancement process are proposed in this paper. Differential evolution is a simple yet powerful evolutionary optimization algorithm that has been successfully used in solving continuous problems. The proposed chaotic differential evolution schemes have fast convergence rate but also maintain the diversity of the population so as to escape from local optima. In this paper, the image contrast enhancement is approached as a constrained nonlinear optimization problem. The objective of the proposed chaotic differential evolution schemes is to maximize the fitness criterion in order to enhance the contrast and detail in the image by adapting the parameters using a contrast enhancement technique. The proposed chaotic differential evolution schemes are compared with classical differential evolution to two testing images. Simulation results on three images show that the application of chaotic sequences instead of random sequences is a possible strategy to improve the performance of classical differential evolution optimization algorithm.

Noise properties and cascadability of SOA-EA regenerators

DEFF Research Database (Denmark)

Öhman, Filip; Bischoff, Svend; Tromborg, Bjarne

2002-01-01

We suggest and analyse a new device containing concatenated pairs of semiconductor optical amplifiers (SOAs) and electroabsorption modulators (EAs). The device has regenerative properties and improves the cascadability of optical fibre links.......We suggest and analyse a new device containing concatenated pairs of semiconductor optical amplifiers (SOAs) and electroabsorption modulators (EAs). The device has regenerative properties and improves the cascadability of optical fibre links....

D1.1 Stakeholders Needs Regarding EA

DEFF Research Database (Denmark)

Ryberg, Thomas; Eleftheriou, Paraskevi; Maglavera, Stavroula

2009-01-01

The objective of this deliverable is to identify and document the competences (skills and knowledge) that employees of both private and public sector organisations need to acquire in order to be capable of developing an EA as well as the competences that university students need to acquire in a u...... competences comprising professional skills, personal skills and knowledge. Finally, three detailed lists of EA related competences are presented for the three target groups of the project i.e. private sector employees, public sector employees and university students....

Posterior Tracheopexy for Severe Tracheomalacia Associated with Esophageal Atresia (EA: Primary Treatment at the Time of Initial EA Repair versus Secondary Treatment

Directory of Open Access Journals (Sweden)

Hester F. Shieh

2018-01-01

Full Text Available PurposeWe review outcomes of posterior tracheopexy for tracheomalacia in esophageal atresia (EA patients, comparing primary treatment at the time of initial EA repair versus secondary treatment.MethodsAll EA patients who underwent posterior tracheopexy from October 2012 to September 2016 were retrospectively reviewed. Clinical symptoms, tracheomalacia scores, and persistent airway intrusion were collected. Indication for posterior tracheopexy was the presence of clinical symptoms, in combination with severe tracheomalacia as identified on bronchoscopic evaluation, typically defined as coaptation in one or more regions of the trachea. Secondary cases were usually those with chronic respiratory symptoms who underwent bronchoscopic evaluation, whereas primary cases were those found to have severe tracheomalacia on routine preoperative dynamic tracheobronchoscopy at the time of initial EA repair.ResultsA total of 118 patients underwent posterior tracheopexy: 18 (15% primary versus 100 (85% secondary cases. Median (interquartile range age was 2 months (1–4 months for primary (22% type C and 18 months (8–40 months for secondary (87% type C cases (p < 0.001. There were statistically significant improvements in most clinical symptoms postoperatively for primary and secondary cases, with no significant differences in any postoperative symptoms between the two groups (p > 0.1. Total tracheomalacia scores improved significantly in primary (p = 0.013 and secondary (p < 0.001 cases. Multivariable Cox regression analysis indicated no differences in persistent airway intrusion requiring reoperation between primary and secondary tracheopexy adjusting for imbalances in age and EA type (p = 0.67.ConclusionPosterior tracheopexy is effective in treating severe tracheomalacia with significant improvements in clinical symptoms and degree of airway collapse on bronchoscopy. With no significant differences in outcomes between primary and

Evolutionary computation for reinforcement learning

NARCIS (Netherlands)

Whiteson, S.; Wiering, M.; van Otterlo, M.

2012-01-01

Algorithms for evolutionary computation, which simulate the process of natural selection to solve optimization problems, are an effective tool for discovering high-performing reinforcement-learning policies. Because they can automatically find good representations, handle continuous action spaces,

Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction

Directory of Open Access Journals (Sweden)

Jingling Zhang

2012-01-01

Full Text Available The multiobjective vehicle routing problem considering customer satisfaction (MVRPCS involves the distribution of orders from several depots to a set of customers over a time window. This paper presents a self-adaptive grid multi-objective quantum evolutionary algorithm (MOQEA for the MVRPCS, which takes into account customer satisfaction as well as travel costs. The degree of customer satisfaction is represented by proposing an improved fuzzy due-time window, and the optimization problem is modeled as a mixed integer linear program. In the MOQEA, nondominated solution set is constructed by the Challenge Cup rules. Moreover, an adaptive grid is designed to achieve the diversity of solution sets; that is, the number of grids in each generation is not fixed but is automatically adjusted based on the distribution of the current generation of nondominated solution set. In the study, the MOQEA is evaluated by applying it to classical benchmark problems. Results of numerical simulation and comparison show that the established model is valid and the MOQEA is effective for MVRPCS.

EA follow-up in the Ghanaian mining sector: Challenges and opportunities

International Nuclear Information System (INIS)

Appiah-Opoku, Seth; Bryan, Hobson C.

2013-01-01

Environmental assessment (EA) follow-up provides a means for monitoring and evaluating the implementation of environmental impact studies. It is integral to the success or failure of a project or program. In spite of its importance, very little attention is given to the need for follow-up programs in most jurisdictions in Africa. Using a case study in the Ghanaian mining sector, this paper explores the challenges and opportunities within the country's EA process for an effective follow-up program. The paper is based on informal interviews, content analysis of relevant publications, official EA documents, and internet searches. The authors suggest a standard EA follow-up program to be formalized as an integral part of Ghana's environmental assessment policy. They also propose a follow-up process that harnesses existing opportunities within the country's EA system. This approach can be replicated in other African countries

Computing the Quartet Distance Between Evolutionary Trees in Time O(n log n)

DEFF Research Database (Denmark)

Brodal, Gerth Sølfting; Fagerberg, Rolf; Pedersen, Christian Nørgaard Storm

2003-01-01

Evolutionary trees describing the relationship for a set of species are central in evolutionary biology, and quantifying differences between evolutionary trees is therefore an important task. The quartet distance is a distance measure between trees previously proposed by Estabrook, McMorris, and ...... unrooted evolutionary trees of n species, where all internal nodes have degree three, in time O(n log n. The previous best algorithm for the problem uses time O(n 2).......Evolutionary trees describing the relationship for a set of species are central in evolutionary biology, and quantifying differences between evolutionary trees is therefore an important task. The quartet distance is a distance measure between trees previously proposed by Estabrook, Mc......Morris, and Meacham. The quartet distance between two unrooted evolutionary trees is the number of quartet topology differences between the two trees, where a quartet topology is the topological subtree induced by four species. In this paper we present an algorithm for computing the quartet distance between two...

OneVA EA Vision and Strategy

Data.gov (United States)

Department of Veterans Affairs — The outcomes/goals supported by effective use of an EA are: Improved Service Delivery, Functional Integration, Resource Optimization and Authoritative Reference. VA...

Robustness of Populations in Stochastic Environments

DEFF Research Database (Denmark)

Gießen, Christian; Kötzing, Timo

2016-01-01

We consider stochastic versions of OneMax and LeadingOnes and analyze the performance of evolutionary algorithms with and without populations on these problems. It is known that the (1+1) EA on OneMax performs well in the presence of very small noise, but poorly for higher noise levels. We extend...... the abilities of the (1+1) EA. Larger population sizes are even more beneficial; we consider both parent and offspring populations. In this sense, populations are robust in these stochastic settings....

An implementation of differential evolution algorithm for inversion of geoelectrical data

Science.gov (United States)

Balkaya, Çağlayan

2013-11-01

Differential evolution (DE), a population-based evolutionary algorithm (EA) has been implemented to invert self-potential (SP) and vertical electrical sounding (VES) data sets. The algorithm uses three operators including mutation, crossover and selection similar to genetic algorithm (GA). Mutation is the most important operator for the success of DE. Three commonly used mutation strategies including DE/best/1 (strategy 1), DE/rand/1 (strategy 2) and DE/rand-to-best/1 (strategy 3) were applied together with a binomial type crossover. Evolution cycle of DE was realized without boundary constraints. For the test studies performed with SP data, in addition to both noise-free and noisy synthetic data sets two field data sets observed over the sulfide ore body in the Malachite mine (Colorado) and over the ore bodies in the Neem-Ka Thana cooper belt (India) were considered. VES test studies were carried out using synthetically produced resistivity data representing a three-layered earth model and a field data set example from Gökçeada (Turkey), which displays a seawater infiltration problem. Mutation strategies mentioned above were also extensively tested on both synthetic and field data sets in consideration. Of these, strategy 1 was found to be the most effective strategy for the parameter estimation by providing less computational cost together with a good accuracy. The solutions obtained by DE for the synthetic cases of SP were quite consistent with particle swarm optimization (PSO) which is a more widely used population-based optimization algorithm than DE in geophysics. Estimated parameters of SP and VES data were also compared with those obtained from Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing (SA) without cooling to clarify uncertainties in the solutions. Comparison to the M-H algorithm shows that DE performs a fast approximate posterior sampling for the case of low-dimensional inverse geophysical problems.

The Tunka-133 EAS Cherenkov light array: Status of 2011

International Nuclear Information System (INIS)

Berezhnev, S.F.; Besson, D.; Budnev, N.M.; Chiavassa, A.; Chvalaev, O.A.; Gress, O.A.; Dyachok, A.N.; Epimakhov, S.N.; Haungs, A.; Karpov, N.I.; Kalmykov, N.N.; Konstantinov, E.N.; Korobchenko, A.V.; Korosteleva, E.E.; Kozhin, V.A.; Kuzmichev, L.A.; Lubsandorzhiev, B.K.; Lubsandorzhiev, N.B.; Mirgazov, R.R.; Panasyuk, M.I.

2012-01-01

A new EAS Cherenkov light array, Tunka-133, with ∼1km 2 geometrical area has been installed at the Tunka Valley (50 km from Lake Baikal) in 2009. The array permits a detailed study of cosmic ray energy spectrum and mass composition in the energy range 10 16 –10 18 eV with a uniform method. We describe the array construction, DAQ and methods of the array calibration. The method of energy reconstruction and absolute calibration of measurements are discussed. The analysis of spatial and time structure of EAS Cherenkov light allows to estimate the depth of the EAS maximum X max . The results on the all particles energy spectrum and the mean depth of the EAS maximum X max vs. primary energy derived from the data of two winter seasons (2009–2011) are presented. Preliminary results of joint operation of the Cherenkov array with antennas for the detection of EAS radio signals are shown. Plans for future upgrades – deployment of remote clusters, radioantennas and a scintillator detector network and a prototype of the HiSCORE gamma-telescope – are discussed.

Robustness of Populations in Stochastic Environments

DEFF Research Database (Denmark)

Gießen, Christian; Kötzing, Timo

2014-01-01

these results to LeadingOnes and to many different noise models, showing how the application of drift theory can significantly simplify and generalize previous analyses. Most surprisingly, even small populations (of size _(log n)) can make evolutionary algorithms perform well for high noise levels, well outside......We consider stochastic versions of OneMax and Leading-Ones and analyze the performance of evolutionary algorithms with and without populations on these problems. It is known that the (1+1) EA on OneMax performs well in the presence of very small noise, but poorly for higher noise levels. We extend...

EAS 2.948 Listed Test Firms

Data.gov (United States)

Federal Communications Commission — EAS (Equipment Authorization System). The following firms have submitted the information required by Section 2.948 of the FCC Rules for measuring devices subject to...

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...

How Do Severe Constraints Affect the Search Ability of Multiobjective Evolutionary Algorithms in Water Resources?

Science.gov (United States)

Clarkin, T. J.; Kasprzyk, J. R.; Raseman, W. J.; Herman, J. D.

2015-12-01

This study contributes a diagnostic assessment of multiobjective evolutionary algorithm (MOEA) search on a set of water resources problem formulations with different configurations of constraints. Unlike constraints in classical optimization modeling, constraints within MOEA simulation-optimization represent limits on acceptable performance that delineate whether solutions within the search problem are feasible. Constraints are relevant because of the emergent pressures on water resources systems: increasing public awareness of their sustainability, coupled with regulatory pressures on water management agencies. In this study, we test several state-of-the-art MOEAs that utilize restricted tournament selection for constraint handling on varying configurations of water resources planning problems. For example, a problem that has no constraints on performance levels will be compared with a problem with several severe constraints, and a problem with constraints that have less severe values on the constraint thresholds. One such problem, Lower Rio Grande Valley (LRGV) portfolio planning, has been solved with a suite of constraints that ensure high reliability, low cost variability, and acceptable performance in a single year severe drought. But to date, it is unclear whether or not the constraints are negatively affecting MOEAs' ability to solve the problem effectively. Two categories of results are explored. The first category uses control maps of algorithm performance to determine if the algorithm's performance is sensitive to user-defined parameters. The second category uses run-time performance metrics to determine the time required for the algorithm to reach sufficient levels of convergence and diversity on the solution sets. Our work exploring the effect of constraints will better enable practitioners to define MOEA problem formulations for real-world systems, especially when stakeholders are concerned with achieving fixed levels of performance according to one or

An Evolutionary Perspective of the Relationship Between Corporate Strategy and Performance, Through the Use of Artificial Neural Networks and Genetic AlgorithmsHttp://Dx.Doi.Org/10.5585/Riae.V9i3.1689

Directory of Open Access Journals (Sweden)

Alexandre Teixeira Dias

2011-01-01

Full Text Available This study aims to contribute to the understanding of the relationship between Corporate Strategy and Performance, from the perspective of the Evolutionary Theory. As methods of data processing, obtained in secondary databases, we used artificial neural networks and genetic algorithms. The results of processing neural networks and genetic algorithms demonstrate the importance of corporate strategies in determining performance. The evolutionary perspective emphasizes the importance of investing in operations as a factor influencing the adequacy of the organization, in order to achieve an improved performance, in addition to establishing relationships with other organizations, through members of the board.

Evolutionary design optimization of traffic signals applied to Quito city.

Science.gov (United States)

Armas, Rolando; Aguirre, Hernán; Daolio, Fabio; Tanaka, Kiyoshi

2017-01-01

This work applies evolutionary computation and machine learning methods to study the transportation system of Quito from a design optimization perspective. It couples an evolutionary algorithm with a microscopic transport simulator and uses the outcome of the optimization process to deepen our understanding of the problem and gain knowledge about the system. The work focuses on the optimization of a large number of traffic lights deployed on a wide area of the city and studies their impact on travel time, emissions and fuel consumption. An evolutionary algorithm with specialized mutation operators is proposed to search effectively in large decision spaces, evolving small populations for a short number of generations. The effects of the operators combined with a varying mutation schedule are studied, and an analysis of the parameters of the algorithm is also included. In addition, hierarchical clustering is performed on the best solutions found in several runs of the algorithm. An analysis of signal clusters and their geolocation, estimation of fuel consumption, spatial analysis of emissions, and an analysis of signal coordination provide an overall picture of the systemic effects of the optimization process.

Evolutionary Hybrid Particle Swarm Optimization Algorithm for Solving NP-Hard No-Wait Flow Shop Scheduling Problems

Directory of Open Access Journals (Sweden)

Laxmi A. Bewoor

2017-10-01

Full Text Available The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard’s benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.

Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm

KAUST Repository

Wong, Ka Chun

2011-02-05

Protein-DNA bindings are essential activities. Understanding them forms the basis for further deciphering of biological and genetic systems. In particular, the protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) play a central role in gene transcription. Comprehensive TF-TFBS binding sequence pairs have been found in a recent study. However, they are in one-to-one mappings which cannot fully reflect the many-to-many mappings within the bindings. An evolutionary algorithm is proposed to learn generalized representations (many-to-many mappings) from the TF-TFBS binding sequence pairs (one-to-one mappings). The generalized pairs are shown to be more meaningful than the original TF-TFBS binding sequence pairs. Some representative examples have been analyzed in this study. In particular, it shows that the TF-TFBS binding sequence pairs are not presumably in one-to-one mappings. They can also exhibit many-to-many mappings. The proposed method can help us extract such many-to-many information from the one-to-one TF-TFBS binding sequence pairs found in the previous study, providing further knowledge in understanding the bindings between TFs and TFBSs. © 2011 Springer-Verlag.

Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm

KAUST Repository

Wong, Ka Chun; Peng, Chengbin; Wong, Manhon; Leung, Kwongsak

2011-01-01

Protein-DNA bindings are essential activities. Understanding them forms the basis for further deciphering of biological and genetic systems. In particular, the protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) play a central role in gene transcription. Comprehensive TF-TFBS binding sequence pairs have been found in a recent study. However, they are in one-to-one mappings which cannot fully reflect the many-to-many mappings within the bindings. An evolutionary algorithm is proposed to learn generalized representations (many-to-many mappings) from the TF-TFBS binding sequence pairs (one-to-one mappings). The generalized pairs are shown to be more meaningful than the original TF-TFBS binding sequence pairs. Some representative examples have been analyzed in this study. In particular, it shows that the TF-TFBS binding sequence pairs are not presumably in one-to-one mappings. They can also exhibit many-to-many mappings. The proposed method can help us extract such many-to-many information from the one-to-one TF-TFBS binding sequence pairs found in the previous study, providing further knowledge in understanding the bindings between TFs and TFBSs. © 2011 Springer-Verlag.

75 FR 33799 - EasTrans, LLC; Notice of Baseline Filing

Science.gov (United States)

2010-06-15

... DEPARTMENT OF ENERGY Federal Energy Regulatory Commission [Docket No. PR10-30-000] EasTrans, LLC; Notice of Baseline Filing June 8, 2010. Take notice that on June 4, 2010, EasTrans, LLC submitted a baseline filing of its Statement of Operating Conditions for services provided under section 311 of the...

A new paradigm for Environmental Assessment (EA) in Korea

International Nuclear Information System (INIS)

Song, Young-Il; Glasson, John

2010-01-01

Over the last 30 years, Environmental Impact Assessment (EIA) in Korea has played an important role in decision-making processes particularly for environmentally sensitive projects. However, the EIA system alone has sometimes not been effective enough to ensure the successful resolution of environmental concerns. In order to compensate for the limitations of the EIA system, a new assessment system called Prior Environmental Review System (PERS), which is relevant to Strategic Environmental Assessment (SEA) in some aspects, was introduced in 1993. PERS aims to balance development and preservation by identifying possible environmental impacts of some administrative plans mainly related to development projects in the early stages of planning. However, PERS still appeared to have some weak points such as a limited range of subjects to be assessed, and weakness of tiering (or vertical integration) from PERS to EIA. Therefore, the necessity for reform of the Korean Environmental Assessment (EA) system, including PERS, was raised. In response, the Korean government sought to establish its policy direction for implementing SEA by enhancing the objectivity and expertise of PERS. The policy was approved by the National Assembly in May 2005, and went into effect in June 2006. The introduction of SEA, by enhancing PERS, provides a framework for a system of EA from the strategic level, including PPPs, to the project level. Yet, despite such improvements, some managerial and technical problems associated with subsequent EA implementation remain. This paper critically reviews the evolution of the EA system in Korea and suggests essential improvements for the current EA system based on experiences of implementation of both EIA and SEA since June 2006, in the context of international good practice.

(MBO) algorithm in multi-reservoir system optimisation

African Journals Online (AJOL)

A comparative study of marriage in honey bees optimisation (MBO) algorithm in ... A practical application of the marriage in honey bees optimisation (MBO) ... to those of other evolutionary algorithms, such as the genetic algorithm (GA), ant ...

Fundamentals of natural computing basic concepts, algorithms, and applications

CERN Document Server

de Castro, Leandro Nunes

2006-01-01

Introduction A Small Sample of Ideas The Philosophy of Natural Computing The Three Branches: A Brief Overview When to Use Natural Computing Approaches Conceptualization General Concepts PART I - COMPUTING INSPIRED BY NATURE Evolutionary Computing Problem Solving as a Search Task Hill Climbing and Simulated Annealing Evolutionary Biology Evolutionary Computing The Other Main Evolutionary Algorithms From Evolutionary Biology to Computing Scope of Evolutionary Computing Neurocomputing The Nervous System Artif

A Multipopulation Coevolutionary Strategy for Multiobjective Immune Algorithm

Directory of Open Access Journals (Sweden)

Jiao Shi

2014-01-01

Full Text Available How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems.

Expert Advisor (EA) Evaluation System Using Web-based ELECTRE Method in Foreign Exchange (Forex) Market

Science.gov (United States)

Satibi; Widodo, Catur Edi; Farikhin

2018-02-01

This research aims to optimize forex trading profit automatically using EA but its still keep considering accuracy and drawdown levels. The evaluation system will classify EA performance based on trading market sessions (Sydney, Tokyo, London and New York) to determine the right EA to be used in certain market sessions. This evaluation system is a web-based ELECTRE methods that interact in real-time with EA through web service and are able to present real-time charts performance dashboard using web socket protocol communications. Web applications are programmed using NodeJs. In the testing period, all EAs had been simulated 24 hours in all market sessions for three months, the best EA is valued by its profit, accuracy and drawdown criteria that calculated using web-based ELECTRE method. The ideas of this research are to compare the best EA on testing period with collaboration performances of each best classified EA by market sessions. This research uses three months historical data of EUR/USD as testing period and other 3 months as validation period. As a result, performance of collaboration four best EA classified by market sessions can increase profits percentage consistently in testing and validation periods and keep securing accuracy and drawdown levels.

Expert Advisor (EA Evaluation System Using Web-based ELECTRE Method in Foreign Exchange (Forex Market

Directory of Open Access Journals (Sweden)

Satibi Satibi

2018-01-01

Full Text Available This research aims to optimize forex trading profit automatically using EA but its still keep considering accuracy and drawdown levels. The evaluation system will classify EA performance based on trading market sessions (Sydney, Tokyo, London and New York to determine the right EA to be used in certain market sessions. This evaluation system is a web-based ELECTRE methods that interact in real-time with EA through web service and are able to present real-time charts performance dashboard using web socket protocol communications. Web applications are programmed using NodeJs. In the testing period, all EAs had been simulated 24 hours in all market sessions for three months, the best EA is valued by its profit, accuracy and drawdown criteria that calculated using web-based ELECTRE method. The ideas of this research are to compare the best EA on testing period with collaboration performances of each best classified EA by market sessions. This research uses three months historical data of EUR/USD as testing period and other 3 months as validation period. As a result, performance of collaboration four best EA classified by market sessions can increase profits percentage consistently in testing and validation periods and keep securing accuracy and drawdown levels.

Reactive power and voltage control based on general quantum genetic algorithms

DEFF Research Database (Denmark)

Vlachogiannis, Ioannis (John); Østergaard, Jacob

2009-01-01

This paper presents an improved evolutionary algorithm based on quantum computing for optima l steady-state performance of power systems. However, the proposed general quantum genetic algorithm (GQ-GA) can be applied in various combinatorial optimization problems. In this study the GQ-GA determines...... techniques such as enhanced GA, multi-objective evolutionary algorithm and particle swarm optimization algorithms, as well as the classical primal-dual interior-point optimal power flow algorithm. The comparison demonstrates the ability of the GQ-GA in reaching more optimal solutions....

(1+1) EA on Generalized Dynamic OneMax

DEFF Research Database (Denmark)

Kötzing, Timo; Lissovoi, Andrei; Witt, Carsten

2015-01-01

. We get tight bounds both for the case of bit strings, as well as for the case of more than two values per position. Surprisingly, in the latter setting, the expected quality of the search point maintained by the (1+1) EA does not depend on the number of values per dimension....... using the modern tool of drift analysis. We extend these results to search spaces which allow for more than two values per dimension. Furthermore, we make an anytime analysis as suggested by Jansen and Zarges (2014), analyzing how closely the (1+1) EA can track the dynamically moving optimum over time...

Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

Directory of Open Access Journals (Sweden)

E. Osaba

2014-01-01

Full Text Available Since their first formulation, genetic algorithms (GAs have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test.

Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

Science.gov (United States)

Osaba, E.; Carballedo, R.; Diaz, F.; Onieva, E.; de la Iglesia, I.; Perallos, A.

2014-01-01

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution z-test. PMID:25165731

Ni-MH batteries state-of-charge prediction based on immune evolutionary network

International Nuclear Information System (INIS)

Cheng Bo; Zhou Yanlu; Zhang Jiexin; Wang Junping; Cao Binggang

2009-01-01

Based on clonal selection theory, an improved immune evolutionary strategy is presented. Compared with conventional evolutionary strategy algorithm (CESA) and immune monoclonal strategy algorithm (IMSA), experimental results show that the proposed algorithm is of high efficiency and can effectively prevent premature convergence. A three-layer feed-forward neural network is presented to predict state-of-charge (SOC) of Ni-MH batteries. Initially, partial least square regression (PLSR) is used to select input variables. Then, five variables, battery terminal voltage, voltage derivative, voltage second derivative, discharge current and battery temperature, are selected as the inputs of NN. In order to overcome the weakness of BP algorithm, the new algorithm is adopted to train weights. Finally, under the state of dynamic power cycle, the predicted SOC and the actual SOC are compared to verify the proposed neural network with acceptable accuracy (5%).

进化作曲研究%Research on evolutionary music composer system

Institute of Scientific and Technical Information of China (English)

汪镭; 郑晓妹; 申林

2014-01-01

Algorithmic composition is the most attractive research area in computer music and genetic algorithm-based evolution-ary music composer system has become a hot spot in the algorithmic composition.This paper gives a structure of evolutionary mu-sic composer system,analyzes different goals of music composer systems,and then discusses two types of evolutionary music com-poser system from the aspect of fitness function design.Finally,several instances of evolutionary music composer system are ana-lyzed.%算法作曲是计算机音乐中最具吸引力的研究领域，而基于遗传算法的进化作曲系统已成为算法作曲中的热点。给出了进化作曲系统的结构，分析了系统不同的作曲目标，从适应度函数的设计讨论了两类作曲系统。最后给出了几个作曲系统实例分析。

Impact of conservatism on the Ea process

International Nuclear Information System (INIS)

Pelletier, C.A.

1985-01-01

Although starting out with an admitted negative view of the impact of conservatism on the Ea process, the opposite conclusion is reached. Overall, the impact of the authors conservatism has been positive

Soft computing integrating evolutionary, neural, and fuzzy systems

CERN Document Server

Tettamanzi, Andrea

2001-01-01

Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically. This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as

47 CFR 11.33 - EAS Decoder.

Science.gov (United States)

2010-10-01

..., satellite, public switched telephone network, or any other source that uses the EAS protocol. (2) Valid..., analog radio and television broadcast stations, analog cable systems and wireless cable systems may... program data must be retained even with power removed. (7) Outputs. Decoders shall have the following...

Evolutionary constrained optimization

CERN Document Server

Deb, Kalyanmoy

2015-01-01

This book makes available a self-contained collection of modern research addressing the general constrained optimization problems using evolutionary algorithms. Broadly the topics covered include constraint handling for single and multi-objective optimizations; penalty function based methodology; multi-objective based methodology; new constraint handling mechanism; hybrid methodology; scaling issues in constrained optimization; design of scalable test problems; parameter adaptation in constrained optimization; handling of integer, discrete and mix variables in addition to continuous variables; application of constraint handling techniques to real-world problems; and constrained optimization in dynamic environment. There is also a separate chapter on hybrid optimization, which is gaining lots of popularity nowadays due to its capability of bridging the gap between evolutionary and classical optimization. The material in the book is useful to researchers, novice, and experts alike. The book will also be useful...

Soft tissue freezing process. Identification of the dual-phase lag model parameters using the evolutionary algorithm

Science.gov (United States)

Mochnacki, Bohdan; Majchrzak, Ewa; Paruch, Marek

2018-01-01

In the paper the soft tissue freezing process is considered. The tissue sub-domain is subjected to the action of cylindrical cryoprobe. Thermal processes proceeding in the domain considered are described using the dual-phase lag equation (DPLE) supplemented by the appropriate boundary and initial conditions. DPLE results from the generalization of the Fourier law in which two lag times are introduced (relaxation and thermalization times). The aim of research is the identification of these parameters on the basis of measured cooling curves at the set of points selected from the tissue domain. To solve the problem the evolutionary algorithms are used. The paper contains the mathematical model of the tissue freezing process, the very short information concerning the numerical solution of the basic problem, the description of the inverse problem solution and the results of computations.

3rd International Conference on Harmony Search Algorithm

CERN Document Server

2017-01-01

This book presents state-of-the-art technical contributions based around one of the most successful evolutionary optimization algorithms published to date: Harmony Search. Contributions span from novel technical derivations of this algorithm to applications in the broad fields of civil engineering, energy, transportation & mobility and health, among many others and focus not only on its cross-domain applicability, but also on its core evolutionary operators, including elements inspired from other meta-heuristics. The global scientific community is witnessing an upsurge in groundbreaking, new advances in all areas of computational intelligence, with a particular flurry of research focusing on evolutionary computation and bio-inspired optimization. Observed processes in nature and sociology have provided the basis for innovative algorithmic developments aimed at leveraging the inherent capability to adapt characterized by various animals, including ants, fireflies, wolves and humans. However, it is the beha...

Increased cardiogenesis in P19-GFP teratocarcinoma cells expressing the propeptide IGF-1Ea

Energy Technology Data Exchange (ETDEWEB)

Poudel, Bhawana [Heart Science Centre, National Heart and Lung Institute, Imperial College, London (United Kingdom); Bilbao, Daniel [EMBL, Mouse Biology Unit, Monterotondo (Italy); Sarathchandra, Padmini; Germack, Renee [Heart Science Centre, National Heart and Lung Institute, Imperial College, London (United Kingdom); Rosenthal, Nadia [Heart Science Centre, National Heart and Lung Institute, Imperial College, London (United Kingdom); Australian Regenerative Medicine Institute, Monash University, Melbourne (Australia); Santini, Maria Paola, E-mail: m.santini@imperial.ac.uk [Heart Science Centre, National Heart and Lung Institute, Imperial College, London (United Kingdom)

2011-12-16

Highlights: Black-Right-Pointing-Pointer In this study, we explored the function of IGF-1Ea propeptide in inducing cardiogenesis of stem cells. Black-Right-Pointing-Pointer IGF-1Ea promoted cardiac mesodermal induction in uncommitted cells. Black-Right-Pointing-Pointer Under differentiation condition, IGF-1Ea increased expression of cardiac differentiation markers. Black-Right-Pointing-Pointer Furthermore, it promoted formation of finely organized sarcomeric structure. Black-Right-Pointing-Pointer IGF-1Ea propeptide may be a good candidate to improve production of cardiomyocytes from pluripotent cells. -- Abstract: The mechanism implicated in differentiation of endogenous cardiac stem cells into cardiomyocytes to regenerate the heart tissue upon an insult remains elusive, limiting the therapeutical goals to exogenous cell injection and/or gene therapy. We have shown previously that cardiac specific overexpression of the insulin-like growth factor 1 propeptide IGF-1Ea induces beneficial myocardial repair after infarct. Although the mechanism is still under investigation, the possibility that this propeptide may be involved in promoting stem cell differentiation into the cardiac lineage has yet to be explored. To investigate whether IGF-1Ea promote cardiogenesis, we initially modified P19 embryonal carcinoma cells to express IGF-1Ea. Taking advantage of their cardiomyogenic nature, we analyzed whether overexpression of this propeptide affected cardiac differentiation program. The data herein presented showed for the first time that constitutively overexpressed IGF-1Ea increased cardiogenic differentiation program in both undifferentiated and DMSO-differentiated cells. In details, IGF-1Ea overexpression promoted localization of alpha-actinin in finely organized sarcomeric structure compared to control cells and upregulated the cardiac mesodermal marker NKX-2.5 and the ventricular structural protein MLC2v. Furthermore, activated IGF-1 signaling promoted cardiac

Increased cardiogenesis in P19-GFP teratocarcinoma cells expressing the propeptide IGF-1Ea

International Nuclear Information System (INIS)

Poudel, Bhawana; Bilbao, Daniel; Sarathchandra, Padmini; Germack, Renee; Rosenthal, Nadia; Santini, Maria Paola

2011-01-01

Highlights: ► In this study, we explored the function of IGF-1Ea propeptide in inducing cardiogenesis of stem cells. ► IGF-1Ea promoted cardiac mesodermal induction in uncommitted cells. ► Under differentiation condition, IGF-1Ea increased expression of cardiac differentiation markers. ► Furthermore, it promoted formation of finely organized sarcomeric structure. ► IGF-1Ea propeptide may be a good candidate to improve production of cardiomyocytes from pluripotent cells. -- Abstract: The mechanism implicated in differentiation of endogenous cardiac stem cells into cardiomyocytes to regenerate the heart tissue upon an insult remains elusive, limiting the therapeutical goals to exogenous cell injection and/or gene therapy. We have shown previously that cardiac specific overexpression of the insulin-like growth factor 1 propeptide IGF-1Ea induces beneficial myocardial repair after infarct. Although the mechanism is still under investigation, the possibility that this propeptide may be involved in promoting stem cell differentiation into the cardiac lineage has yet to be explored. To investigate whether IGF-1Ea promote cardiogenesis, we initially modified P19 embryonal carcinoma cells to express IGF-1Ea. Taking advantage of their cardiomyogenic nature, we analyzed whether overexpression of this propeptide affected cardiac differentiation program. The data herein presented showed for the first time that constitutively overexpressed IGF-1Ea increased cardiogenic differentiation program in both undifferentiated and DMSO-differentiated cells. In details, IGF-1Ea overexpression promoted localization of alpha-actinin in finely organized sarcomeric structure compared to control cells and upregulated the cardiac mesodermal marker NKX-2.5 and the ventricular structural protein MLC2v. Furthermore, activated IGF-1 signaling promoted cardiac mesodermal induction in undifferentiated cells independently of cell proliferation. This analysis suggests that IGF-1Ea may be a

A brief introduction to continuous evolutionary optimization

CERN Document Server

Kramer, Oliver

2014-01-01

Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel ...

47 CFR 11.32 - EAS Encoder.

Science.gov (United States)

2010-10-01

... Telecommunication FEDERAL COMMUNICATIONS COMMISSION GENERAL EMERGENCY ALERT SYSTEM (EAS) Equipment Requirements § 11... operation. (vi) Indicator Display. The encoder shall be provided with a visual and/or aural indicator which... to +50 degrees C and a range of relative humidity of up to 95%. (c) Primary Supply Voltage Variation...

Digitaalisen markkinointiviestinnän mahdollisuudet : Case Kelloliike EA Lahti Oy

OpenAIRE

Harmaala, Tiina

2011-01-01

TIIVISTELMÄ Harmaala, Tiina. 2011. Digitaalisen markkinointiviestinnän mahdollisuudet – Case Kelloliike EA Lahti Oy. Opinnäytetyö. Kemi-Tornion ammattikorkeakoulu. Kaupan ja kulttuurin toimiala. Sivuja 62. Opinnäytetyön tavoitteena on Kelloliike EA Lahti Oy:n digitaalisen markkinointiviestinnän suunnittelu. Tutkimuksessa tarkastelen digitaalista markkinointiviestintää integroidun markkinointiviestinnän näkökulmasta. Tutkimuksen tarkoituksena on selvittää toimeksiantajalle sen markkino...

An adaptive ES with a ranking based constraint handling strategy

Directory of Open Access Journals (Sweden)

Kusakci Ali Osman

2014-01-01

Full Text Available To solve a constrained optimization problem, equality constraints can be used to eliminate a problem variable. If it is not feasible, the relations imposed implicitly by the constraints can still be exploited. Most conventional constraint handling methods in Evolutionary Algorithms (EAs do not consider the correlations between problem variables imposed by the constraints. This paper relies on the idea that a proper search operator, which captures mentioned implicit correlations, can improve performance of evolutionary constrained optimization algorithms. To realize this, an Evolution Strategy (ES along with a simplified Covariance Matrix Adaptation (CMA based mutation operator is used with a ranking based constraint-handling method. The proposed algorithm is tested on 13 benchmark problems as well as on a real life design problem. The outperformance of the algorithm is significant when compared with conventional ES-based methods.

A proposal of multi-objective function for submarine rigid pipelines route optimization via evolutionary algorithms

Energy Technology Data Exchange (ETDEWEB)

Fernandes, D.H.; Medeiros, A.R. [Subsea7, Niteroi, RJ (Brazil); Jacob, B.P.; Lima, B.S.L.P.; Albrecht, C.H. [Universidade Federaldo Rio de Janeiro (COPPE/UFRJ), RJ (Brazil). Coordenacao de Programas de Pos-graduacao em Engenharia

2009-07-01

This work presents studies regarding the determination of optimal pipeline routes for offshore applications. The assembly of an objective function is presented; this function can be later associated with Evolutionary Algorithm to implement a computational tool for the automatic determination of the most advantageous pipeline route for a given scenario. This tool may reduce computational overheads, avoid mistakes with route interpretation, and minimize costs with respect to submarine pipeline design and installation. The following aspects can be considered in the assembly of the objective function: Geophysical and geotechnical data obtained from the bathymetry and sonography; the influence of the installation method, total pipeline length and number of free spans to be mitigated along the routes as well as vessel time for both cases. Case studies are presented to illustrate the use of the proposed objective function, including a sensitivity analysis intended to identify the relative influence of selected parameters in the evaluation of different routes. (author)

Evolutionary optimization of production materials workflow processes

DEFF Research Database (Denmark)

Herbert, Luke Thomas; Hansen, Zaza Nadja Lee; Jacobsen, Peter

2014-01-01

We present an evolutionary optimisation technique for stochastic production processes, which is able to find improved production materials workflow processes with respect to arbitrary combinations of numerical quantities associated with the production process. Working from a core fragment...... of the BPMN language, we employ an evolutionary algorithm where stochastic model checking is used as a fitness function to determine the degree of improvement of candidate processes derived from the original process through mutation and cross-over operations. We illustrate this technique using a case study...

Improved differential evolution algorithms for handling economic dispatch optimization with generator constraints

International Nuclear Information System (INIS)

Coelho, Leandro dos Santos; Mariani, Viviana Cocco

2007-01-01

Global optimization based on evolutionary algorithms can be used as the important component for many engineering optimization problems. Evolutionary algorithms have yielded promising results for solving nonlinear, non-differentiable and multi-modal optimization problems in the power systems area. Differential evolution (DE) is a simple and efficient evolutionary algorithm for function optimization over continuous spaces. It has reportedly outperformed search heuristics when tested over both benchmark and real world problems. This paper proposes improved DE algorithms for solving economic load dispatch problems that take into account nonlinear generator features such as ramp rate limits and prohibited operating zones in the power system operation. The DE algorithms and its variants are validated for two test systems consisting of 6 and 15 thermal units. Various DE approaches outperforms other state of the art algorithms reported in the literature in solving load dispatch problems with generator constraints

A controllable sensor management algorithm capable of learning

Science.gov (United States)

Osadciw, Lisa A.; Veeramacheneni, Kalyan K.

2005-03-01

Sensor management technology progress is challenged by the geographic space it spans, the heterogeneity of the sensors, and the real-time timeframes within which plans controlling the assets are executed. This paper presents a new sensor management paradigm and demonstrates its application in a sensor management algorithm designed for a biometric access control system. This approach consists of an artificial intelligence (AI) algorithm focused on uncertainty measures, which makes the high level decisions to reduce uncertainties and interfaces with the user, integrated cohesively with a bottom up evolutionary algorithm, which optimizes the sensor network"s operation as determined by the AI algorithm. The sensor management algorithm presented is composed of a Bayesian network, the AI algorithm component, and a swarm optimization algorithm, the evolutionary algorithm. Thus, the algorithm can change its own performance goals in real-time and will modify its own decisions based on observed measures within the sensor network. The definition of the measures as well as the Bayesian network determine the robustness of the algorithm and its utility in reacting dynamically to changes in the global system.

E+A galaxies in the SDSS. Stellar population and morphology

Science.gov (United States)

Leiva, R.; Galaz, G.

2014-10-01

Galaxies with E+A spectrum have deep Balmer absorption and no H_{α} and [OII] emission. This suggest recent star formation and the lack of ongoing star formation. With an E+A sample from the SDSS DR 7 (Aihara et al. 2011) we study the morphology with Galaxy Zoo 1 data and the star formation history fitting models from Bruzual & Charlot (2003). We found an underpopulation of spiral and disk like galaxies and an overpopulation of interacting galaxies, the last seems consistent with the scenario where, at low z, the interaction mechanism is responsible for at least part of the E+A galaxies. The star formation history (SFH) fits most of the spectra indicating an increased star formation around 2 Gyr in the past. Additional parameters like dust internal extinction need to be included to improve the fitting.

Learning Intelligent Genetic Algorithms Using Japanese Nonograms

Science.gov (United States)

Tsai, Jinn-Tsong; Chou, Ping-Yi; Fang, Jia-Cen

2012-01-01

An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and…

Cash Management Policies By Evolutionary Models: A Comparison Using The MILLER-ORR Model

Directory of Open Access Journals (Sweden)

Marcelo Botelho da Costa Moraes

2013-10-01

Full Text Available This work aims to apply genetic algorithms (GA and particle swarm optimization (PSO to managing cash balance, comparing performance results between computational models and the Miller-Orr model. Thus, the paper proposes the application of computational evolutionary models to minimize the total cost of cash balance maintenance, obtaining the parameters for a cash management policy, using assumptions presented in the literature, considering the cost of maintenance and opportunity for cost of cash. For such, we developed computational experiments from cash flows simulated to implement the algorithms. For a control purpose, an algorithm has been developed that uses the Miller-Orr model defining the lower bound parameter, which is not obtained by the original model. The results indicate that evolutionary algorithms present better results than the Miller-Orr model, with prevalence for PSO algorithm in results.

Confronting Decision Cliffs: Diagnostic Assessment of Multi-Objective Evolutionary Algorithms' Performance for Addressing Uncertain Environmental Thresholds

Science.gov (United States)

Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.

2014-12-01

As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a

A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm.

Science.gov (United States)

Zhang, Aizhu; Sun, Genyun; Ren, Jinchang; Li, Xiaodong; Wang, Zhenjie; Jia, Xiuping

2018-01-01

Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named , which stores those superior agents after fitness sorting in each iteration. Since the global property of remains unchanged in the whole evolutionary process, GSA emphasizes exploitation over exploration and suffers from rapid loss of diversity and premature convergence. To address these problems, in this paper, we propose a dynamic neighborhood learning (DNL) strategy to replace the model and thereby present a DNL-based GSA (DNLGSA). The method incorporates the local and global neighborhood topologies for enhancing the exploration and obtaining adaptive balance between exploration and exploitation. The local neighborhoods are dynamically formed based on evolutionary states. To delineate the evolutionary states, two convergence criteria named limit value and population diversity, are introduced. Moreover, a mutation operator is designed for escaping from the local optima on the basis of evolutionary states. The proposed algorithm was evaluated on 27 benchmark problems with different characteristic and various difficulties. The results reveal that DNLGSA exhibits competitive performances when compared with a variety of state-of-the-art M-HS algorithms. Moreover, the incorporation of local neighborhood topology reduces the numbers of calculations of gravitational force and thus alleviates the high computational cost of GSA.

Evolutionary engineering for industrial microbiology.

Science.gov (United States)

Vanee, Niti; Fisher, Adam B; Fong, Stephen S

2012-01-01

Superficially, evolutionary engineering is a paradoxical field that balances competing interests. In natural settings, evolution iteratively selects and enriches subpopulations that are best adapted to a particular ecological niche using random processes such as genetic mutation. In engineering desired approaches utilize rational prospective design to address targeted problems. When considering details of evolutionary and engineering processes, more commonality can be found. Engineering relies on detailed knowledge of the problem parameters and design properties in order to predict design outcomes that would be an optimized solution. When detailed knowledge of a system is lacking, engineers often employ algorithmic search strategies to identify empirical solutions. Evolution epitomizes this iterative optimization by continuously diversifying design options from a parental design, and then selecting the progeny designs that represent satisfactory solutions. In this chapter, the technique of applying the natural principles of evolution to engineer microbes for industrial applications is discussed to highlight the challenges and principles of evolutionary engineering.

Face Alignment Using Boosting and Evolutionary Search

NARCIS (Netherlands)

Zhang, Hua; Liu, Duanduan; Poel, Mannes; Nijholt, Antinus; Zha, H.; Taniguchi, R.-I.; Maybank, S.

2010-01-01

In this paper, we present a face alignment approach using granular features, boosting, and an evolutionary search algorithm. Active Appearance Models (AAM) integrate a shape-texture-combined morphable face model into an efficient fitting strategy, then Boosting Appearance Models (BAM) consider the

Environmental Agents Service (EAS) Registry System of Records

Data.gov (United States)

Department of Veterans Affairs — The Environmental Agent Service (EAS) Registries is the information system encompassing the Ionizing Radiation Registry (IRR), the Agent Orange Registry (AOR), and...

7 CFR 1955.136 - Environmental Assessment (EA) and Environmental Impact Statement (EIS).

Science.gov (United States)

2010-01-01

... 7 Agriculture 14 2010-01-01 2009-01-01 true Environmental Assessment (EA) and Environmental Impact... Disposal of Inventory Property General § 1955.136 Environmental Assessment (EA) and Environmental Impact Statement (EIS). (a) Prior to a final decision on some disposal actions, an environmental assessment must be...

An Efficient Evolutionary Based Method For Image Segmentation

OpenAIRE

Aslanzadeh, Roohollah; Qazanfari, Kazem; Rahmati, Mohammad

2017-01-01

The goal of this paper is to present a new efficient image segmentation method based on evolutionary computation which is a model inspired from human behavior. Based on this model, a four layer process for image segmentation is proposed using the split/merge approach. In the first layer, an image is split into numerous regions using the watershed algorithm. In the second layer, a co-evolutionary process is applied to form centers of finals segments by merging similar primary regions. In the t...

Aggregate meta-models for evolutionary multiobjective and many-objective optimization

Czech Academy of Sciences Publication Activity Database

Pilát, Martin; Neruda, Roman

Roč. 116, 20 September (2013), s. 392-402 ISSN 0925-2312 R&D Projects: GA ČR GAP202/11/1368 Institutional support: RVO:67985807 Keywords : evolutionary algorithms * multiobjective optimization * many-objective optimization * surrogate models * meta-models * memetic algorithm Subject RIV: IN - Informatics, Computer Science Impact factor: 2.005, year: 2013

Self-Adaptive Operator Scheduling using the Religion-Based EA

DEFF Research Database (Denmark)

Thomsen, Rene; Krink, Thiemo

2002-01-01

of their application is determined by a constant parameter, such as a fixed mutation rate. However, recent studies have shown that the optimal usage of a variation operator changes during the EA run. In this study, we combined the idea of self-adaptive mutation operator scheduling with the Religion-Based EA (RBEA......), which is an agent model with spatially structured and variable sized subpopulations (religions). In our new model (OSRBEA), we used a selection of different operators, such that each operator type was applied within one specific subpopulation only. Our results indicate that the optimal choice...

Portfolio selection using genetic algorithms | Yahaya | International ...

African Journals Online (AJOL)

In this paper, one of the nature-inspired evolutionary algorithms – a Genetic Algorithms (GA) was used in solving the portfolio selection problem (PSP). Based on a real dataset from a popular stock market, the performance of the algorithm in relation to those obtained from one of the popular quadratic programming (QP) ...

The Complexity of Constructing Evolutionary Trees Using Experiments

DEFF Research Database (Denmark)

Brodal, Gerth Stølting; Fagerberg, Rolf; Pedersen, Christian Nørgaard Storm

2001-01-01

We present tight upper and lower bounds for the problem of constructing evolutionary trees in the experiment model. We describe an algorithm which constructs an evolutionary tree of n species in time O(nd logd n) using at most n⌈d/2⌉(log2⌈d/2⌉-1 n+O(1)) experiments for d > 2, and at most n(log n......+O(1)) experiments for d = 2, where d is the degree of the tree. This improves the previous best upper bound by a factor θ(log d). For d = 2 the previously best algorithm with running time O(n log n) had a bound of 4n log n on the number of experiments. By an explicit adversary argument, we show an Ω......(nd logd n) lower bound, matching our upper bounds and improving the previous best lower bound by a factor θ(logd n). Central to our algorithm is the construction and maintenance of separator trees of small height, which may be of independent interest....

A New Hybrid Algorithm to Solve Winner Determination Problem in Multiunit Double Internet Auction

Directory of Open Access Journals (Sweden)

Mourad Ykhlef

2015-01-01

Full Text Available Solving winner determination problem in multiunit double auction has become an important E-business task. The main issue in double auction is to improve the reward in order to match the ideal prices and quantity and make the best profit for sellers and buyers according to their bids and predefined quantities. There are many algorithms introduced for solving winner in multiunit double auction. Conventional algorithms can find the optimal solution but they take a long time, particularly when they are applied to large dataset. Nowadays, some evolutionary algorithms, such as particle swarm optimization and genetic algorithm, were proposed and have been applied. In order to improve the speed of evolutionary algorithms convergence, we will propose a new kind of hybrid evolutionary algorithm that combines genetic algorithm (GA with particle swarm optimization (PSO to solve winner determination problem in multiunit double auction; we will refer to this algorithm as AUC-GAPSO.

Identification of fractional-order systems via a switching differential evolution subject to noise perturbations

Energy Technology Data Exchange (ETDEWEB)

Zhu, Wu, E-mail: dtzhuwu@gmail.com [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Fang, Jian-an [College of Information Science and Technology, Donghua University, Shanghai 201620 (China); Tang, Yang, E-mail: yang.tang@pik-potsdam.de [Institute of Physics, Humboldt University, Berlin 12489 (Germany); Potsdam Institute for Climate Impact Research, Potsdam 14415 (Germany); Research Institute for Intelligent Control and System, Harbin Institute of Technology, Harbin 150006 (China); Zhang, Wenbing [Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong (China); Xu, Yulong [College of Information Science and Technology, Donghua University, Shanghai 201620 (China)

2012-10-01

In this Letter, a differential evolution variant, called switching DE (SDE), has been employed to estimate the orders and parameters in incommensurate fractional-order chaotic systems. The proposed algorithm includes a switching population utilization strategy, where the population size is adjusted dynamically based on the solution-searching status. Thus, this adaptive control method realizes the identification of fractional-order Lorenz, Lü and Chen systems in both deterministic and stochastic environments, respectively. Numerical simulations are provided, where comparisons are made with five other State-of-the-Art evolutionary algorithms (EAs) to verify the effectiveness of the proposed method. -- Highlights: ► Switching population utilization strategy is applied for differential evolution. ► The parameters are estimated in both deterministic and stochastic environments. ► Comparisons with five other EAs verify the effectiveness of the proposed method.

Identification of fractional-order systems via a switching differential evolution subject to noise perturbations

International Nuclear Information System (INIS)

Zhu, Wu; Fang, Jian-an; Tang, Yang; Zhang, Wenbing; Xu, Yulong

2012-01-01

In this Letter, a differential evolution variant, called switching DE (SDE), has been employed to estimate the orders and parameters in incommensurate fractional-order chaotic systems. The proposed algorithm includes a switching population utilization strategy, where the population size is adjusted dynamically based on the solution-searching status. Thus, this adaptive control method realizes the identification of fractional-order Lorenz, Lü and Chen systems in both deterministic and stochastic environments, respectively. Numerical simulations are provided, where comparisons are made with five other State-of-the-Art evolutionary algorithms (EAs) to verify the effectiveness of the proposed method. -- Highlights: ► Switching population utilization strategy is applied for differential evolution. ► The parameters are estimated in both deterministic and stochastic environments. ► Comparisons with five other EAs verify the effectiveness of the proposed method.

Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks.

Science.gov (United States)

Fernández Caballero, Juan Carlos; Martínez, Francisco José; Hervás, César; Gutiérrez, Pedro Antonio

2010-05-01

This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class.

MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

Science.gov (United States)

Tamura, Koichiro; Peterson, Daniel; Peterson, Nicholas; Stecher, Glen; Nei, Masatoshi; Kumar, Sudhir

2011-01-01

Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net. PMID:21546353

Boolean Queries Optimization by Genetic Algorithms

Czech Academy of Sciences Publication Activity Database

Húsek, Dušan; Owais, S.S.J.; Krömer, P.; Snášel, Václav

2005-01-01

Roč. 15, - (2005), s. 395-409 ISSN 1210-0552 R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary algorithms * genetic algorithms * genetic programming * information retrieval * Boolean query Subject RIV: BB - Applied Statistics, Operational Research

Measurable difference in Cherenkov light between gamma and hadron induced EAS

Energy Technology Data Exchange (ETDEWEB)

Cabot, H.; Meynadier, Ch. [Universite de Perpignan, Groupe de Physique Fondamentale, Perpignan (France); Sobczynska, D. [Experimental Physics Department, University of Lodz, Lodz (Poland); Szabelska, B. [Soltan Institute for Nuclear Studies, Lodz (Poland); Szabelski, J. [Universite de Perpignan, Groupe de Physique Fondamentale, Perpignan (France)]|[Soltan Institute for Nuclear Studies, Lodz (Poland); Wibig, T. [Experimental Physics Department, University of Lodz, Lodz (Poland)

1997-12-31

We describe the possibly measurable difference in the Cherenkov light component of EAS induced by en electromagnetic particle (i.e. e{sup +}, e{sup -} or {gamma}) and induced by a hadron (i.e. proton or heavier nuclei) in TeV range. The method can be applied in experiments which use wavefront sampling method of EAS Cherenkov light detection (e.g. THEMISTOCLE, ASGAT). (author) 16 refs, 9 figs

A new evolutionary system for evolving artificial neural networks.

Science.gov (United States)

Yao, X; Liu, Y

1997-01-01

This paper presents a new evolutionary system, i.e., EPNet, for evolving artificial neural networks (ANNs). The evolutionary algorithm used in EPNet is based on Fogel's evolutionary programming (EP). Unlike most previous studies on evolving ANN's, this paper puts its emphasis on evolving ANN's behaviors. Five mutation operators proposed in EPNet reflect such an emphasis on evolving behaviors. Close behavioral links between parents and their offspring are maintained by various mutations, such as partial training and node splitting. EPNet evolves ANN's architectures and connection weights (including biases) simultaneously in order to reduce the noise in fitness evaluation. The parsimony of evolved ANN's is encouraged by preferring node/connection deletion to addition. EPNet has been tested on a number of benchmark problems in machine learning and ANNs, such as the parity problem, the medical diagnosis problems, the Australian credit card assessment problem, and the Mackey-Glass time series prediction problem. The experimental results show that EPNet can produce very compact ANNs with good generalization ability in comparison with other algorithms.

Phylogenetically Acquired Representations and Evolutionary Algorithms.

OpenAIRE

Wozniak , Adrianna

2006-01-01

First, we explain why Genetic Algorithms (GAs), inspired by the Modern Synthesis, do not accurately model biological evolution, being rather an artificial version of artificial, rather than natural selection. Being focused on optimisation, we propose two improvements of GAs, with the aim to successfully generate adapted, desired behaviour. The first one concerns phylogenetic grounding of meaning, a way to avoid the Symbol Grounding Problem. We give a definition of Phylogenetically Acquired Re...

Combining Interactive Infrastructure Modeling and Evolutionary Algorithm Optimization for Sustainable Water Resources Design

Science.gov (United States)

Smith, R.; Kasprzyk, J. R.; Zagona, E. A.

2013-12-01

Population growth and climate change, combined with difficulties in building new infrastructure, motivate portfolio-based solutions to ensuring sufficient water supply. Powerful simulation models with graphical user interfaces (GUI) are often used to evaluate infrastructure portfolios; these GUI based models require manual modification of the system parameters, such as reservoir operation rules, water transfer schemes, or system capacities. Multiobjective evolutionary algorithm (MOEA) based optimization can be employed to balance multiple objectives and automatically suggest designs for infrastructure systems, but MOEA based decision support typically uses a fixed problem formulation (i.e., a single set of objectives, decisions, and constraints). This presentation suggests a dynamic framework for linking GUI-based infrastructure models with MOEA search. The framework begins with an initial formulation which is solved using a MOEA. Then, stakeholders can interact with candidate solutions, viewing their properties in the GUI model. This is followed by changes in the formulation which represent users' evolving understanding of exigent system properties. Our case study is built using RiverWare, an object-oriented, data-centered model that facilitates the representation of a diverse array of water resources systems. Results suggest that assumptions within the initial MOEA search are violated after investigating tradeoffs and reveal how formulations should be modified to better capture stakeholders' preferences.

Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms

Directory of Open Access Journals (Sweden)

Erik Cuevas

2015-01-01

Full Text Available As an alternative to classical techniques, the problem of image segmentation has also been handled through evolutionary methods. Recently, several algorithms based on evolutionary principles have been successfully applied to image segmentation with interesting performances. However, most of them maintain two important limitations: (1 they frequently obtain suboptimal results (misclassifications as a consequence of an inappropriate balance between exploration and exploitation in their search strategies; (2 the number of classes is fixed and known in advance. This paper presents an algorithm for the automatic selection of pixel classes for image segmentation. The proposed method combines a novel evolutionary method with the definition of a new objective function that appropriately evaluates the segmentation quality with respect to the number of classes. The new evolutionary algorithm, called Locust Search (LS, is based on the behavior of swarms of locusts. Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal solutions and the limited exploration-exploitation balance. Experimental tests over several benchmark functions and images validate the efficiency of the proposed technique with regard to accuracy and robustness.

EAS selection in the EMMA underground array

DEFF Research Database (Denmark)

Sarkamo, J.; Bezrukov, L.; Enqvist, T.

2013-01-01

The first measurements of the Experiment with MultiMuon Array (EMMA) have been analyzed for the selection of the Extensive Air Showers (EAS). Test data were recorded with an underground muon tracking station and a satellite station separated laterally by 10 metres. Events with tracks distributed...

Algorithms for computing parsimonious evolutionary scenarios for genome evolution, the last universal common ancestor and dominance of horizontal gene transfer in the evolution of prokaryotes

Directory of Open Access Journals (Sweden)

Galperin Michael Y

2003-01-01

Full Text Available Abstract Background Comparative analysis of sequenced genomes reveals numerous instances of apparent horizontal gene transfer (HGT, at least in prokaryotes, and indicates that lineage-specific gene loss might have been even more common in evolution. This complicates the notion of a species tree, which needs to be re-interpreted as a prevailing evolutionary trend, rather than the full depiction of evolution, and makes reconstruction of ancestral genomes a non-trivial task. Results We addressed the problem of constructing parsimonious scenarios for individual sets of orthologous genes given a species tree. The orthologous sets were taken from the database of Clusters of Orthologous Groups of proteins (COGs. We show that the phyletic patterns (patterns of presence-absence in completely sequenced genomes of almost 90% of the COGs are inconsistent with the hypothetical species tree. Algorithms were developed to reconcile the phyletic patterns with the species tree by postulating gene loss, COG emergence and HGT (the latter two classes of events were collectively treated as gene gains. We prove that each of these algorithms produces a parsimonious evolutionary scenario, which can be represented as mapping of loss and gain events on the species tree. The distribution of the evolutionary events among the tree nodes substantially depends on the underlying assumptions of the reconciliation algorithm, e.g. whether or not independent gene gains (gain after loss after gain are permitted. Biological considerations suggest that, on average, gene loss might be a more likely event than gene gain. Therefore different gain penalties were used and the resulting series of reconstructed gene sets for the last universal common ancestor (LUCA of the extant life forms were analysed. The number of genes in the reconstructed LUCA gene sets grows as the gain penalty increases. However, qualitative examination of the LUCA versions reconstructed with different gain penalties

Recent advances in swarm intelligence and evolutionary computation

CERN Document Server

2015-01-01

This timely review volume summarizes the state-of-the-art developments in nature-inspired algorithms and applications with the emphasis on swarm intelligence and bio-inspired computation. Topics include the analysis and overview of swarm intelligence and evolutionary computation, hybrid metaheuristic algorithms, bat algorithm, discrete cuckoo search, firefly algorithm, particle swarm optimization, and harmony search as well as convergent hybridization. Application case studies have focused on the dehydration of fruits and vegetables by the firefly algorithm and goal programming, feature selection by the binary flower pollination algorithm, job shop scheduling, single row facility layout optimization, training of feed-forward neural networks, damage and stiffness identification, synthesis of cross-ambiguity functions by the bat algorithm, web document clustering, truss analysis, water distribution networks, sustainable building designs and others. As a timely review, this book can serve as an ideal reference f...

Optimal Solution for VLSI Physical Design Automation Using Hybrid Genetic Algorithm

Directory of Open Access Journals (Sweden)

I. Hameem Shanavas

2014-01-01

Full Text Available In Optimization of VLSI Physical Design, area minimization and interconnect length minimization is an important objective in physical design automation of very large scale integration chips. The objective of minimizing the area and interconnect length would scale down the size of integrated chips. To meet the above objective, it is necessary to find an optimal solution for physical design components like partitioning, floorplanning, placement, and routing. This work helps to perform the optimization of the benchmark circuits with the above said components of physical design using hierarchical approach of evolutionary algorithms. The goal of minimizing the delay in partitioning, minimizing the silicon area in floorplanning, minimizing the layout area in placement, minimizing the wirelength in routing has indefinite influence on other criteria like power, clock, speed, cost, and so forth. Hybrid evolutionary algorithm is applied on each of its phases to achieve the objective. Because evolutionary algorithm that includes one or many local search steps within its evolutionary cycles to obtain the minimization of area and interconnect length. This approach combines a hierarchical design like genetic algorithm and simulated annealing to attain the objective. This hybrid approach can quickly produce optimal solutions for the popular benchmarks.

Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference.

Science.gov (United States)

Suchow, Jordan W; Bourgin, David D; Griffiths, Thomas L

2017-07-01

Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reliability-redundancy optimization by means of a chaotic differential evolution approach

International Nuclear Information System (INIS)

Coelho, Leandro dos Santos

2009-01-01

The reliability design is related to the performance analysis of many engineering systems. The reliability-redundancy optimization problems involve selection of components with multiple choices and redundancy levels that produce maximum benefits, can be subject to the cost, weight, and volume constraints. Classical mathematical methods have failed in handling nonconvexities and nonsmoothness in optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solution in reliability-redundancy optimization problems. Evolutionary algorithms (EAs) - paradigms of evolutionary computation field - are stochastic and robust meta-heuristics useful to solve reliability-redundancy 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. A differential evolution approach based on chaotic sequences using Lozi's map for reliability-redundancy optimization problems is proposed in this paper. The proposed method has a fast convergence rate but also maintains the diversity of the population so as to escape from local optima. An application example in reliability-redundancy optimization based on the overspeed protection system of a gas turbine is given to show its usefulness and efficiency. Simulation results show that the application of deterministic chaotic sequences instead of random sequences is a possible strategy to improve the performance of differential evolution.

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

Directory of Open Access Journals (Sweden)

Feng Lin

2007-11-01

Full Text Available Abstract Background Peptides binding to Major Histocompatibility Complex (MHC class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides. In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs. Results The proposed methods are intended for finding peptides binding to MHC class II I-Ag7 molecule – a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-Ag7 datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1 an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2 quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules. Conclusion We present two MOEA-based algorithms for finding motifs

Evolutionary computation in zoology and ecology.

Science.gov (United States)

Boone, Randall B

2017-12-01

Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species' niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate.

Prevalence of E/A wave fusion and A wave truncation in DDD pacemaker patients with complete AV block under nominal AV intervals.

Directory of Open Access Journals (Sweden)

Wolfram C Poller

Full Text Available Optimization of the AV-interval (AVI in DDD pacemakers improves cardiac hemodynamics and reduces pacemaker syndromes. Manual optimization is typically not performed in clinical routine. In the present study we analyze the prevalence of E/A wave fusion and A wave truncation under resting conditions in 160 patients with complete AV block (AVB under the pre-programmed AVI. We manually optimized sub-optimal AVI.We analyzed 160 pacemaker patients with complete AVB, both in sinus rhythm (AV-sense; n = 129 and under atrial pacing (AV-pace; n = 31. Using Doppler analyses of the transmitral inflow we classified the nominal AVI as: a normal, b too long (E/A wave fusion or c too short (A wave truncation. In patients with a sub-optimal AVI, we performed manual optimization according to the recommendations of the American Society of Echocardiography.All AVB patients with atrial pacing exhibited a normal transmitral inflow under the nominal AV-pace intervals (100%. In contrast, 25 AVB patients in sinus rhythm showed E/A wave fusion under the pre-programmed AV-sense intervals (19.4%; 95% confidence interval (CI: 12.6-26.2%. A wave truncations were not observed in any patient. All patients with a complete E/A wave fusion achieved a normal transmitral inflow after AV-sense interval reduction (mean optimized AVI: 79.4 ± 13.6 ms.Given the rate of 19.4% (CI 12.6-26.2% of patients with a too long nominal AV-sense interval, automatic algorithms may prove useful in improving cardiac hemodynamics, especially in the subgroup of atrially triggered pacemaker patients with AV node diseases.

CASA-MIA: A ''precision'' EAS detector

International Nuclear Information System (INIS)

Borione, A.; Cronin, J.W.; Covault, C.E.; Fick, B.E.; Gibbs, K.G.; Krimm, H.A.; McKay, T.A.; Mueller, D.; Newport, B.J.; Ong, R.A.; Rosenberg, L.J.; Catanese, M.; Green, K.D.; Matthews, J.; Nitz, D.F.; Sinclair, D.; van der Velde, J.C.; Kieda, D.

1993-01-01

The CASA-MIA detector was constructed to search for sources of UHE neutral radiation. As such it has established limits well below those of previously reported observations and of most contemporaneous detectors. In addition to its primary mission, however, CASA-MIA measures the lateral distribution of both electrons and muons in EAS throughout a range of energies and with a degree of sampling not previously available

APPLICATION OF RESTART COVARIANCE MATRIX ADAPTATION EVOLUTION STRATEGY (RCMA-ES TO GENERATION EXPANSION PLANNING PROBLEM

Directory of Open Access Journals (Sweden)

K. Karthikeyan

2012-10-01

Full Text Available This paper describes the application of an evolutionary algorithm, Restart Covariance Matrix Adaptation Evolution Strategy (RCMA-ES to the Generation Expansion Planning (GEP problem. RCMA-ES is a class of continuous Evolutionary Algorithm (EA derived from the concept of self-adaptation in evolution strategies, which adapts the covariance matrix of a multivariate normal search distribution. The original GEP problem is modified by incorporating Virtual Mapping Procedure (VMP. The GEP problem of a synthetic test systems for 6-year, 14-year and 24-year planning horizons having five types of candidate units is considered. Two different constraint-handling methods are incorporated and impact of each method has been compared. In addition, comparison and validation has also made with dynamic programming method.

Optimization of a Conical Corrugated Antenna Using Multiobjective Heuristics for Radio-Astronomy Applications

OpenAIRE

López-Ruiz, S.; Sánchez Montero, R.; Tercero-Martínez, F.; López-Espí, P. L.; López-Fernandez, J. A.

2016-01-01

This paper presents the design of a tree sections corrugated horn antenna with a modified linear profile, using NURBS, suitable for radio-astronomy applications. The operating band ranges from 4.5 to 8.8 GHz. The aperture efficiency is higher than 84% and the return losses are greater than 20 dB in the whole bandwidth. The antenna optimization has been carried out with multiobjective versions of an evolutionary algorithm (EA) and a particle swarm optimization (PSO) algorithm. We show that bot...

Phylogenetic inference with weighted codon evolutionary distances.

Science.gov (United States)

Criscuolo, Alexis; Michel, Christian J

2009-04-01

We develop a new approach to estimate a matrix of pairwise evolutionary distances from a codon-based alignment based on a codon evolutionary model. The method first computes a standard distance matrix for each of the three codon positions. Then these three distance matrices are weighted according to an estimate of the global evolutionary rate of each codon position and averaged into a unique distance matrix. Using a large set of both real and simulated codon-based alignments of nucleotide sequences, we show that this approach leads to distance matrices that have a significantly better treelikeness compared to those obtained by standard nucleotide evolutionary distances. We also propose an alternative weighting to eliminate the part of the noise often associated with some codon positions, particularly the third position, which is known to induce a fast evolutionary rate. Simulation results show that fast distance-based tree reconstruction algorithms on distance matrices based on this codon position weighting can lead to phylogenetic trees that are at least as accurate as, if not better, than those inferred by maximum likelihood. Finally, a well-known multigene dataset composed of eight yeast species and 106 codon-based alignments is reanalyzed and shows that our codon evolutionary distances allow building a phylogenetic tree which is similar to those obtained by non-distance-based methods (e.g., maximum parsimony and maximum likelihood) and also significantly improved compared to standard nucleotide evolutionary distance estimates.

A novel algorithm of artificial immune system for high-dimensional function numerical optimization

Institute of Scientific and Technical Information of China (English)

DU Haifeng; GONG Maoguo; JIAO Licheng; LIU Ruochen

2005-01-01

Based on the clonal selection theory and immune memory theory, a novel artificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is put forward. Using the theorem of Markov chain, it is proved that IMCPA is convergent. Compared with some other evolutionary programming algorithms (like Breeder genetic algorithm), IMCPA is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like high-dimensional function optimization, which maintains the diversity of the population and avoids prematurity to some extent, and has a higher convergence speed.

Optimization of large-scale industrial systems : an emerging method

Energy Technology Data Exchange (ETDEWEB)

Hammache, A.; Aube, F.; Benali, M.; Cantave, R. [Natural Resources Canada, Varennes, PQ (Canada). CANMET Energy Technology Centre

2006-07-01

This paper reviewed optimization methods of large-scale industrial production systems and presented a novel systematic multi-objective and multi-scale optimization methodology. The methodology was based on a combined local optimality search with global optimality determination, and advanced system decomposition and constraint handling. The proposed method focused on the simultaneous optimization of the energy, economy and ecology aspects of industrial systems (E{sup 3}-ISO). The aim of the methodology was to provide guidelines for decision-making strategies. The approach was based on evolutionary algorithms (EA) with specifications including hybridization of global optimality determination with a local optimality search; a self-adaptive algorithm to account for the dynamic changes of operating parameters and design variables occurring during the optimization process; interactive optimization; advanced constraint handling and decomposition strategy; and object-oriented programming and parallelization techniques. Flowcharts of the working principles of the basic EA were presented. It was concluded that the EA uses a novel decomposition and constraint handling technique to enhance the Pareto solution search procedure for multi-objective problems. 6 refs., 9 figs.

Feature Selection of Network Intrusion Data using Genetic Algorithm and Particle Swarm Optimization

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Iwan Syarif

2016-12-01

Full Text Available This paper describes the advantages of using Evolutionary Algorithms (EA for feature selection on network intrusion dataset. Most current Network Intrusion Detection Systems (NIDS are unable to detect intrusions in real time because of high dimensional data produced during daily operation. Extracting knowledge from huge data such as intrusion data requires new approach. The more complex the datasets, the higher computation time and the harder they are to be interpreted and analyzed. This paper investigates the performance of feature selection algoritms in network intrusiona data. We used Genetic Algorithms (GA and Particle Swarm Optimizations (PSO as feature selection algorithms. When applied to network intrusion datasets, both GA and PSO have significantly reduces the number of features. Our experiments show that GA successfully reduces the number of attributes from 41 to 15 while PSO reduces the number of attributes from 41 to 9. Using k Nearest Neighbour (k-NN as a classifier,the GA-reduced dataset which consists of 37% of original attributes, has accuracy improvement from 99.28% to 99.70% and its execution time is also 4.8 faster than the execution time of original dataset. Using the same classifier, PSO-reduced dataset which consists of 22% of original attributes, has the fastest execution time (7.2 times faster than the execution time of original datasets. However, its accuracy is slightly reduced 0.02% from 99.28% to 99.26%. Overall, both GA and PSO are good solution as feature selection techniques because theyhave shown very good performance in reducing the number of features significantly while still maintaining and sometimes improving the classification accuracy as well as reducing the computation time.

Optimal mutation rates for the (1+λ) EA on OneMax

DEFF Research Database (Denmark)

Gießen, Christian; Witt, Carsten

2016-01-01

We study the (1 + λ) EA with mutation probability c/n, where c > 0 is a constant, on the ONEMAX problem. Using an improved variable drift theorem, we show that upper and lower bounds on the expected runtime of the (1+λ) EA obtained from variable drift theorems are at most apart by a small lower...... mutation rates for the (1+λ) EA for various parameter settings of c and λ and also for moderate sizes of n. This makes the need for potentially lengthy and costly experiments in order to optimize the parameters unnecessary. Interestingly, even for moderate n and not too small λ it turns out that mutation...... rates up to 10% larger than the asymptotically optimal rate 1/n minimize the expected runtime. However, in absolute terms the expected runtime does not change by much when replacing 1/n with the optimal mutation rate....

From evolutionary computation to the evolution of things

NARCIS (Netherlands)

Eiben, A.E.; Smith, J.E.

2015-01-01

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as

Biology Needs Evolutionary Software Tools: Let’s Build Them Right

Science.gov (United States)

Team, Galaxy; Goecks, Jeremy; Taylor, James

2018-01-01

Abstract Research in population genetics and evolutionary biology has always provided a computational backbone for life sciences as a whole. Today evolutionary and population biology reasoning are essential for interpretation of large complex datasets that are characteristic of all domains of today’s life sciences ranging from cancer biology to microbial ecology. This situation makes algorithms and software tools developed by our community more important than ever before. This means that we, developers of software tool for molecular evolutionary analyses, now have a shared responsibility to make these tools accessible using modern technological developments as well as provide adequate documentation and training. PMID:29688462

Isolation and characterization of new lignin streams derived from extractive-ammonia (EA) pretreatment

Energy Technology Data Exchange (ETDEWEB)

da Costa Sousa, Leonardo [Michigan State Univ., East Lansing, MI (United States); Foston, Marcus [Washington Univ., St. Louis, MO (United States); Bokade, Vijay [National Chemical Lab., Pune (India); Azarpira, Ali [Univ. of Wisconsin, Madison, WI (United States); Lu, Fachuang [Univ. of Wisconsin, Madison, WI (United States); Ragauskas, Arthur J. [Univ. of Tennessee, Knoxville, TN (United States); Ralph, John [Univ. of Wisconsin, Madison, WI (United States); Dale, Bruce [Michigan State Univ., East Lansing, MI (United States); Balan, Venkatesh [Michigan State Univ., East Lansing, MI (United States)

2016-05-05

One of the key challenges facing lignin conversion to fuels and chemicals is related to the level of carbohydrate and ash impurities found in extracted lignin. Structural modifications of lignin may also occur as a result of biomass pretreatment and harsh lignin extraction protocols. Extractive-Ammonia (EA) is a new pretreatment technology that uses liquid ammonia to cleave lignin–carbohydrate complexes, decrystallize cellulose, solubilize lignin, and selectively extract lignin from lignocellulosic biomass, enabling better utilization of both lignin and carbohydrate components in a biorefinery. The EA-based biorefinery produces two different lignin-rich streams, with different properties, that could potentially be upgraded to fuels and chemicals using green processes. Here, a water/ethanol-based fractionation method was developed to enrich the ammonia-soluble extractives, resulting in a major product stream containing 92% lignin. Detailed characterization of the various streams resulting from EA treatment, including compositional analysis, structural characterization by nuclear magnetic resonance (NMR) spectrometry, elemental analysis, molecular weight analysis, and thermo-gravimetric analysis provides a broad evaluation of the EA-derived lignin product stream structures and properties, assessing their potential for commercial applications. In conclusion, EA-derived lignins preserve much of lignin's functionality, including the sensitive β-aryl ether units. Furthermore, we observed nitrogen incorporation in the lignin-rich streams, notably due to the presence of hydroxycinnamoyl amides formed during ammonia pretreatment.

The Stellar Kinematics of E+A Galaxies in SDSS IV-MaNGA

Science.gov (United States)

Johnson, Amalya; Dudley, Raymond; Edwards, Kay; Gonzalez, Andrea; Kerrison, Nicole; Marinelli, Mariarosa; Melchert, Nancy; Ojanen, Winonah; Liu, Charles; SDSS-IV MaNGA

2018-01-01

E+A galaxies, hypothesized to be “transition” galaxies between the blue cloud and the red sequence, are valuable sources for studying the evolution of galaxies. Using data from the Sloan Digital Sky Survey Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, a large scale integral field spectroscopic survey of nearby galaxies from 3600 to 10300 Å, we identifed galaxies that exhibitted E+A characteristics within their optical spectra. We analyzed the 2,812 galaxies thus far observed by MaNGA to identify those that showed evidence of a starburst about 1 billion years ago, followed by cessation of star formation and quenching of the galaxy. Through this process we identifed 39 E+A galaxies by directly looking at the optical spectra and ensuring they exhibited the necessary properties of an E+A spectra, including a strong break at the 4000 Å mark, little to no Hα emission and absorption through the Balmer series, and a blue slope of the continuum past ~5000 Å as the flux decreases. We analyzed the stellar kinematics of these galaxies to determine whether or not they were fast or slow rotators, a proposed indicator of a major merger in their recent past. Using Voronoi binned graphs from the MaNGA Marvin database, we measured their stellar rotation curves in order to more clearly show the range of velocities within the galaxies. Among our 39 E+A candidates, all but two exhibited significant, orderly rotation across the galaxy, and 29 out of 39 of our galaxies show rotation faster than 30 km/s. With the caveat that our selection process was biased toward galaxies with orderly rotation, this prevalence of rotation challenges the belief that all E+A galaxies are created from major mergers. This work was supported by grants AST-1460860 from the National Science Foundation and SDSS FAST/SSP-483 from the Alfred P. Sloan Foundation to the CUNY College of Staten Island.

Quantitative Trait Loci Mapping Problem: An Extinction-Based Multi-Objective Evolutionary Algorithm Approach

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Nicholas S. Flann

2013-09-01

Full Text Available The Quantitative Trait Loci (QTL mapping problem aims to identify regions in the genome that are linked to phenotypic features of the developed organism that vary in degree. It is a principle step in determining targets for further genetic analysis and is key in decoding the role of specific genes that control quantitative traits within species. Applications include identifying genetic causes of disease, optimization of cross-breeding for desired traits and understanding trait diversity in populations. In this paper a new multi-objective evolutionary algorithm (MOEA method is introduced and is shown to increase the accuracy of QTL mapping identification for both independent and epistatic loci interactions. The MOEA method optimizes over the space of possible partial least squares (PLS regression QTL models and considers the conflicting objectives of model simplicity versus model accuracy. By optimizing for minimal model complexity, MOEA has the advantage of solving the over-fitting problem of conventional PLS models. The effectiveness of the method is confirmed by comparing the new method with Bayesian Interval Mapping approaches over a series of test cases where the optimal solutions are known. This approach can be applied to many problems that arise in analysis of genomic data sets where the number of features far exceeds the number of observations and where features can be highly correlated.

Heat transfer analysis of unsteady graphene oxide nanofluid flow using a fuzzy identifier evolved by genetically encoded mutable smart bee algorithm

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Mohammadreza Azimi

2015-03-01

Full Text Available In the current research, the unsteady two dimensional Graphene Oxide water based nanofluid heat transfer between two moving parallel plates is analyzed using an intelligent black-box identifier. The developed intelligent tool is known as evolvable evolutionary fuzzy inference system (EE-FIS which is based on the integration of low-level fuzzy programming and hyper-level evolutionary computing concepts. Here, the authors propose the use of a modified evolutionary algorithm (EA which is called hybrid genetic mutable smart bee algorithm (HGMSBA. The proposed HGMSBA is used to evolve both antecedent and consequent parts of fuzzy rule base. Besides, it tries to prune the rule base of fuzzy inference system (FIS to decrease its computational complexity and increase its interpretability. By considering the prediction error of the fuzzy identifier as the objective function of HGMSBA, an automatic soft interpolation machine is developed which can intuitively increase the robustness and accuracy of the final model. Here, HGMSBA-FIS is used to provide a nonlinear map between inputs, i.e. nanoparticles solid volume fraction (ϕ, Eckert number (Ec and a moving parameter which describes the movements of plates (S, and output, i.e. Nusselt number (Nu. Prior to proceeding with the modeling process, a comprehensive numerical comparative study is performed to investigate the potentials of the proposed model for nonlinear system identification. After demonstrating the efficacy of HGMSBA for training the FIS, the system is applied to the considered problem. Based on the obtained results, it can be inferred that the developed HGMSBA-FIS black-box identifier can be used as a very authentic tool with respect to accuracy and robustness. Besides, as the proposed black-box is not a physics-based identifier, it frees experts from the cumbersome mathematical formulations, and can be used for advanced real-time applications such as model-based control. The simulations

Hidden long evolutionary memory in a model biochemical network

Science.gov (United States)

Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-04-01

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

Exploratory Analysis of an On-line Evolutionary Algorithm in Simulated Robots

NARCIS (Netherlands)

Haasdijk, E.W.; Smit, S.K.; Eiben, A.E.

2012-01-01

In traditional evolutionary robotics, robot controllers are evolved in a separate design phase preceding actual deployment; we call this off-line evolution. Alternatively, robot controllers can evolve while the robots perform their proper tasks, during the actual operational phase; we call this

Modeling of genetic algorithms with a finite population

NARCIS (Netherlands)

C.H.M. van Kemenade

1997-01-01

textabstractCross-competition between non-overlapping building blocks can strongly influence the performance of evolutionary algorithms. The choice of the selection scheme can have a strong influence on the performance of a genetic algorithm. This paper describes a number of different genetic

Artificial root foraging optimizer algorithm with hybrid strategies

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Yang Liu

2017-02-01

Full Text Available In this work, a new plant-inspired optimization algorithm namely the hybrid artificial root foraging optimizion (HARFO is proposed, which mimics the iterative root foraging behaviors for complex optimization. In HARFO model, two innovative strategies were developed: one is the root-to-root communication strategy, which enables the individual exchange information with each other in different efficient topologies that can essentially improve the exploration ability; the other is co-evolution strategy, which can structure the hierarchical spatial population driven by evolutionary pressure of multiple sub-populations that ensure the diversity of root population to be well maintained. The proposed algorithm is benchmarked against four classical evolutionary algorithms on well-designed test function suites including both classical and composition test functions. Through the rigorous performance analysis that of all these tests highlight the significant performance improvement, and the comparative results show the superiority of the proposed algorithm.

Tools for Accurate and Efficient Analysis of Complex Evolutionary Mechanisms in Microbial Genomes. Final Report

Energy Technology Data Exchange (ETDEWEB)

Nakhleh, Luay

2014-03-12

I proposed to develop computationally efficient tools for accurate detection and reconstruction of microbes' complex evolutionary mechanisms, thus enabling rapid and accurate annotation, analysis and understanding of their genomes. To achieve this goal, I proposed to address three aspects. (1) Mathematical modeling. A major challenge facing the accurate detection of HGT is that of distinguishing between these two events on the one hand and other events that have similar "effects." I proposed to develop a novel mathematical approach for distinguishing among these events. Further, I proposed to develop a set of novel optimization criteria for the evolutionary analysis of microbial genomes in the presence of these complex evolutionary events. (2) Algorithm design. In this aspect of the project, I proposed to develop an array of e cient and accurate algorithms for analyzing microbial genomes based on the formulated optimization criteria. Further, I proposed to test the viability of the criteria and the accuracy of the algorithms in an experimental setting using both synthetic as well as biological data. (3) Software development. I proposed the nal outcome to be a suite of software tools which implements the mathematical models as well as the algorithms developed.

Self-Organized Criticality and Mass Extinction in Evolutionary Algorithms

DEFF Research Database (Denmark)

Krink, Thiemo; Thomsen, Rene

2001-01-01

The gaps in the fossil record gave rise to the hypothesis that evolution proceeded in long periods of stasis, which alternated with occasional, rapid changes that yielded evolutionary progress. One mechanism that could cause these punctuated bursts is the re-colonbation of changing and deserted...... at a critical state between chaos and order, known as self-organized criticality (SOC). Based on this background, we used SOC to control the size of spatial extinction zones in a diffusion model. The SOC selection process was easy to implement and implied only negligible computational costs. Our results show...

Enhancing Evolutionary Optimization in Uncertain Environments by Allocating Evaluations via Multi-armed Bandit Algorithms

OpenAIRE

Qiu, Xin; Miikkulainen, Risto

2018-01-01

Optimization problems with uncertain fitness functions are common in the real world, and present unique challenges for evolutionary optimization approaches. Existing issues include excessively expensive evaluation, lack of solution reliability, and incapability in maintaining high overall fitness during optimization. Using conversion rate optimization as an example, this paper proposes a series of new techniques for addressing these issues. The main innovation is to augment evolutionary algor...

Classification of EA1-box proteins and new insights into their role during reproduction in grasses.

Science.gov (United States)

Uebler, Susanne; Márton, Mihaela L; Dresselhaus, Thomas

2015-12-01

EA1-box protein classification. Success in reproduction and vegetative development in flowering plants strongly depends on precise cell-to-cell signaling events mediated by secreted peptides.A small peptide family named as EA1-like (EAL) has been first described 10 years ago and includes EA1 involved in pollen tubes attraction by the female gametophyte and EAL1-regulating germ cell identity in maize. EALs consist of an N-terminal endoplasmic reticulum-targeting motif, the highly conserved EA1-box and a short C-terminal alanine-rich domain. Whereas EAL peptides are exclusively found in the Gramineae, the EA1-box is widely distributed throughout the plant kingdom. Based on in silico analysis and subcellular localization studies, we report here a new classification of EA1-box proteins in flowering plants. They can be distinguished into three protein classes: the already defined EAL proteins, the EAG (EA1-box glycine-rich) proteins and the EAC (EA1-box containing)proteins. While fusion proteins of EAL and EAC classes locate to the secretory pathway, EAGs are cytoplasmic and locate also to the nucleus. Moreover, we further show that the third EAL protein of Zea mays, EAL2, appears to be also involved in processes related to late embryogenic development as its peptide level increases after formation of leaf primordia. Immunohistochemical studies indicate its presence in the scutellar parenchyma and around the vasculature, where it is secreted to the extracellular space. In conclusion, the members of the maize EAL family possess very diverse functions during reproduction and it will now be exciting to elucidate the functions of EAGs and EACs in plants.

Evolutionary computing in Nuclear Engineering Institute/CNEN-Brazil

International Nuclear Information System (INIS)

Pereira, Claudio M.N.A.; Lapa, Celso M.F.; Lapa, Nelbia da Silva; Mol, Antonio C.

2000-01-01

This paper aims to discuss the importance of evolutionary computation (CE) for nuclear engineering and the development of this area in the Instituto de Engenharia Nuclear (IEN) at the last years. Are describe, briefly, the applications realized in this institute by the technical group of CE. For example: nuclear reactor core design optimization, preventive maintenance scheduling optimizing and nuclear reactor transient identifications. It is also shown a novel computational tool to implementation of genetic algorithm that was development in this institute and applied in those works. Some results were presents and the gains obtained with the evolutionary computation were discussing. (author)

Applications of evolutionary computation in image processing and pattern recognition

CERN Document Server

Cuevas, Erik; Perez-Cisneros, Marco

2016-01-01

This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an...

Investigating the Multi-memetic Mind Evolutionary Computation Algorithm Efficiency

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M. K. Sakharov

2017-01-01

Full Text Available In solving practically significant problems of global optimization, the objective function is often of high dimensionality and computational complexity and of nontrivial landscape as well. Studies show that often one optimization method is not enough for solving such problems efficiently - hybridization of several optimization methods is necessary.One of the most promising contemporary trends in this field are memetic algorithms (MA, which can be viewed as a combination of the population-based search for a global optimum and the procedures for a local refinement of solutions (memes, provided by a synergy. Since there are relatively few theoretical studies concerning the MA configuration, which is advisable for use to solve the black-box optimization problems, many researchers tend just to adaptive algorithms, which for search select the most efficient methods of local optimization for the certain domains of the search space.The article proposes a multi-memetic modification of a simple SMEC algorithm, using random hyper-heuristics. Presents the software algorithm and memes used (Nelder-Mead method, method of random hyper-sphere surface search, Hooke-Jeeves method. Conducts a comparative study of the efficiency of the proposed algorithm depending on the set and the number of memes. The study has been carried out using Rastrigin, Rosenbrock, and Zakharov multidimensional test functions. Computational experiments have been carried out for all possible combinations of memes and for each meme individually.According to results of study, conducted by the multi-start method, the combinations of memes, comprising the Hooke-Jeeves method, were successful. These results prove a rapid convergence of the method to a local optimum in comparison with other memes, since all methods perform the fixed number of iterations at the most.The analysis of the average number of iterations shows that using the most efficient sets of memes allows us to find the optimal

AMOBH: Adaptive Multiobjective Black Hole Algorithm.

Science.gov (United States)

Wu, Chong; Wu, Tao; Fu, Kaiyuan; Zhu, Yuan; Li, Yongbo; He, Wangyong; Tang, Shengwen

2017-01-01

This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called "adaptive multiobjective black hole algorithm" (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

Spectrophotometric Properties of E+A Galaxies in SDSS-IV MaNGA

Science.gov (United States)

Marinelli, Mariarosa; Dudley, Raymond; Edwards, Kay; Gonzalez, Andrea; Johnson, Amalya; Kerrison, Nicole; Melchert, Nancy; Ojanen, Winonah; Weaver, Olivia; Liu, Charles; SDSS-IV MaNGA

2018-01-01

Quenched post-starburst galaxies, or E+A galaxies, represent a unique and informative phase in the evolution of galaxies. We used a qualitative rubric-based methodology, informed by the literature, to manually select galaxies from the SDSS-IV IFU survey Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) using the single-fiber spectra from the Sloan Digital Sky Survey Data Release 8. Of the 2,812 galaxies observed so far in MaNGA, we found 39 galaxies meeting our criteria for E+A classification. Spectral energy distributions of these 39 galaxies from the far-UV to the mid-infrared demonstrate a heterogeneity in our sample emerging in the infrared, indicating many distinct paths to visually similar optical spectra. We used SDSS-IV MaNGA Pipe3D data products to analyze stellar population ages, and found that 34 galaxies exhibited stellar populations that were older at 1 effective radius than at the center of the galaxy. Given that our sample was manually chosen based on E+A markers in the single-fiber spectra aimed at the center of each galaxy, our E+A galaxies may have only experienced their significant starbursts in the central region, with a disk of quenched or quenching material further outward. This work was supported by grants AST-1460860 from the National Science Foundation and SDSS FAST/SSP-483 from the Alfred P. Sloan Foundation to the CUNY College of Staten Island.

A hybrid multi-objective evolutionary algorithm approach for ...

Indian Academy of Sciences (India)

V K MANUPATI

for handling sequence- and machine-dependent set-up times ... algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventional ..... position and cognitive learning factor are considered for.

A bi-population based scheme for an explicit exploration/exploitation trade-off in dynamic environments

Science.gov (United States)

Ben-Romdhane, Hajer; Krichen, Saoussen; Alba, Enrique

2017-05-01

Optimisation in changing environments is a challenging research topic since many real-world problems are inherently dynamic. Inspired by the natural evolution process, evolutionary algorithms (EAs) are among the most successful and promising approaches that have addressed dynamic optimisation problems. However, managing the exploration/exploitation trade-off in EAs is still a prevalent issue, and this is due to the difficulties associated with the control and measurement of such a behaviour. The proposal of this paper is to achieve a balance between exploration and exploitation in an explicit manner. The idea is to use two equally sized populations: the first one performs exploration while the second one is responsible for exploitation. These tasks are alternated from one generation to the next one in a regular pattern, so as to obtain a balanced search engine. Besides, we reinforce the ability of our algorithm to quickly adapt after cnhanges by means of a memory of past solutions. Such a combination aims to restrain the premature convergence, to broaden the search area, and to speed up the optimisation. We show through computational experiments, and based on a series of dynamic problems and many performance measures, that our approach improves the performance of EAs and outperforms competing algorithms.

A global optimization algorithm inspired in the behavior of selfish herds.

Science.gov (United States)

Fausto, Fernando; Cuevas, Erik; Valdivia, Arturo; González, Adrián

2017-10-01

In this paper, a novel swarm optimization algorithm called the Selfish Herd Optimizer (SHO) is proposed for solving global optimization problems. SHO is based on the simulation of the widely observed selfish herd behavior manifested by individuals within a herd of animals subjected to some form of predation risk. In SHO, individuals emulate the predatory interactions between groups of prey and predators by two types of search agents: the members of a selfish herd (the prey) and a pack of hungry predators. Depending on their classification as either a prey or a predator, each individual is conducted by a set of unique evolutionary operators inspired by such prey-predator relationship. These unique traits allow SHO to improve the balance between exploration and exploitation without altering the population size. To illustrate the proficiency and robustness of the proposed method, it is compared to other well-known evolutionary optimization approaches such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Differential Evolution (DE), Genetic Algorithms (GA), Crow Search Algorithm (CSA), Dragonfly Algorithm (DA), Moth-flame Optimization Algorithm (MOA) and Sine Cosine Algorithm (SCA). The comparison examines several standard benchmark functions, commonly considered within the literature of evolutionary algorithms. The experimental results show the remarkable performance of our proposed approach against those of the other compared methods, and as such SHO is proven to be an excellent alternative to solve global optimization problems. Copyright © 2017 Elsevier B.V. All rights reserved.

Group leaders optimization algorithm

Science.gov (United States)

Daskin, Anmer; Kais, Sabre

2011-03-01

We present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique which is designed into a group architecture. To demonstrate the efficiency of the method, a standard suite of single and multi-dimensional optimization functions along with the energies and the geometric structures of Lennard-Jones clusters are given as well as the application of the algorithm on quantum circuit design problems. We show that as an improvement over previous methods, the algorithm scales as N 2.5 for the Lennard-Jones clusters of N-particles. In addition, an efficient circuit design is shown for a two-qubit Grover search algorithm which is a quantum algorithm providing quadratic speedup over the classical counterpart.

Evolutionary Algorithms Approach to the Solution of Damage Detection Problems

Science.gov (United States)

Salazar Pinto, Pedro Yoajim; Begambre, Oscar

2010-09-01

In this work is proposed a new Self-Configured Hybrid Algorithm by combining the Particle Swarm Optimization (PSO) and a Genetic Algorithm (GA). The aim of the proposed strategy is to increase the stability and accuracy of the search. The central idea is the concept of Guide Particle, this particle (the best PSO global in each generation) transmits its information to a particle of the following PSO generation, which is controlled by the GA. Thus, the proposed hybrid has an elitism feature that improves its performance and guarantees the convergence of the procedure. In different test carried out in benchmark functions, reported in the international literature, a better performance in stability and accuracy was observed; therefore the new algorithm was used to identify damage in a simple supported beam using modal data. Finally, it is worth noting that the algorithm is independent of the initial definition of heuristic parameters.

THE CONCEPT OF USING EVOLUTIONARY ALGORITHMS AS TOOLS FOR OPTIMAL PLANNING OF MULTIMODAL COMPOSITION IN THE DIDACTIC TEXTS

Directory of Open Access Journals (Sweden)

Marek A. Jakubowski

2014-11-01

Full Text Available At the beginning we would like to provide a short description of the new theory of learning in the digital age called connectivism. It is the integration of principles explored by the following theories: chaos, network, complexity and self-organization. Next, we describe in short new visual solutions for the teaching of writing so called multimodal literacy 5–11. We define and describe the following notions: multimodal text and original theory so called NOS (non-optimum systems methodology as a basis for new methods of visual solutions at the classes and audiovisual texts applications. Especially, we would like to emphasize the tremendous usefulness of evolutionary algorithms VEGA and NSGA as tools for optimal planning of multimodal composition in teaching texts. Finally, we give some examples of didactic texts for classrooms, which provide a deep insight into learning skills and tasks needed in the Internet age.

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

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Gidrol Xavier

2008-02-01

Full Text Available Abstract Background Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge. Results We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC devoted to BN structure learning. Conclusion We studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.

Enhancing Accuracy of Sediment Total Load Prediction Using Evolutionary Algorithms (Case Study: Gotoorchay River

Directory of Open Access Journals (Sweden)

K. Roshangar

2016-09-01

Full Text Available Introduction: Exact prediction of transported sediment rate by rivers in water resources projects is of utmost importance. Basically erosion and sediment transport process is one of the most complexes hydrodynamic. Although different studies have been developed on the application of intelligent models based on neural, they are not widely used because of lacking explicitness and complexity governing on choosing and architecting of proper network. In this study, a Genetic expression programming model (as an important branches of evolutionary algorithems for predicting of sediment load is selected and investigated as an intelligent approach along with other known classical and imperical methods such as Larsen´s equation, Engelund-Hansen´s equation and Bagnold´s equation. Materials and Methods: In this study, in order to improve explicit prediction of sediment load of Gotoorchay, located in Aras catchment, Northwestern Iran latitude: 38°24´33.3˝ and longitude: 44°46´13.2˝, genetic programming (GP and Genetic Algorithm (GA were applied. Moreover, the semi-empirical models for predicting of total sediment load and rating curve have been used. Finally all the methods were compared and the best ones were introduced. Two statistical measures were used to compare the performance of the different models, namely root mean square error (RMSE and determination coefficient (DC. RMSE and DC indicate the discrepancy between the observed and computed values. Results and Discussions: The statistical characteristics results obtained from the analysis of genetic programming method for both selected model groups indicated that the model 4 including the only discharge of the river, relative to other studied models had the highest DC and the least RMSE in the testing stage (DC= 0.907, RMSE= 0.067. Although there were several parameters applied in other models, these models were complicated and had weak results of prediction. Our results showed that the model 9

47 CFR 1.1311 - Environmental information to be included in the environmental assessment (EA).

Science.gov (United States)

2010-10-01

... a residential area, the EA must also address the impact of this lighting upon the residents. (2) A... concerning the proposal's environmental impact, if any. The EA shall deal specifically with any feature of... land utilized (e.g., deforestation, water diversion, wetland fill, or other extensive change of surface...

An improved genetic algorithm with dynamic topology

International Nuclear Information System (INIS)

Cai Kai-Quan; Tang Yan-Wu; Zhang Xue-Jun; Guan Xiang-Min

2016-01-01

The genetic algorithm (GA) is a nature-inspired evolutionary algorithm to find optima in search space via the interaction of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topologies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence. (paper)

Energy-Efficient Train Operation Using Nature-Inspired Algorithms

Directory of Open Access Journals (Sweden)

Kemal Keskin

2017-01-01

Full Text Available A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. As a case study a real-like train and test track from a part of Eskisehir light rail network were modeled. Speed limitations, various track alignments, maximum allowable trip time, and changes in train mass were considered, and punctuality was put into objective function as a penalty factor. Results have shown that all three evolutionary methods generated effective and consistent solutions. However, it has also been shown that each one has different accuracy and convergence characteristics.

Enrichment of Microbial Cultures for Hydrolysis of EA2192

National Research Council Canada - National Science Library

Rastogi, Vipin

1998-01-01

.... The enrichment was initiated using consortium adapted to biodegrade hydrolyzed vx (courtesy Dr. DeFrank). Based on NMR analysis, the concentration of EA2192 reduced from 120 ppm to 50 ppm in 48 hr of growth...

The time structure of the EAS muon component observed in the KASCADE experiment

International Nuclear Information System (INIS)

Brancus, I.M.; Badea, A.F.; Duma, M.; Vulpescu, B.; Rebel, H.; Haeusler, R.; Mathes, H.J.

1998-01-01

The determination of the chemical composition in the energy range of 10 15 eV is the scientific goal of the KASCADE (Karlsruhe Shower Core and Array Detector) experiment. Under this aspect, the temporal structure of the muon component of extended air showers (EAS) is of great interest for a detailed understanding of the EAS features, since the muon arrival time distribution maps the longitudinal EAS development via the time-of-flight of muons produced in large atmospheric heights and shows effects for mass discrimination of the EAS primaries. Using the muon detection and timing facilities of the KASCADE Central Detector, in particular the 'trigger' layer of 456 scintillation detectors and the position-sensitive large-area multiwire proportional chambers, installed under the hadron calorimeter, the muon arrival time distributions (E μ ≥ 2 GeV) registered relative to the arrival of the foremost muons have been measured. Their dependence on various shower quantities, the distance from the shower center R μ , the zenith angle Θ of shower incidence and muon shower content N μ tr (which is an identifier of the primary energy), have been studied. We characterize the single arrival time distribution by various different moments: the median Δτ 0.50 , the 1 st and the 3 rd quartiles (Δτ 0.25 and Δτ 0.75 ) which express different features of the distributions. The dependence of the measured median distributions on the distance from the shower center for a restricted Θ-range and averaged over the observed shower size spectrum is presented. The shapes can be approximated by a gamma probability distribution function. There are also displayed the mean values (Δτ 0.50 ) and the dispersion σ 0.50 of the median distributions, whose R μ -variations represent the (median) profile on the EAS muon disc. The theoretical interpretation of this kind of experimental results on the basis of Monte Carlo simulations of the EAS development is under progress. (authors)

[A sporadic case of episodic ataxia with nystagmus (EA-2)].

Science.gov (United States)

Namekawa, M; Takiyama, Y; Ueno, N; Nishizawa, M

1998-05-01

A 39-year-old man with episodic ataxia with nystagmus (EA-2) was reported. He showed intermittent cerebellar dysfunction, i.e., ataxia, nystagmus, dysarthria and vertigo, since he was 10 years old. Although this attack lasted for several hours, he was normal with exception of interictal nystagmus. His parents and sister showed no episodic ataxia. We ruled out the diseases, which may cause episodic ataxia, such as multiple sclerosis, vascular disorders, metabolic disorders and congenital anomalies. He was released from the attack by treatment with acetazolamide. EA-2 has been associated with mutations in the alpha 1A-voltage dependent calcium channel gene (CACNL1A4), which is also affected in familial hemiplegic migraine (FMH) and spinocerebellar ataxia type 6 (SCA6). In EA-2, frame-shift mutation leading to premature stop and splice-site mutation leading to truncated, non-functional channel protein have been reported. However, our patient did not have the mutations in the CACNL1A4 gene that were previously reported. In addition, our patient did not have an expanded CAG allele in the CACNL1A4 gene which is responsible for SCA6. Further examination is required to address whether a new mutation exists in the CACNL1A4 gene in our patient.

Effectively Tackling Reinsurance Problems by Using Evolutionary and Swarm Intelligence Algorithms

Directory of Open Access Journals (Sweden)

Sancho Salcedo-Sanz

2014-04-01

Full Text Available This paper is focused on solving different hard optimization problems that arise in the field of insurance and, more specifically, in reinsurance problems. In this area, the complexity of the models and assumptions considered in the definition of the reinsurance rules and conditions produces hard black-box optimization problems (problems in which the objective function does not have an algebraic expression, but it is the output of a system (usually a computer program, which must be solved in order to obtain the optimal output of the reinsurance. The application of traditional optimization approaches is not possible in this kind of mathematical problem, so new computational paradigms must be applied to solve these problems. In this paper, we show the performance of two evolutionary and swarm intelligence techniques (evolutionary programming and particle swarm optimization. We provide an analysis in three black-box optimization problems in reinsurance, where the proposed approaches exhibit an excellent behavior, finding the optimal solution within a fraction of the computational cost used by inspection or enumeration methods.

A Combined group EA-PROMETHEE method for a supplier selection problem

Directory of Open Access Journals (Sweden)

Hamid Reza Rezaee Kelidbari

2016-07-01

Full Text Available One of the important decisions which impacts all firms’ activities is the supplier selection problem. Since the 1950s, several works have addressed this problem by treating different aspects and instances. In this paper, a combined multiple criteria decision making (MCDM technique (EA-PROMETHEE has been applied to implement a proper decision making. To this aim, after reviewing the theoretical background regarding to supplier selection, the extension analysis (EA is used to determine the importance of criteria and PROMETHEE for appraisal of suppliers based on the criteria. An empirical example illustrated the proposed approach.

Computational characterization of HPGe detectors usable for a wide variety of source geometries by using Monte Carlo simulation and a multi-objective evolutionary algorithm

Science.gov (United States)

Guerra, J. G.; Rubiano, J. G.; Winter, G.; Guerra, A. G.; Alonso, H.; Arnedo, M. A.; Tejera, A.; Martel, P.; Bolivar, J. P.

2017-06-01

In this work, we have developed a computational methodology for characterizing HPGe detectors by implementing in parallel a multi-objective evolutionary algorithm, together with a Monte Carlo simulation code. The evolutionary algorithm is used for searching the geometrical parameters of a model of detector by minimizing the differences between the efficiencies calculated by Monte Carlo simulation and two reference sets of Full Energy Peak Efficiencies (FEPEs) corresponding to two given sample geometries, a beaker of small diameter laid over the detector window and a beaker of large capacity which wrap the detector. This methodology is a generalization of a previously published work, which was limited to beakers placed over the window of the detector with a diameter equal or smaller than the crystal diameter, so that the crystal mount cap (which surround the lateral surface of the crystal), was not considered in the detector model. The generalization has been accomplished not only by including such a mount cap in the model, but also using multi-objective optimization instead of mono-objective, with the aim of building a model sufficiently accurate for a wider variety of beakers commonly used for the measurement of environmental samples by gamma spectrometry, like for instance, Marinellis, Petris, or any other beaker with a diameter larger than the crystal diameter, for which part of the detected radiation have to pass through the mount cap. The proposed methodology has been applied to an HPGe XtRa detector, providing a model of detector which has been successfully verificated for different source-detector geometries and materials and experimentally validated using CRMs.

Time for a new approach to public participation in EA: Promoting cooperation and consensus for sustainability

International Nuclear Information System (INIS)

Doelle, Meinhard; Sinclair, A. John

2006-01-01

One of the fundamental challenges of project-based environmental assessments (EA) has been to deliver on the promise of meaningful public participation leading to decisions that put affected societies on the path to sustainability. The record to date has been less than promising, leading the authors to propose that it is time to consider a different approach to legislating public participation in project assessments, one that starts with the ultimate objective of cooperation and consensus building. The authors work back from this objective and propose an EA process specifically designed to encourage all participants to participate constructively. In the process, the authors identify how the proposed process will address various criticisms made of the traditional approach to EA by proponents, government officials and members of the public alike. Through a fundamental shift from process requirements to a focus on the outcomes of EA, the authors propose a way forward for project-based EA to deliver on the promise of becoming a central tool on the path to sustainability

Schroedinger operators and evolutionary strategies

International Nuclear Information System (INIS)

Asselmeyer, T.

1997-01-01

First we introduce a simple model for the description of evolutionary algorithms, which is based on 2nd order partial differential equations for the distribution function of the individuals. Then we turn to the properties of Boltzmann's and Darwin's strategy. the next chapter is dedicated to the mathematical properties of Schroedinger operators. Both statements on the spectral density and their reproducibility during the simulation are summarized. The remaining of this chapter are dedicated to the analysis of the kernel as well as the dependence of the Schroedinger operator on the potential. As conclusion from the results of this chapter we obtain the classification of the strategies in dependence of the fitness. We obtain the classification of the evolutionary strategies, which are described by a 2nd order partial differential equation, in relation to their solution behaviour. Thereafter we are employed with the variation of the mutation distribution

A New Kind of Void Soap-free P(MMA-EA-MAA) Latex Particles

Institute of Scientific and Technical Information of China (English)

Kai KANG; Cheng You KAN; Yi DU; Yu Zhong LI; De Shan LIU

2005-01-01

Soap-free P(MMA-EA-MAA) particles with narrow size distribution were synthesized by seeded emulsion polymerization of methyl methacrylate (MMA), ethyl acrylate (EA) and methacrylic acid (MAA), and large voids inside the particles were generated by alkali posttreatment in the presence of 2-butanone. Results indicated that the size of void and the particle volume were related with the amount of 2-butanone. The generation mechanism of voids was proposed.

Optimizing a reconfigurable material via evolutionary computation

Science.gov (United States)

Wilken, Sam; Miskin, Marc Z.; Jaeger, Heinrich M.

2015-08-01

Rapid prototyping by combining evolutionary computation with simulations is becoming a powerful tool for solving complex design problems in materials science. This method of optimization operates in a virtual design space that simulates potential material behaviors and after completion needs to be validated by experiment. However, in principle an evolutionary optimizer can also operate on an actual physical structure or laboratory experiment directly, provided the relevant material parameters can be accessed by the optimizer and information about the material's performance can be updated by direct measurements. Here we provide a proof of concept of such direct, physical optimization by showing how a reconfigurable, highly nonlinear material can be tuned to respond to impact. We report on an entirely computer controlled laboratory experiment in which a 6 ×6 grid of electromagnets creates a magnetic field pattern that tunes the local rigidity of a concentrated suspension of ferrofluid and iron filings. A genetic algorithm is implemented and tasked to find field patterns that minimize the force transmitted through the suspension. Searching within a space of roughly 1010 possible configurations, after testing only 1500 independent trials the algorithm identifies an optimized configuration of layered rigid and compliant regions.

Multi-objective evolutionary optimisation for product design and manufacturing

CERN Document Server

2011-01-01

Presents state-of-the-art research in the area of multi-objective evolutionary optimisation for integrated product design and manufacturing Provides a comprehensive review of the literature Gives in-depth descriptions of recently developed innovative and novel methodologies, algorithms and systems in the area of modelling, simulation and optimisation

Genetic evolutionary taboo search for optimal marker placement in infrared patient setup

International Nuclear Information System (INIS)

Riboldi, M; Baroni, G; Spadea, M F; Tagaste, B; Garibaldi, C; Cambria, R; Orecchia, R; Pedotti, A

2007-01-01

In infrared patient setup adequate selection of the external fiducial configuration is required for compensating inner target displacements (target registration error, TRE). Genetic algorithms (GA) and taboo search (TS) were applied in a newly designed approach to optimal marker placement: the genetic evolutionary taboo search (GETS) algorithm. In the GETS paradigm, multiple solutions are simultaneously tested in a stochastic evolutionary scheme, where taboo-based decision making and adaptive memory guide the optimization process. The GETS algorithm was tested on a group of ten prostate patients, to be compared to standard optimization and to randomly selected configurations. The changes in the optimal marker configuration, when TRE is minimized for OARs, were specifically examined. Optimal GETS configurations ensured a 26.5% mean decrease in the TRE value, versus 19.4% for conventional quasi-Newton optimization. Common features in GETS marker configurations were highlighted in the dataset of ten patients, even when multiple runs of the stochastic algorithm were performed. Including OARs in TRE minimization did not considerably affect the spatial distribution of GETS marker configurations. In conclusion, the GETS algorithm proved to be highly effective in solving the optimal marker placement problem. Further work is needed to embed site-specific deformation models in the optimization process

Learning from nature: Nature-inspired algorithms

DEFF Research Database (Denmark)

Albeanu, Grigore; Madsen, Henrik; Popentiu-Vladicescu, Florin

2016-01-01

.), genetic and evolutionary strategies, artificial immune systems etc. Well-known examples of applications include: aircraft wing design, wind turbine design, bionic car, bullet train, optimal decisions related to traffic, appropriate strategies to survive under a well-adapted immune system etc. Based......During last decade, the nature has inspired researchers to develop new algorithms. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees' algorithm, bat algorithm, firefly algorithm etc...... on collective social behaviour of organisms, researchers have developed optimization strategies taking into account not only the individuals, but also groups and environment. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms...

Business Architecture Development at Public Administration - Insights from Government EA Method Engineering Project in Finland

Science.gov (United States)

Valtonen, Katariina; Leppänen, Mauri

Governments worldwide are concerned for efficient production of services to customers. To improve quality of services and to make service production more efficient, information and communication technology (ICT) is largely exploited in public administration (PA). Succeeding in this exploitation calls for large-scale planning which embraces issues from strategic to technological level. In this planning the notion of enterprise architecture (EA) is commonly applied. One of the sub-architectures of EA is business architecture (BA). BA planning is challenging in PA due to a large number of stakeholders, a wide set of customers, and solid and hierarchical structures of organizations. To support EA planning in Finland, a project to engineer a government EA (GEA) method was launched. In this chapter, we analyze the discussions and outputs of the project workshops and reflect emerged issues on current e-government literature. We bring forth insights into and suggestions for government BA and its development.

Advances of evolutionary computation methods and operators

CERN Document Server

Cuevas, Erik; Oliva Navarro, Diego Alberto

2016-01-01

The goal of this book is to present advances that discuss alternative Evolutionary Computation (EC) developments and non-conventional operators which have proved to be effective in the solution of several complex problems. The book has been structured so that each chapter can be read independently from the others. The book contains nine chapters with the following themes: 1) Introduction, 2) the Social Spider Optimization (SSO), 3) the States of Matter Search (SMS), 4) the collective animal behavior (CAB) algorithm, 5) the Allostatic Optimization (AO) method, 6) the Locust Search (LS) algorithm, 7) the Adaptive Population with Reduced Evaluations (APRE) method, 8) the multimodal CAB, 9) the constrained SSO method.

Optimization of the p-xylene oxidation process by a multi-objective differential evolution algorithm with adaptive parameters co-derived with the population-based incremental learning algorithm

Science.gov (United States)

Guo, Zhan; Yan, Xuefeng

2018-04-01

Different operating conditions of p-xylene oxidation have different influences on the product, purified terephthalic acid. It is necessary to obtain the optimal combination of reaction conditions to ensure the quality of the products, cut down on consumption and increase revenues. A multi-objective differential evolution (MODE) algorithm co-evolved with the population-based incremental learning (PBIL) algorithm, called PBMODE, is proposed. The PBMODE algorithm was designed as a co-evolutionary system. Each individual has its own parameter individual, which is co-evolved by PBIL. PBIL uses statistical analysis to build a model based on the corresponding symbiotic individuals of the superior original individuals during the main evolutionary process. The results of simulations and statistical analysis indicate that the overall performance of the PBMODE algorithm is better than that of the compared algorithms and it can be used to optimize the operating conditions of the p-xylene oxidation process effectively and efficiently.

Evolutionary computation techniques a comparative perspective

CERN Document Server

Cuevas, Erik; Oliva, Diego

2017-01-01

This book compares the performance of various evolutionary computation (EC) techniques when they are faced with complex optimization problems extracted from different engineering domains. Particularly focusing on recently developed algorithms, it is designed so that each chapter can be read independently. Several comparisons among EC techniques have been reported in the literature, however, they all suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. In each chapter, a complex engineering optimization problem is posed, and then a particular EC technique is presented as the best choice, according to its search characteristics. Lastly, a set of experiments is conducted in order to compare its performance to other popular EC methods.

Trends in Governmental Enterprise Architecture: Reviewing National EA Programs

DEFF Research Database (Denmark)

Gøtze, John; Christiansen, Peter Engelund

2007-01-01

This article is the first in a series of three, where we will analyze and evaluate governmental enterprise architecture policies and practices from an international perspective. This first article presents the overall findings from an international study, we have made about governmental EA, and i...

Inclusive pseudodata for eA collisions at the LHeC

CERN Document Server

Armesto, N

2010-01-01

I explain how the pseudodata for eA collisions are generated, for the reduced cross section, $F_2$, $F_L$ and the corresponding heavy flavor decompositions. I also discuss how the uncertainties are computed.

Analysis of Population Diversity of Dynamic Probabilistic Particle Swarm Optimization Algorithms

Directory of Open Access Journals (Sweden)

Qingjian Ni

2014-01-01

Full Text Available In evolutionary algorithm, population diversity is an important factor for solving performance. In this paper, combined with some population diversity analysis methods in other evolutionary algorithms, three indicators are introduced to be measures of population diversity in PSO algorithms, which are standard deviation of population fitness values, population entropy, and Manhattan norm of standard deviation in population positions. The three measures are used to analyze the population diversity in a relatively new PSO variant—Dynamic Probabilistic Particle Swarm Optimization (DPPSO. The results show that the three measure methods can fully reflect the evolution of population diversity in DPPSO algorithms from different angles, and we also discuss the impact of population diversity on the DPPSO variants. The relevant conclusions of the population diversity on DPPSO can be used to analyze, design, and improve the DPPSO algorithms, thus improving optimization performance, which could also be beneficial to understand the working mechanism of DPPSO theoretically.

Quantum Genetic Algorithms for Computer Scientists

OpenAIRE

Lahoz Beltrá, Rafael

2016-01-01

Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as “Quantum Geneti...

EAS vahendas mullu Harjumaale 47 miljonit krooni toetusi / Allar Korjas

Index Scriptorium Estoniae

Korjas, Allar

2005-01-01

Möödunud aastal jõudis EAS- i vahendusel alustavate ja tegutsevate ettevõtete ning regionaalse arengu toetamiseks Harjumaale 47,1 miljonit krooni. Tabel. Vt. samas: Aare Ets. Regionaalne koostöö Harjumaal on oluline

A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

Energy Technology Data Exchange (ETDEWEB)

Tumuluru, Jaya Shankar [Idaho National Laboratory; McCulloch, Richard Chet James [Idaho National Laboratory

2015-07-01

In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

A conceptual evolutionary aseismic decision support framework for hospitals

Science.gov (United States)

Hu, Yufeng; Dargush, Gary F.; Shao, Xiaoyun

2012-12-01

In this paper, aconceptual evolutionary framework for aseismic decision support for hospitalsthat attempts to integrate a range of engineering and sociotechnical models is presented. Genetic algorithms are applied to find the optimal decision sets. A case study is completed to demonstrate how the frameworkmay applytoa specific hospital.The simulations show that the proposed evolutionary decision support framework is able to discover robust policy sets in either uncertain or fixed environments. The framework also qualitatively identifies some of the characteristicbehavior of the critical care organization. Thus, by utilizing the proposedframework, the decision makers are able to make more informed decisions, especially toenhance the seismic safety of the hospitals.

A tabu search evalutionary algorithm for multiobjective optimization: Application to a bi-criterion aircraft structural reliability problem

Science.gov (United States)

Long, Kim Chenming

Real-world engineering optimization problems often require the consideration of multiple conflicting and noncommensurate objectives, subject to nonconvex constraint regions in a high-dimensional decision space. Further challenges occur for combinatorial multiobjective problems in which the decision variables are not continuous. Traditional multiobjective optimization methods of operations research, such as weighting and epsilon constraint methods, are ill-suited to solving these complex, multiobjective problems. This has given rise to the application of a wide range of metaheuristic optimization algorithms, such as evolutionary, particle swarm, simulated annealing, and ant colony methods, to multiobjective optimization. Several multiobjective evolutionary algorithms have been developed, including the strength Pareto evolutionary algorithm (SPEA) and the non-dominated sorting genetic algorithm (NSGA), for determining the Pareto-optimal set of non-dominated solutions. Although numerous researchers have developed a wide range of multiobjective optimization algorithms, there is a continuing need to construct computationally efficient algorithms with an improved ability to converge to globally non-dominated solutions along the Pareto-optimal front for complex, large-scale, multiobjective engineering optimization problems. This is particularly important when the multiple objective functions and constraints of the real-world system cannot be expressed in explicit mathematical representations. This research presents a novel metaheuristic evolutionary algorithm for complex multiobjective optimization problems, which combines the metaheuristic tabu search algorithm with the evolutionary algorithm (TSEA), as embodied in genetic algorithms. TSEA is successfully applied to bicriteria (i.e., structural reliability and retrofit cost) optimization of the aircraft tail structure fatigue life, which increases its reliability by prolonging fatigue life. A comparison for this

Design of synthetic biological logic circuits based on evolutionary algorithm.

Science.gov (United States)

Chuang, Chia-Hua; Lin, Chun-Liang; Chang, Yen-Chang; Jennawasin, Tanagorn; Chen, Po-Kuei

2013-08-01

The construction of an artificial biological logic circuit using systematic strategy is recognised as one of the most important topics for the development of synthetic biology. In this study, a real-structured genetic algorithm (RSGA), which combines general advantages of the traditional real genetic algorithm with those of the structured genetic algorithm, is proposed to deal with the biological logic circuit design problem. A general model with the cis-regulatory input function and appropriate promoter activity functions is proposed to synthesise a wide variety of fundamental logic gates such as NOT, Buffer, AND, OR, NAND, NOR and XOR. The results obtained can be extended to synthesise advanced combinational and sequential logic circuits by topologically distinct connections. The resulting optimal design of these logic gates and circuits are established via the RSGA. The in silico computer-based modelling technology has been verified showing its great advantages in the purpose.

Study on the evolutionary optimisation of the topology of network control systems

Science.gov (United States)

Zhou, Zude; Chen, Benyuan; Wang, Hong; Fan, Zhun

2010-08-01

Computer networks have been very popular in enterprise applications. However, optimisation of network designs that allows networks to be used more efficiently in industrial environment and enterprise applications remains an interesting research topic. This article mainly discusses the topology optimisation theory and methods of the network control system based on switched Ethernet in an industrial context. Factors that affect the real-time performance of the industrial control network are presented in detail, and optimisation criteria with their internal relations are analysed. After the definition of performance parameters, the normalised indices for the evaluation of the topology optimisation are proposed. The topology optimisation problem is formulated as a multi-objective optimisation problem and the evolutionary algorithm is applied to solve it. Special communication characteristics of the industrial control network are considered in the optimisation process. In respect to the evolutionary algorithm design, an improved arena algorithm is proposed for the construction of the non-dominated set of the population. In addition, for the evaluation of individuals, the integrated use of the dominative relation method and the objective function combination method, for reducing the computational cost of the algorithm, are given. Simulation tests show that the performance of the proposed algorithm is preferable and superior compared to other algorithms. The final solution greatly improves the following indices: traffic localisation, traffic balance and utilisation rate balance of switches. In addition, a new performance index with its estimation process is proposed.

MODELING AND DESIGN STUDY USING HFC-236EA AS AN ALTERNATIVE REFRIGERANT IN A CENTRIFUGAL COMPRESSOR

Science.gov (United States)

The report gives results of an investigation of the operation of a centrifugal compressor--part of a chlorofluorocarbon (CFC)-114 chiller installation--with the new refrigerant hydrofluorocarbon (HFC)-236ea, a proposed alternative to CFC-114. A large set of CFC-236ea operating da...

Virtual Enterprise Risk Management Using Artificial Intelligence

Directory of Open Access Journals (Sweden)

Hanning Chen

2010-01-01

Full Text Available Virtual enterprise (VE has to manage its risk effectively in order to guarantee the profit. However, restricting the risk in a VE to the acceptable level is considered difficult due to the agility and diversity of its distributed characteristics. First, in this paper, an optimization model for VE risk management based on distributed decision making model is introduced. This optimization model has two levels, namely, the top model and the base model, which describe the decision processes of the owner and the partners of the VE, respectively. In order to solve the proposed model effectively, this work then applies two powerful artificial intelligence optimization techniques known as evolutionary algorithms (EA and swarm intelligence (SI. Experiments present comparative studies on the VE risk management problem for one EA and three state-of-the-art SI algorithms. All of the algorithms are evaluated against a test scenario, in which the VE is constructed by one owner and different partners. The simulation results show that the PS2O algorithm, which is a recently developed SI paradigm simulating symbiotic coevolution behavior in nature, obtains the superior solution for VE risk management problem than the other algorithms in terms of optimization accuracy and computation robustness.

A comparative analysis of particle swarm optimization and differential evolution algorithms for parameter estimation in nonlinear dynamic systems

International Nuclear Information System (INIS)

Banerjee, Amit; Abu-Mahfouz, Issam

2014-01-01

The use of evolutionary algorithms has been popular in recent years for solving the inverse problem of identifying system parameters given the chaotic response of a dynamical system. The inverse problem is reformulated as a minimization problem and population-based optimizers such as evolutionary algorithms have been shown to be efficient solvers of the minimization problem. However, to the best of our knowledge, there has been no published work that evaluates the efficacy of using the two most popular evolutionary techniques – particle swarm optimization and differential evolution algorithm, on a wide range of parameter estimation problems. In this paper, the two methods along with their variants (for a total of seven algorithms) are applied to fifteen different parameter estimation problems of varying degrees of complexity. Estimation results are analyzed using nonparametric statistical methods to identify if an algorithm is statistically superior to others over the class of problems analyzed. Results based on parameter estimation quality suggest that there are significant differences between the algorithms with the newer, more sophisticated algorithms performing better than their canonical versions. More importantly, significant differences were also found among variants of the particle swarm optimizer and the best performing differential evolution algorithm

Automatic Circuit Design and Optimization Using Modified PSO Algorithm

Directory of Open Access Journals (Sweden)

Subhash Patel

2016-04-01

Full Text Available In this work, we have proposed modified PSO algorithm based optimizer for automatic circuit design. The performance of the modified PSO algorithm is compared with two other evolutionary algorithms namely ABC algorithm and standard PSO algorithm by designing two stage CMOS operational amplifier and bulk driven OTA in 130nm technology. The results show the robustness of the proposed algorithm. With modified PSO algorithm, the average design error for two stage op-amp is only 0.054% in contrast to 3.04% for standard PSO algorithm and 5.45% for ABC algorithm. For bulk driven OTA, average design error is 1.32% with MPSO compared to 4.70% with ABC algorithm and 5.63% with standard PSO algorithm.

A Parallel Adaptive Particle Swarm Optimization Algorithm for Economic/Environmental Power Dispatch

Directory of Open Access Journals (Sweden)

Jinchao Li

2012-01-01

Full Text Available A parallel adaptive particle swarm optimization algorithm (PAPSO is proposed for economic/environmental power dispatch, which can overcome the premature characteristic, the slow-speed convergence in the late evolutionary phase, and lacking good direction in particles’ evolutionary process. A search population is randomly divided into several subpopulations. Then for each subpopulation, the optimal solution is searched synchronously using the proposed method, and thus parallel computing is realized. To avoid converging to a local optimum, a crossover operator is introduced to exchange the information among the subpopulations and the diversity of population is sustained simultaneously. Simulation results show that the proposed algorithm can effectively solve the economic/environmental operation problem of hydropower generating units. Performance comparisons show that the solution from the proposed method is better than those from the conventional particle swarm algorithm and other optimization algorithms.

Markov Networks in Evolutionary Computation

CERN Document Server

Shakya, Siddhartha

2012-01-01

Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs).  EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis. This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models. All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current researc...

Search for Anti-EA(D Antibodies in Subjects with an “Isolated VCA IgG” Pattern

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Massimo De Paschale

2010-01-01

Full Text Available The presence of an “isolated viral capsid antigen (VCA IgG” pattern in serum is not easy to interpret without the aid of further tests, such as specific immunoblotting or a virus genome search, that often give rise to organisational and economic problems. However, one alternative is to use an enzyme-linked immunosorbent assay (ELISA to detect anti-early antigen (EA antibodies, which can be found in about 85% of subjects with acute Epstein-Barr virus (EBV infections. The purpose of this work was to search for anti-EA(D antibodies in 130 samples with an isolated VCA IgG pattern at ELISA screening and classified as being indicative of past (102 cases or acute (28 cases infection on the basis of the immunoblotting results. Thirty-seven samples (28.5% were positive for anti-EA(D, of which 25 (89.3% had been classified by immunoblotting as indicating acute and 12 (11.8% past EBV infection. This difference was statistically significant (<.01. The results of our search for anti-EA(D antibodies correctly identified nearly 90% of acute (presence or past EBV infections (absence. When other tests are not available, the search for anti-EA antibodies may therefore be helpful in diagnosing patients with an isolated VCA IgG pattern at screening tests.

Preequilibrium light particle emission in heavy ion collisions for E/A between 10 and 20 MeV/A

International Nuclear Information System (INIS)

Kailas, S.

1989-01-01

It is known that when two heavy ions collide, while the interaction process is dominated by nucleus-nucleus mean field effects at lower energies (E/A < 10Me V), the nucleon-nucleon collision aspects dominate at higher energies (E/A <100 MeV) . In the intermediate E/A region, both the above mentioned effects are important t o varying degrees. Many experiments have shown that when the incident energy excee ds 10 MeV/A, the complete fusion (CF) of target and projectile gives way to an inco mplete fusion (ICF) mechanism associated with preequilibrium light particle (PELP) emmi sion. This phenomenon becomes increasingly important as E increases and at E/A < 20 Me V, the cross section for PELP emission becomes comparable to the reaction cross section . Considerable progress has been made in the last decade towards understanding of ICF and PELP. In the present work, the review of this field is mainly restricted to E/A values lying between 10 and 20 MeV/A. (author). 27 refs., 5 figs

Separation and properties of EA-rosette-forming lymphocytes in humans

NARCIS (Netherlands)

van Oers, M. H.; Zeijlemaker, W. P.; Schellekens, P. T.

1977-01-01

Human peripheral blood lymphocytes were separated into subpopulations enriched or depleted with respect to B lymphocytes (Ig-bearing cells), T lymphocytes, (cell forming rosettes with sheep erythrocytes: E-RFC) and Fc receptor-bearing lymphocytes (EA-RFC). From the distributions and recoveries of

Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation

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Schell Thomas

2003-01-01

Full Text Available In image compression, the wavelet transformation is a state-of-the-art component. Recently, wavelet packet decomposition has received quite an interest. A popular approach for wavelet packet decomposition is the near-best-basis algorithm using nonadditive cost functions. In contrast to additive cost functions, the wavelet packet decomposition of the near-best-basis algorithm is only suboptimal. We apply methods from the field of evolutionary computation (EC to test the quality of the near-best-basis results. We observe a phenomenon: the results of the near-best-basis algorithm are inferior in terms of cost-function optimization but are superior in terms of rate/distortion performance compared to EC methods.

ACC/AHA guidelines superior to ESC/EAS guidelines for primary prevention with statins in non-diabetic Europeans

DEFF Research Database (Denmark)

Mortensen, Martin Bødtker; Nordestgaard, Børge G; Afzal, Shoaib

2017-01-01

Aim We compared the 2013 American College of Cardiology/American Heart Association (ACC/AHA) and the 2016 European Society of Cardiology/European Atherosclerosis Society (ESC/EAS) guidelines on prevention of atherosclerotic cardiovascular disease (ASCVD) using different risk prediction models [US......-calibrated around decision thresholds for statin therapy. For a Class I recommendation, 42% of individuals qualified for statins using the ACC/AHA guidelines vs. 6% with the ESC/EAS guidelines. Using ACC/AHA- vs. ESC/EAS-defined statin eligibility led to a substantial gain in sensitivity (+62% for any ASCVD and+76......% for fatal ASCVD) with a smaller loss in specificity (-35% for any ASCVD and -36% for fatal ASCVD). Similar differences between the ACC/AHA and ESC/EAS guidelines were found for men and women separately, and for Class IIa recommendations. The sensitivity and specificity of a US-PCE risk of 5% were similar...

Improving Polyp Detection Algorithms for CT Colonography: Pareto Front Approach.

Science.gov (United States)

Huang, Adam; Li, Jiang; Summers, Ronald M; Petrick, Nicholas; Hara, Amy K

2010-03-21

We investigated a Pareto front approach to improving polyp detection algorithms for CT colonography (CTC). A dataset of 56 CTC colon surfaces with 87 proven positive detections of 53 polyps sized 4 to 60 mm was used to evaluate the performance of a one-step and a two-step curvature-based region growing algorithm. The algorithmic performance was statistically evaluated and compared based on the Pareto optimal solutions from 20 experiments by evolutionary algorithms. The false positive rate was lower (pPareto optimization process can effectively help in fine-tuning and redesigning polyp detection algorithms.

Coevolution of Artificial Agents Using Evolutionary Computation in Bargaining Game

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Sangwook Lee

2015-01-01

Full Text Available Analysis of bargaining game using evolutionary computation is essential issue in the field of game theory. This paper investigates the interaction and coevolutionary process among heterogeneous artificial agents using evolutionary computation (EC in the bargaining game. In particular, the game performance with regard to payoff through the interaction and coevolution of agents is studied. We present three kinds of EC based agents (EC-agent participating in the bargaining game: genetic algorithm (GA, particle swarm optimization (PSO, and differential evolution (DE. The agents’ performance with regard to changing condition is compared. From the simulation results it is found that the PSO-agent is superior to the other agents.

75 FR 62133 - Notice of Availability of Final Environmental Assessment (FINAL EA) and a Finding of No...

Science.gov (United States)

2010-10-07

... Availability of Final Environmental Assessment (FINAL EA) and a Finding of No Significant Impact (FONSI) for... of No Significant Impact (FONSI) for Land Purchase, Access Road Construction and Access Tunnel... Impact (FONSI) based on the Final Environmental Assessment (FINAL EA) for Land Purchase, Access Road...

Rapid maximum likelihood ancestral state reconstruction of continuous characters: A rerooting-free algorithm.

Science.gov (United States)

Goolsby, Eric W

2017-04-01

Ancestral state reconstruction is a method used to study the evolutionary trajectories of quantitative characters on phylogenies. Although efficient methods for univariate ancestral state reconstruction under a Brownian motion model have been described for at least 25 years, to date no generalization has been described to allow more complex evolutionary models, such as multivariate trait evolution, non-Brownian models, missing data, and within-species variation. Furthermore, even for simple univariate Brownian motion models, most phylogenetic comparative R packages compute ancestral states via inefficient tree rerooting and full tree traversals at each tree node, making ancestral state reconstruction extremely time-consuming for large phylogenies. Here, a computationally efficient method for fast maximum likelihood ancestral state reconstruction of continuous characters is described. The algorithm has linear complexity relative to the number of species and outperforms the fastest existing R implementations by several orders of magnitude. The described algorithm is capable of performing ancestral state reconstruction on a 1,000,000-species phylogeny in fewer than 2 s using a standard laptop, whereas the next fastest R implementation would take several days to complete. The method is generalizable to more complex evolutionary models, such as phylogenetic regression, within-species variation, non-Brownian evolutionary models, and multivariate trait evolution. Because this method enables fast repeated computations on phylogenies of virtually any size, implementation of the described algorithm can drastically alleviate the computational burden of many otherwise prohibitively time-consuming tasks requiring reconstruction of ancestral states, such as phylogenetic imputation of missing data, bootstrapping procedures, Expectation-Maximization algorithms, and Bayesian estimation. The described ancestral state reconstruction algorithm is implemented in the Rphylopars

Multidimensional extended spatial evolutionary games.

Science.gov (United States)

Krześlak, Michał; Świerniak, Andrzej

2016-02-01

The goal of this paper is to study the classical hawk-dove model using mixed spatial evolutionary games (MSEG). In these games, played on a lattice, an additional spatial layer is introduced for dependence on more complex parameters and simulation of changes in the environment. Furthermore, diverse polymorphic equilibrium points dependent on cell reproduction, model parameters, and their simulation are discussed. Our analysis demonstrates the sensitivity properties of MSEGs and possibilities for further development. We discuss applications of MSEGs, particularly algorithms for modelling cell interactions during the development of tumours. Copyright © 2015 Elsevier Ltd. All rights reserved.

A Food Chain Algorithm for Capacitated Vehicle Routing Problem with Recycling in Reverse Logistics

Science.gov (United States)

Song, Qiang; Gao, Xuexia; Santos, Emmanuel T.

2015-12-01

This paper introduces the capacitated vehicle routing problem with recycling in reverse logistics, and designs a food chain algorithm for it. Some illustrative examples are selected to conduct simulation and comparison. Numerical results show that the performance of the food chain algorithm is better than the genetic algorithm, particle swarm optimization as well as quantum evolutionary algorithm.

Optimal Design of a Centrifugal Compressor Impeller Using Evolutionary Algorithms