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Sample records for genetic algorithm approach

  1. A genetic engineering approach to genetic algorithms.

    Science.gov (United States)

    Gero, J S; Kazakov, V

    2001-01-01

    We present an extension to the standard genetic algorithm (GA), which is based on concepts of genetic engineering. The motivation is to discover useful and harmful genetic materials and then execute an evolutionary process in such a way that the population becomes increasingly composed of useful genetic material and increasingly free of the harmful genetic material. Compared to the standard GA, it provides some computational advantages as well as a tool for automatic generation of hierarchical genetic representations specifically tailored to suit certain classes of problems.

  2. Genetic Algorithm Approaches for Actuator Placement

    Science.gov (United States)

    Crossley, William A.

    2000-01-01

    This research investigated genetic algorithm approaches for smart actuator placement to provide aircraft maneuverability without requiring hinged flaps or other control surfaces. The effort supported goals of the Multidisciplinary Design Optimization focus efforts in NASA's Aircraft au program. This work helped to properly identify various aspects of the genetic algorithm operators and parameters that allow for placement of discrete control actuators/effectors. An improved problem definition, including better definition of the objective function and constraints, resulted from this research effort. The work conducted for this research used a geometrically simple wing model; however, an increasing number of potential actuator placement locations were incorporated to illustrate the ability of the GA to determine promising actuator placement arrangements. This effort's major result is a useful genetic algorithm-based approach to assist in the discrete actuator/effector placement problem.

  3. A novel mating approach for genetic algorithms.

    Science.gov (United States)

    Galán, Severino F; Mengshoel, Ole J; Pinter, Rafael

    2013-01-01

    Genetic algorithms typically use crossover, which relies on mating a set of selected parents. As part of crossover, random mating is often carried out. A novel approach to parent mating is presented in this work. Our novel approach can be applied in combination with a traditional similarity-based criterion to measure distance between individuals or with a fitness-based criterion. We introduce a parameter called the mating index that allows different mating strategies to be developed within a uniform framework: an exploitative strategy called best-first, an explorative strategy called best-last, and an adaptive strategy called self-adaptive. Self-adaptive mating is defined in the context of the novel algorithm, and aims to achieve a balance between exploitation and exploration in a domain-independent manner. The present work formally defines the novel mating approach, analyzes its behavior, and conducts an extensive experimental study to quantitatively determine its benefits. In the domain of real function optimization, the experiments show that, as the degree of multimodality of the function at hand grows, increasing the mating index improves performance. In the case of the self-adaptive mating strategy, the experiments give strong results for several case studies.

  4. Stego-audio Using Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    V. Santhi

    2014-06-01

    Full Text Available With the rapid development of digital multimedia applications, the secure data transmission becomes the main issue in data communication system. So the multimedia data hiding techniques have been developed to ensure the secured data transfer. Steganography is an art of hiding a secret message within an image/audio/video file in such a way that the secret message cannot be perceived by hacker/intruder. In this study, we use RSA encryption algorithm to encrypt the message and Genetic Algorithm (GA to encode the message in the audio file. This study presents a method to access the negative audio bytes and includes the negative audio bytes in the message encoding and position embedding process. This increases the capacity of encoding message in the audio file. The use of GA operators in Genetic Algorithm reduces the noise distortions.

  5. Integrating Genetic Algorithm, Tabu Search Approach for Job Shop Scheduling

    CERN Document Server

    Thamilselvan, R

    2009-01-01

    This paper presents a new algorithm based on integrating Genetic Algorithms and Tabu Search methods to solve the Job Shop Scheduling problem. The idea of the proposed algorithm is derived from Genetic Algorithms. Most of the scheduling problems require either exponential time or space to generate an optimal answer. Job Shop scheduling (JSS) is the general scheduling problem and it is a NP-complete problem, but it is difficult to find the optimal solution. This paper applies Genetic Algorithms and Tabu Search for Job Shop Scheduling problem and compares the results obtained by each. With the implementation of our approach the JSS problems reaches optimal solution and minimize the makespan.

  6. Cognitive Radio — Genetic Algorithm Approach

    Science.gov (United States)

    Reddy, Y. B.

    2005-03-01

    Cognitive Radio (CR) is relatively a new technology, which intelligently detects a particular segment of the radio spectrum currently in use and selects unused spectrum quickly without interfering the transmission of authorized users. Cognitive Radios can learn about current use of spectrum in their operating area, make intelligent decisions, and react to immediate changes in the use of spectrum by other authorized users. The goal of CR technology is to relieve radio spectrum overcrowding, which actually translates to a lack of access to full radio spectrum utilization. Due to this adaptive behavior, the CR can easily avoid the interference of signals in a crowded radio frequency spectrum. In this research, we discuss the possible application of genetic algorithms (GA) to create a CR that can respond intelligently in changing and unanticipated circumstances and in the presence of hostile jammers and interferers. Genetic algorithms are problem solving techniques based on evolution and natural selection. GA models adapt Charles Darwin's evolutionary theory for analysis of data and interchanging design elements in hundreds of thousands of different combinations. Only the best-performing combinations are permitted to survive, and those combinations "reproduce" further, progressively yielding better and better results.

  7. Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling

    Science.gov (United States)

    Lohn, Jason; Colombano, Silvano

    1997-01-01

    We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.

  8. Genetic algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steven E.

    1991-01-01

    Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

  9. An Airborne Conflict Resolution Approach Using a Genetic Algorithm

    Science.gov (United States)

    Mondoloni, Stephane; Conway, Sheila

    2001-01-01

    An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.

  10. A genetic algorithm approach to routine gamma spectra analysis

    Energy Technology Data Exchange (ETDEWEB)

    Carlevaro, C M [Instituto de FIsica de LIquidos y Sistemas Biologicos, Calle 59 No 789, B1900BTE La Plata (Argentina); Wilkinson, M V [Autoridad Regulatoria Nuclear, Avda. del Libertador 8250, C1429BNP Buenos Aires (Argentina); Barrios, L A [Autoridad Regulatoria Nuclear, Avda. del Libertador 8250, C1429BNP Buenos Aires (Argentina)

    2008-01-15

    In this work we present an alternative method for performing routine gamma spectra analysis based on genetic algorithm techniques. The main idea is to search for patterns of single nuclide spectra obtained by simulation in a sample spectrum targeted for analysis. We show how this approach is applied to the analysis of simulated and real target spectra, and also to the study of interference resolution.

  11. Discovering Fuzzy Censored Classification Rules (Fccrs: A Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Renu Bala

    2012-08-01

    Full Text Available Classification Rules (CRs are often discovered in the form of ‘If-Then’ Production Rules (PRs. PRs, beinghigh level symbolic rules, are comprehensible and easy to implement. However, they are not capable ofdealing with cognitive uncertainties like vagueness and ambiguity imperative to real word decision makingsituations. Fuzzy Classification Rules (FCRs based on fuzzy logic provide a framework for a flexiblehuman like reasoning involving linguistic variables. Moreover, a classification system consisting of simple‘If-Then’ rules is not competent in handling exceptional circumstances. In this paper, we propose aGenetic Algorithm approach to discover Fuzzy Censored Classification Rules (FCCRs. A FCCR is aFuzzy Classification Rule (FCRs augmented with censors. Here, censors are exceptional conditions inwhich the behaviour of a rule gets modified. The proposed algorithm works in two phases. In the firstphase, the Genetic Algorithm discovers Fuzzy Classification Rules. Subsequently, these FuzzyClassification Rules are mutated to produce FCCRs in the second phase. The appropriate encodingscheme, fitness function and genetic operators are designed for the discovery of FCCRs. The proposedapproach for discovering FCCRs is then illustrated on a synthetic dataset.

  12. A genetic algorithm approach to recognition and data mining

    Energy Technology Data Exchange (ETDEWEB)

    Punch, W.F.; Goodman, E.D.; Min, Pei [Michigan State Univ., East Lansing, MI (United States)] [and others

    1996-12-31

    We review here our use of genetic algorithm (GA) and genetic programming (GP) techniques to perform {open_quotes}data mining,{close_quotes} the discovery of particular/important data within large datasets, by finding optimal data classifications using known examples. Our first experiments concentrated on the use of a K-nearest neighbor algorithm in combination with a GA. The GA selected weights for each feature so as to optimize knn classification based on a linear combination of features. This combined GA-knn approach was successfully applied to both generated and real-world data. We later extended this work by substituting a GP for the GA. The GP-knn could not only optimize data classification via linear combinations of features but also determine functional relationships among the features. This allowed for improved performance and new information on important relationships among features. We review the effectiveness of the overall approach on examples from biology and compare the effectiveness of the GA and GP.

  13. Controlling Risk Exposure in Periodic Environments: A Genetic Algorithm Approach

    CERN Document Server

    Navarro, Emeterio

    2007-01-01

    In this paper, we compare the performance of different agent's investment strategies in an investment scenario with periodic returns and different types and levels of noise. We consider an investment model, where an agent decides the percentage of budget to risk at each time step. Afterwards, agent's investment is evaluated in the market via a return on investment (RoI), which we assume is a stochastic process with unknown periodicities and different levels of noise. To control the risk exposure, we investigate approaches based on: technical analysis (Moving Least Squares, MLS), and evolutionary computation (Genetic Algorithms, GA). In our comparison, we also consider two reference strategies for zero-knowledge and complete-knowledge behaviors, respectively. In our approach, the performance of a strategy corresponds to the average budget that can be obtained with this strategy over a certain number of time steps. To this end, we perform some computer experiments, where for each strategy the budget obtained af...

  14. Mobile transporter path planning using a genetic algorithm approach

    Science.gov (United States)

    Baffes, Paul; Wang, Lui

    1988-01-01

    The use of an optimization technique known as a genetic algorithm for solving the mobile transporter path planning problem is investigated. The mobile transporter is a traveling robotic vehicle proposed for the Space Station which must be able to reach any point of the structure autonomously. Specific elements of the genetic algorithm are explored in both a theoretical and experimental sense. Recent developments in genetic algorithm theory are shown to be particularly effective in a path planning problem domain, though problem areas can be cited which require more research. However, trajectory planning problems are common in space systems and the genetic algorithm provides an attractive alternative to the classical techniques used to solve these problems.

  15. Linkage intensity learning approach with genetic algorithm for causality diagram

    Institute of Scientific and Technical Information of China (English)

    WANG Cheng-liang; CHEN Juan-juan

    2007-01-01

    The causality diagram theory, which adopts graphical expression of knowledge and direct intensity of causality, overcomes some shortages in belief network and has evolved into a mixed causality diagram methodology for discrete and continuous variable. But to give linkage intensity of causality diagram is difficult, particularly in many working conditions in which sampling data are limited or noisy. The classic learning algorithm is hard to be adopted. We used genetic algorithm to learn linkage intensity from limited data. The simulation results demonstrate that this algorithm is more suitable than the classic algorithm in the condition of sample shortage such as space shuttle's fault diagnoisis.

  16. A genetic algorithm approach in interface and surface structure optimization

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Jian [Iowa State Univ., Ames, IA (United States)

    2010-01-01

    The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.

  17. Hierarchical Genetic Algorithm Approach to Determine Pulse Sequences in NMR

    CERN Document Server

    Ajoy, Ashok

    2009-01-01

    We develop a new class of genetic algorithm that computationally determines efficient pulse sequences to implement a quantum gate U in a three-qubit system. The method is shown to be quite general, and the same algorithm can be used to derive efficient sequences for a variety of target matrices. We demonstrate this by implementing the inversion-on-equality gate efficiently when the spin-spin coupling constants $J_{12}=J_{23}=J$ and $J_{13}=0$. We also propose new pulse sequences to implement the Parity gate and Fanout gate, which are about 50% more efficient than the previous best efforts. Moreover, these sequences are shown to require significantly less RF power for their implementation. The proposed algorithm introduces several new features in the conventional genetic algorithm framework. We use matrices instead of linear chains, and the columns of these matrices have a well defined hierarchy. The algorithm is a genetic algorithm coupled to a fast local optimizer, and is hence a hybrid GA. It shows fast con...

  18. Combining Single (Mixed) Metric Approach and Genetic Algorithm for QoS Routing Problem

    Institute of Scientific and Technical Information of China (English)

    胡世余; 谢剑英

    2004-01-01

    A hybrid algorithm for the delay constrained least cost path problem is proposed through combination of single (mixed) metric approach and genetic algorithm. Compared with the known genetic algorithm for the same problem, the new algorithm adopts integral coding scheme and new genetic operator, which reduces the search space and improves the efficiency of genetic operation. Meanwhile, the single (mixed) approach accelerates the convergence speed. Simulation results indicate that the proposed algorithm can find near-optimal even optimal solutions within moderate numbers of generations.

  19. A Genetic Algorithms Based Approach for Group Multicast Routing

    Directory of Open Access Journals (Sweden)

    Luca Sanna Randaccio

    2006-08-01

    Full Text Available Whereas multicast transmission in one-to-many communications allows the operator to drastically save network resources, it also makes the routing of the traffic flows more complex then in unicast transmissions. A huge amount of possible trees have to be considered and analyzed to find the appropriate routing paths. To address this problem, we propose the use of the genetic algorithms (GA, which considerably reduce the number of solutions to be evaluated. A heuristic procedure is first used to discern a set of possible trees for each multicast session in isolation. Then, the GA are applied to find the appropriate combination of the trees to comply with the bandwidth needs of the group of multicast sessions simultaneously. The goodness of each solution is assessed by means of an expression that weights both network bandwidth allocation and one-way delay. The resulting cost function is guided by few parameters that can be easily tuned during traffic engineering operations; an appropriate setting of these parameters allows the operator to configure the desired balance between network resource utilization and provided quality of service. Simulations have been performed to compare the proposed algorithm with alternative solutions in terms of bandwidth utilization and transmission delay.

  20. A Genetic-Algorithms-Based Approach for Programming Linear and Quadratic Optimization Problems with Uncertainty

    Directory of Open Access Journals (Sweden)

    Weihua Jin

    2013-01-01

    Full Text Available This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks. The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.

  1. Hybrid genetic algorithm approach for selective harmonic control

    Energy Technology Data Exchange (ETDEWEB)

    Dahidah, Mohamed S.A. [Faculty of Engineering, Multimedia University, 63100, Jalan Multimedia-Cyberjaya, Selangor (Malaysia); Agelidis, Vassilios G. [School of Electrical and Information Engineering, The University of Sydney, NSW (Australia); Rao, Machavaram V. [Faculty of Engineering and Technology, Multimedia University, 75450, Jalan Ayer Keroh Lama-Melaka (Malaysia)

    2008-02-15

    The paper presents an optimal solution for a selective harmonic elimination pulse width modulated (SHE-PWM) technique suitable for a high power inverter used in constant frequency utility applications. The main challenge of solving the associated non-linear equations, which are transcendental in nature and, therefore, have multiple solutions, is the convergence, and therefore, an initial point selected considerably close to the exact solution is required. The paper discusses an efficient hybrid real coded genetic algorithm (HRCGA) that reduces significantly the computational burden, resulting in fast convergence. An objective function describing a measure of the effectiveness of eliminating selected orders of harmonics while controlling the fundamental, namely a weighted total harmonic distortion (WTHD) is derived, and a comparison of different operating points is reported. It is observed that the method was able to find the optimal solution for a modulation index that is higher than unity. The theoretical considerations reported in this paper are verified through simulation and experimentally on a low power laboratory prototype. (author)

  2. A Fuzzy Genetic Algorithm Approach to an Adaptive Information Retrieval Agent.

    Science.gov (United States)

    Martin-Bautista, Maria J.; Vila, Maria-Amparo; Larsen, Henrik Legind

    1999-01-01

    Presents an approach to a Genetic Information Retrieval Agent Filter (GIRAF) that filters and ranks documents retrieved from the Internet according to users' preferences by using a Genetic Algorithm and fuzzy set theory to handle the imprecision of users' preferences and users' evaluation of the retrieved documents. (Author/LRW)

  3. A Genetic Algorithms-based Approach for Optimized Self-protection in a Pervasive Service Middleware

    DEFF Research Database (Denmark)

    Zhang, Weishan; Ingstrup, Mads; Hansen, Klaus Marius

    2009-01-01

    the constraints of heterogeneous devices and networks. In this paper, we present a Genetic Algorithms-based approach for obtaining optimized security configurations at run time, supported by a set of security OWL ontologies and an event-driven framework. This approach has been realized as a prototype for self...

  4. Genetic algorithm based image binarization approach and its quantitative evaluation via pooling

    Science.gov (United States)

    Hu, Huijun; Liu, Ya; Liu, Maofu

    2015-12-01

    The binarized image is very critical to image visual feature extraction, especially shape feature, and the image binarization approaches have been attracted more attentions in the past decades. In this paper, the genetic algorithm is applied to optimizing the binarization threshold of the strip steel defect image. In order to evaluate our genetic algorithm based image binarization approach in terms of quantity, we propose the novel pooling based evaluation metric, motivated by information retrieval community, to avoid the lack of ground-truth binary image. Experimental results show that our genetic algorithm based binarization approach is effective and efficiency in the strip steel defect images and our quantitative evaluation metric on image binarization via pooling is also feasible and practical.

  5. A Genetic Algorithm Approach to Nonlinear Least Squares Estimation

    Science.gov (United States)

    Olinsky, Alan D.; Quinn, John T.; Mangiameli, Paul M.; Chen, Shaw K.

    2004-01-01

    A common type of problem encountered in mathematics is optimizing nonlinear functions. Many popular algorithms that are currently available for finding nonlinear least squares estimators, a special class of nonlinear problems, are sometimes inadequate. They might not converge to an optimal value, or if they do, it could be to a local rather than…

  6. Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

    Science.gov (United States)

    Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R

    2012-08-01

    A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three

  7. A Detailed look of Audio Steganography Techniques using LSB and Genetic Algorithm Approach

    OpenAIRE

    Gunjan Nehru; Puja Dhar

    2012-01-01

    This paper is the study of various techniques of audio steganography using different algorithmis like genetic algorithm approach and LSB approach. We have tried some approaches that helps in audio steganography. As we know it is the art and science of writing hidden messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message, a form of security through obscurity. In steganography, the message used to hide secret message is called hos...

  8. A Genetic Algorithm Approach for Group Formation in Collaborative Learning Considering Multiple Student Characteristics

    Science.gov (United States)

    Moreno, Julian; Ovalle, Demetrio A.; Vicari, Rosa M.

    2012-01-01

    Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as…

  9. A Genetic Algorithm Approach for Group Formation in Collaborative Learning Considering Multiple Student Characteristics

    Science.gov (United States)

    Moreno, Julian; Ovalle, Demetrio A.; Vicari, Rosa M.

    2012-01-01

    Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as…

  10. A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

    Directory of Open Access Journals (Sweden)

    Dalton Meitei Thounaojam

    2016-01-01

    Full Text Available This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.

  11. A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.

    Science.gov (United States)

    Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta

    2016-01-01

    This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.

  12. A Genetic Algorithm-based Antenna Selection Approach for Large-but-Finite MIMO Networks

    KAUST Repository

    Makki, Behrooz

    2016-12-29

    We study the performance of antenna selectionbased multiple-input-multiple-output (MIMO) networks with large but finite number of transmit antennas and receivers. Considering the continuous and bursty communication scenarios with different users’ data request probabilities, we develop an efficient antenna selection scheme using genetic algorithms (GA). As demonstrated, the proposed algorithm is generic in the sense that it can be used in the cases with different objective functions, precoding methods, levels of available channel state information and channel models. Our results show that the proposed GAbased algorithm reaches (almost) the same throughput as the exhaustive search-based optimal approach, with substantially less implementation complexity.

  13. Solving an aggregate production planning problem by using multi-objective genetic algorithm (MOGA approach

    Directory of Open Access Journals (Sweden)

    Ripon Kumar Chakrabortty

    2013-01-01

    Full Text Available In hierarchical production planning system, Aggregate Production Planning (APP falls between the broad decisions of long-range planning and the highly specific and detailed short-range planning decisions. This study develops an interactive Multi-Objective Genetic Algorithm (MOGA approach for solving the multi-product, multi-period aggregate production planning (APP with forecasted demand, related operating costs, and capacity. The proposed approach attempts to minimize total costs with reference to inventory levels, labor levels, overtime, subcontracting and backordering levels, and labor, machine and warehouse capacity. Here several genetic algorithm parameters are considered for solving NP-hard problem (APP problem and their relative comparisons are focused to choose the most auspicious combination for solving multiple objective problems. An industrial case demonstrates the feasibility of applying the proposed approach to real APP decision problems. Consequently, the proposed MOGA approach yields an efficient APP compromise solution for large-scale problems.

  14. Improving the accuracy of density-functional theory calculation: the genetic algorithm and neural network approach.

    Science.gov (United States)

    Li, Hui; Shi, LiLi; Zhang, Min; Su, Zhongmin; Wang, XiuJun; Hu, LiHong; Chen, GuanHua

    2007-04-14

    The combination of genetic algorithm and neural network approach (GANN) has been developed to improve the calculation accuracy of density functional theory. As a demonstration, this combined quantum mechanical calculation and GANN correction approach has been applied to evaluate the optical absorption energies of 150 organic molecules. The neural network approach reduces the root-mean-square (rms) deviation of the calculated absorption energies of 150 organic molecules from 0.47 to 0.22 eV for the TDDFTB3LYP6-31G(d) calculation, and the newly developed GANN correction approach reduces the rms deviation to 0.16 eV.

  15. A Novel Approach for Discovery Quantitative Fuzzy Multi-Level Association Rules Mining Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Saad M. Darwish

    2016-10-01

    Full Text Available Quantitative multilevel association rules mining is a central field to realize motivating associations among data components with multiple levels abstractions. The problem of expanding procedures to handle quantitative data has been attracting the attention of many researchers. The algorithms regularly discretize the attribute fields into sharp intervals, and then implement uncomplicated algorithms established for Boolean attributes. Fuzzy association rules mining approaches are intended to defeat such shortcomings based on the fuzzy set theory. Furthermore, most of the current algorithms in the direction of this topic are based on very tiring search methods to govern the ideal support and confidence thresholds that agonize from risky computational cost in searching association rules. To accelerate quantitative multilevel association rules searching and escape the extreme computation, in this paper, we propose a new genetic-based method with significant innovation to determine threshold values for frequent item sets. In this approach, a sophisticated coding method is settled, and the qualified confidence is employed as the fitness function. With the genetic algorithm, a comprehensive search can be achieved and system automation is applied, because our model does not need the user-specified threshold of minimum support. Experiment results indicate that the recommended algorithm can powerfully generate non-redundant fuzzy multilevel association rules.

  16. An integrated approach to structural design of buildings using genetic algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Rafiq, M.Y.; Mathews, J.D. [Univ. of Plymouth (United Kingdom)

    1996-12-31

    This paper presents an evolutionary approach to the integration of design activities, in the area of structural design of buildings, using Genetic Algorithms (GA). Integration process is viewed in two contexts: (i) Integration across the design activities within a particular discipline, and (ii) Integration across of the disciplines involved in the design. Particular advantages of the integration of design activities during the conceptual stage of the design process are highlighted.

  17. Software For Genetic Algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steve E.

    1992-01-01

    SPLICER computer program is genetic-algorithm software tool used to solve search and optimization problems. Provides underlying framework and structure for building genetic-algorithm application program. Written in Think C.

  18. Branch-pipe-routing approach for ships using improved genetic algorithm

    Science.gov (United States)

    Sui, Haiteng; Niu, Wentie

    2016-09-01

    Branch-pipe routing plays fundamental and critical roles in ship-pipe design. The branch-pipe-routing problem is a complex combinatorial optimization problem and is thus difficult to solve when depending only on human experts. A modified genetic-algorithm-based approach is proposed in this paper to solve this problem. The simplified layout space is first divided into threedimensional (3D) grids to build its mathematical model. Branch pipes in layout space are regarded as a combination of several two-point pipes, and the pipe route between two connection points is generated using an improved maze algorithm. The coding of branch pipes is then defined, and the genetic operators are devised, especially the complete crossover strategy that greatly accelerates the convergence speed. Finally, simulation tests demonstrate the performance of proposed method.

  19. Optimization of Electrical System for Offshore Wind Farms via a Genetic Algorithm Approach

    DEFF Research Database (Denmark)

    Zhao, Menghua

    , and the LTC limitation of transformers, the power generation limits and the voltage operation range are considered as the constraints. The optimization method combined with probabilistic analysis is used to obtain the capacity of a given wind farm site. The OES-OWF is approached by Genetic Algorithm (GA...... to very different costs, system reliability, power quality, and power losses etc. Therefore, the optimization of electrical system design for offshore wind farms becomes more and more necessary. There are two tasks in this project: 1) the first one is to construct an algorithm for finding the capacity......). This platform is based on a knowledge database, and composed of several functional modules such as cost calculation, reliability evaluation, losses calculation, AC-DC integrated load flow algorithm etc. All these modules are based on a spreadsheet database which provides an interface for users to input...

  20. An Evolutionary Approach to Drug-Design Using a Novel Neighbourhood Based Genetic Algorithm

    CERN Document Server

    Ghosh, Arnab; Chowdhury, Arkabandhu; Konar, Amit

    2012-01-01

    The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein. The structure is represented as a tree where each non-empty node represents a functional group. It is assumed that the active site configuration of the target protein is known with position of the essential residues. In this paper the interaction energy of the ligands with the protein target is minimized. Moreover, the size of the tree is difficult to obtain and it will be different for different active sites. To overcome the difficulty, a variable tree size configuration is used for designing ligands. The optimization is done using a novel Neighbourhood Based Genetic Algorithm (NBGA) which uses dynamic neighbourhood topology. To get variable tree size, a variable-length version of the above algorithm is devised. To judge the merit of the algorithm, it is initially applied on the well known Travelling Salesman Problem (TSP).

  1. Pre-emptive resource-constrained multimode project scheduling using genetic algorithm: A dynamic forward approach

    Directory of Open Access Journals (Sweden)

    Aidin Delgoshaei

    2016-09-01

    Full Text Available Purpose: The issue resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating drawback is frequently seen after scheduling of a project in practice which causes a schedule to be useless. Modifying an over-allocated schedule is very complicated and needs a lot of efforts and time. In this paper, a new and fast tracking method is proposed to schedule large scale projects which can help project engineers to schedule the project rapidly and with more confidence. Design/methodology/approach: In this article, a forward approach for maximizing net present value (NPV in multi-mode resource constrained project scheduling problem while assuming discounted positive cash flows (MRCPSP-DCF is proposed. The progress payment method is used and all resources are considered as pre-emptible. The proposed approach maximizes NPV using unscheduled resources through resource calendar in forward mode. For this purpose, a Genetic Algorithm is applied to solve. Findings: The findings show that the proposed method is an effective way to maximize NPV in MRCPSP-DCF problems while activity splitting is allowed. The proposed algorithm is very fast and can schedule experimental cases with 1000 variables and 100 resources in few seconds. The results are then compared with branch and bound method and simulated annealing algorithm and it is found the proposed genetic algorithm can provide results with better quality. Then algorithm is then applied for scheduling a hospital in practice. Originality/value: The method can be used alone or as a macro in Microsoft Office Project® Software to schedule MRCPSP-DCF problems or to modify resource over-allocated activities after scheduling a project. This can help project engineers to schedule project activities rapidly with more accuracy in practice.

  2. An Approach to Derive Parametric L-System Using Genetic Algorithm

    Science.gov (United States)

    Farooq, Humera; Zakaria, M. Nordin; Hassan, Mohd. Fadzil; Sulaiman, Suziah

    In computer graphics, L-System is widely used to model artificial plants structures and fractals. The Genetic Algorithm (GA) is the most popular form of Evolutionary Algorithms. This paper examines a method for automatic plant modeling which is based on an integration of GA and Parametric L-System using appropriate fitness function. The approach is specifically based on the implementation of two layered GA to derive the rewriting rules of Parametric L-System. The higher level of GA deals with the evolution of symbols and lower level deals with the evolution of numerical parameters. Initial results derived from the approach are very promising, which shows that complicated branching structures can be easily derived by the multilayered architecture of GA.

  3. Genetic Programming and Genetic Algorithms for Propositions

    Directory of Open Access Journals (Sweden)

    Nabil M. HEWAHI

    2012-01-01

    Full Text Available In this paper we propose a mechanism to discover the compound proposition solutions for a given truth table without knowing the compound propositions that lead to the truth table results. The approach is based on two proposed algorithms, the first is called Producing Formula (PF algorithm which is based on the genetic programming idea, to find out the compound proposition solutions for the given truth table. The second algorithm is called the Solutions Optimization (SO algorithm which is based on genetic algorithms idea, to find a list of the optimum compound propositions that can solve the truth table. The obtained list will depend on the solutions obtained from the PF algorithm. Various types of genetic operators have been introduced to obtain the solutions either within the PF algorithm or SO algorithm.

  4. A New Optimization Approach for Maximizing the Photovoltaic Panel Power Based on Genetic Algorithm and Lagrange Multiplier Algorithm

    Directory of Open Access Journals (Sweden)

    Mahdi M. M. El-Arini

    2013-01-01

    Full Text Available In recent years, the solar energy has become one of the most important alternative sources of electric energy, so it is important to operate photovoltaic (PV panel at the optimal point to obtain the possible maximum efficiency. This paper presents a new optimization approach to maximize the electrical power of a PV panel. The technique which is based on objective function represents the output power of the PV panel and constraints, equality and inequality. First the dummy variables that have effect on the output power are classified into two categories: dependent and independent. The proposed approach is a multistage one as the genetic algorithm, GA, is used to obtain the best initial population at optimal solution and this initial population is fed to Lagrange multiplier algorithm (LM, then a comparison between the two algorithms, GA and LM, is performed. The proposed technique is applied to solar radiation measured at Helwan city at latitude 29.87°, Egypt. The results showed that the proposed technique is applicable.

  5. Genetic algorithm-fuzzy based dynamic motion planning approach for a mobile robot

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Presents the mobile robots dynamic motion planning problem with a task to find an obstacle-free route that requires minimum travel time from the start point to the destination point in a changing environment, due to the obstacle's moving. An Genetic Algorithm fuzzy(GA-Fuzzy)based optimal approach proposed to find any obstacle-free path and the GA used to select the optimal one, points ont that using this learned knowledge off line, a mobile robot can navigate to its goal point when it faces new scenario on-line. Concludes with the opti mal rule base given and the simulation results showing its effectiveness.

  6. A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

    Science.gov (United States)

    Shehadeh, Hana; Lea, Robert N.

    1992-01-01

    Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

  7. Fluid Genetic Algorithm (FGA

    Directory of Open Access Journals (Sweden)

    Ruholla Jafari-Marandi

    2017-04-01

    Full Text Available Genetic Algorithm (GA has been one of the most popular methods for many challenging optimization problems when exact approaches are too computationally expensive. A review of the literature shows extensive research attempting to adapt and develop the standard GA. Nevertheless, the essence of GA which consists of concepts such as chromosomes, individuals, crossover, mutation, and others rarely has been the focus of recent researchers. In this paper method, Fluid Genetic Algorithm (FGA, some of these concepts are changed, removed, and furthermore, new concepts are introduced. The performance of GA and FGA are compared through seven benchmark functions. FGA not only shows a better success rate and better convergence control, but it can be applied to a wider range of problems including multi-objective and multi-level problems. Also, the application of FGA for a real engineering problem, Quadric Assignment Problem (AQP, is shown and experienced.

  8. Optimization of Electrical System for Offshore Wind Farms via a Genetic Algorithm Approach

    DEFF Research Database (Denmark)

    Zhao, Menghua

    to very different costs, system reliability, power quality, and power losses etc. Therefore, the optimization of electrical system design for offshore wind farms becomes more and more necessary. There are two tasks in this project: 1) the first one is to construct an algorithm for finding the capacity...... of a grid-connected wind farm; 2) the second one is the optimization of electrical system for offshore wind farms (OES-OWF). The capacity of a grid connected wind farm is limited by the transfer capability of the grid system, where the thermal limit of the transmission lines, the voltage stability......, and the LTC limitation of transformers, the power generation limits and the voltage operation range are considered as the constraints. The optimization method combined with probabilistic analysis is used to obtain the capacity of a given wind farm site. The OES-OWF is approached by Genetic Algorithm (GA...

  9. A Detailed look of Audio Steganography Techniques using LSB and Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Gunjan Nehru

    2012-01-01

    Full Text Available This paper is the study of various techniques of audio steganography using different algorithmis like genetic algorithm approach and LSB approach. We have tried some approaches that helps in audio steganography. As we know it is the art and science of writing hidden messages in such a way that no one, apart from the sender and intended recipient, suspects the existence of the message, a form of security through obscurity. In steganography, the message used to hide secret message is called host message or cover message. Once the contents of the host message or cover message are modified, the resultant message is known as stego message. In other words, stego message is combination of host message and secret message. Audio steganography requires a text or audio secret message to be embedded within a cover audio message. Due to availability of redundancy, the cover audio message before steganography, stego message after steganography remains same. for information hiding.

  10. Process planning optimization on turning machine tool using a hybrid genetic algorithm with local search approach

    Directory of Open Access Journals (Sweden)

    Yuliang Su

    2015-04-01

    Full Text Available A turning machine tool is a kind of new type of machine tool that is equipped with more than one spindle and turret. The distinctive simultaneous and parallel processing abilities of turning machine tool increase the complexity of process planning. The operations would not only be sequenced and satisfy precedence constraints, but also should be scheduled with multiple objectives such as minimizing machining cost, maximizing utilization of turning machine tool, and so on. To solve this problem, a hybrid genetic algorithm was proposed to generate optimal process plans based on a mixed 0-1 integer programming model. An operation precedence graph is used to represent precedence constraints and help generate a feasible initial population of hybrid genetic algorithm. Encoding strategy based on data structure was developed to represent process plans digitally in order to form the solution space. In addition, a local search approach for optimizing the assignments of available turrets would be added to incorporate scheduling with process planning. A real-world case is used to prove that the proposed approach could avoid infeasible solutions and effectively generate a global optimal process plan.

  11. Genetic algorithm optimization of entanglement

    CERN Document Server

    Navarro-Munoz, J C; Rosu, H C; Navarro-Munoz, Jorge C.

    2006-01-01

    We present an application of a genetic algorithmic computational method to the optimization of the concurrence measure of entanglement for the cases of one dimensional chains, as well as square and triangular lattices in a simple tight-binding approach

  12. DYNAMIC RELOCATION OF PLANT/WAREHOUSE FACILITIES:A FAST COMPACT GENETIC ALGORITHM APPROACH

    Institute of Scientific and Technical Information of China (English)

    Li Shugang; Wu Zhiming; Pang Xiaohong

    2004-01-01

    The problem of dynamic relocation and phase-out of combined manufacturing plant and warehousing facilities in the supply chain are concerned.A multiple time/multiple objective model is proposed to maximize total profit during the time horizon, minimize total access time from the plant/warehouse facilities to its suppliers and customers and maximize aggregated local incentives during the time horizon.The relocation problem keeps the feature of NP-hard and with the traditional method the optimal result cannot be got easily.So a compact genetic algorithm (CGA) is introduced to solve the problem.In order to accelerate the convergence speed of the CGA, the least square approach is introduced and a fast compact genetic algorithm (fCGA) is proposed.Finally, simulation results with the fCGA are compared with the CGA and classical integer programming (IP).The results show that the fCGA proposed is of high efficiency for Pareto optimality problem.

  13. A genetic-algorithm approach for assessing the liquefaction potential of sandy soils

    OpenAIRE

    Sen, G.; Akyol, E.

    2010-01-01

    The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone...

  14. A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes.

    Science.gov (United States)

    Lee, I; Sikora, R; Shaw, M J

    1997-01-01

    Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling.

  15. Nonlinear Electrical Circuit Oscillator Control Based on Backstepping Method: A Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Mahsa Khoeiniha

    2012-01-01

    Full Text Available This paper investigated study of dynamics of nonlinear electrical circuit by means of modern nonlinear techniques and the control of a class of chaotic system by using backstepping method based on Lyapunov function. The behavior of such nonlinear system when they are under the influence of external sinusoidal disturbances with unknown amplitudes has been considered. The objective is to analyze the performance of this system at different amplitudes of disturbances. We illustrate the proposed approach for controlling duffing oscillator problem to stabilize this system at the equilibrium point. Also Genetic Algorithm method (GA for computing the parameters of controller has been used. GA can be successfully applied to achieve a better controller. Simulation results have shown the effectiveness of the proposed method.

  16. NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    WANG Weizhong; ZHAO Jie; GAO Yongsheng; CAI Hegao

    2006-01-01

    A novel approach for collision-free path planning of a multiple degree-of-freedom (DOF)articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point (VNP), a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise fiom initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

  17. A NAÏVE APPROACH TO SPEED UP PORTFOLIO OPTIMIZATION PROBLEM USING A MULTIOBJECTIVE GENETIC ALGORITHM

    Directory of Open Access Journals (Sweden)

    Baixauli-Soler, J. Samuel

    2012-05-01

    Full Text Available Genetic algorithms (GAs are appropriate when investors have the objective of obtaining mean‑variance (VaR efficient frontier as minimising VaR leads to non‑convex and non‑differential risk‑return optimisation problems. However GAs are a time‑consuming optimisation technique. In this paper, we propose to use a naïve approach consisting of using samples split by quartile of risk to obtain complete efficient frontiers in a reasonable computation time. Our results show that using reduced problems which only consider a quartile of the assets allow us to explore the efficient frontier for a large range of risk values. In particular, the third quartile allows us to obtain efficient frontiers from the 1.8% to 2.5% level of VaR quickly, while that of the first quartile of assets is from 1% to 1.3% level of VaR.

  18. [A general approach to the structural shape optimization using genetic algorithms and geometric design elements].

    Science.gov (United States)

    Annicchiarico, W

    2001-01-01

    Structural optimization is an engineering field which deal with the improvement of existing solutions or even more find new solutions that are better than the previous ones under some selected criterion. Shape optimization is a research area in this field and it is involved in developing new methodologies to find better structural design based on the shape as resistant element, as for example solutions with the less stress concentration zones and made with the minimum amount of material. The goal of this doctoral dissertation is to present and discuss a general structural shape optimization methodology able to optimize several structural systems or mechanical devices. The approach presented herein is based on global search optimization tools such as Genetic Algorithms and geometric design elements by means of beta-splines curves and surfaces representation. Finally the great versatility of the developed tool is presented and discussed with an application example.

  19. A Parallel Approach To Optimum Actuator Selection With a Genetic Algorithm

    Science.gov (United States)

    Rogers, James L.

    2000-01-01

    Recent discoveries in smart technologies have created a variety of aerodynamic actuators which have great potential to enable entirely new approaches to aerospace vehicle flight control. For a revolutionary concept such as a seamless aircraft with no moving control surfaces, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements. The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement Maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. Genetic algorithms have been instrumental in achieving good solutions to discrete optimization problems, such as the actuator placement problem. As a proof of concept, a genetic has been developed to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control for a simplified, untapered, unswept wing model. To find the optimum placement by searching all possible combinations would require 1,100 hours. Formulating the problem and as a multi-objective problem and modifying it to take advantage of the parallel processing capabilities of a multi-processor computer, reduces the optimization time to 22 hours.

  20. An Adaptive Agent-Based Model of Homing Pigeons: A Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Francis Oloo

    2017-01-01

    Full Text Available Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used “at the end” to validate the conceptual understanding. In today’s data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis.

  1. A New Spectral Shape-Based Record Selection Approach Using Np and Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Edén Bojórquez

    2013-01-01

    Full Text Available With the aim to improve code-based real records selection criteria, an approach inspired in a parameter proxy of spectral shape, named Np, is analyzed. The procedure is based on several objectives aimed to minimize the record-to-record variability of the ground motions selected for seismic structural assessment. In order to select the best ground motion set of records to be used as an input for nonlinear dynamic analysis, an optimization approach is applied using genetic algorithms focuse on finding the set of records more compatible with a target spectrum and target Np values. The results of the new Np-based approach suggest that the real accelerograms obtained with this procedure, reduce the scatter of the response spectra as compared with the traditional approach; furthermore, the mean spectrum of the set of records is very similar to the target seismic design spectrum in the range of interest periods, and at the same time, similar Np values are obtained for the selected records and the target spectrum.

  2. A HYBRID GENETIC ALGORITHM-NEURAL NETWORK APPROACH FOR PRICING CORES AND REMANUFACTURED CORES

    Directory of Open Access Journals (Sweden)

    M. Seidi

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT:Sustainability has become a major issue in most economies, causing many leading companies to focus on product recovery and reverse logistics. Remanufacturing is an industrial process that makes used products reusable. One of the important aspects in both reverse logistics and remanufacturing is the pricing of returned and remanufactured products (called cores. In this paper, we focus on pricing the cores and remanufactured cores. First we present a mathematical model for this purpose. Since this model does not satisfy our requirements, we propose a simulation optimisation approach. This approach consists of a hybrid genetic algorithm based on a neural network employed as the fitness function. We use automata learning theory to obtain the learning rate required for training the neural network. Numerical results demonstrate that the optimal value of the acquisition price of cores and price of remanufactured cores is obtained by this approach.

    AFRIKAANSE OPSOMMING: Volhoubaarheid het ‘n belangrike saak geword in die meeste ekonomieë, wat verskeie maatskappye genoop het om produkherwinning en omgekeerde logistiek te onder oë te neem. Hervervaardiging is ‘n industriële proses wat gebruikte produkte weer bruikbaar maak. Een van die belangrike aspekte in beide omgekeerde logistiek en hervervaardiging is die prysbepaling van herwinne en hervervaardigde produkte. Hierdie artikel fokus op die prysbepalingsaspekte by wyse van ‘n wiskundige model.

  3. A Modified Genetic Algorithm for Product Family Optimization with Platform Specified by Information Theoretical Approach

    Institute of Scientific and Technical Information of China (English)

    CHEN Chun-bao; WANG Li-ya

    2008-01-01

    Many existing product family design methods assume a given platform, However, it is not an in-tuitive task to select the platform and unique variable within a product family. Meanwhile, most approachesare single-platform methods, in which design variables are either shared across all product variants or not atall. While in multiple-platform design, platform variables can have special value with regard to a subset ofproduct variants within the product family, and offer opportunities for superior overall design. An informationtheoretical approach incorporating fuzzy clustering and Shannon's entropy was proposed for platform variablesselection in multiple-platform product family. A 2-level chromosome genetic algorithm (2LCGA) was proposedand developed for optimizing the corresponding product family in a single stage, simultaneously determiningthe optimal settings for the product platform and unique variables. The single-stage approach can yield im-provements in the overall performance of the product family compared with two-stage approaches, in which thefirst stage involves determining the best settings for the platform and values of unique variables are found foreach product in the second stage. An example of design of a family of universal motors was used to verify theproposed method.

  4. An Interval-Valued Approach to Business Process Simulation Based on Genetic Algorithms and the BPMN

    Directory of Open Access Journals (Sweden)

    Mario G.C.A. Cimino

    2014-05-01

    Full Text Available Simulating organizational processes characterized by interacting human activities, resources, business rules and constraints, is a challenging task, because of the inherent uncertainty, inaccuracy, variability and dynamicity. With regard to this problem, currently available business process simulation (BPS methods and tools are unable to efficiently capture the process behavior along its lifecycle. In this paper, a novel approach of BPS is presented. To build and manage simulation models according to the proposed approach, a simulation system is designed, developed and tested on pilot scenarios, as well as on real-world processes. The proposed approach exploits interval-valued data to represent model parameters, in place of conventional single-valued or probability-valued parameters. Indeed, an interval-valued parameter is comprehensive; it is the easiest to understand and express and the simplest to process, among multi-valued representations. In order to compute the interval-valued output of the system, a genetic algorithm is used. The resulting process model allows forming mappings at different levels of detail and, therefore, at different model resolutions. The system has been developed as an extension of a publicly available simulation engine, based on the Business Process Model and Notation (BPMN standard.

  5. Image Retrieval Approach Based on Intuitive Fuzzy Set Combined with Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Xiao-yin; XU Wei-hua; HU Chang-zhen

    2009-01-01

    Aiming at shortcomings of traditional image retrieval systems,a new image retrieval approach based on color features of image combining intuitive fuzzy theory with genetic algorithm is proposed.Each image is segmented into a constant number of sub-images in vertical direction.Color features are extracted from every sub-image to get chromosome coding.It is considered that fuzzy membership and intuitive fuzzy hesitancy degree of every pixel's color in image are associated to all the color histogram bins.Certain feature,fuzzy feature and intuitive fuzzy feature of colors in an image,are used together to describe the content of image.Efficient combinations of sub-image are selected according to operation of selecting,crossing and variation.Retrieval resuits are obtained from image matching based on these color feature combinations of sub-images.Tests show that this approach can improve the accuracy of image retrieval in the case of not decreasing the speed of image retrieval.Its mean precision is above 80%.

  6. Planning additional drilling campaign using two-space genetic algorithm: A game theoretical approach

    Science.gov (United States)

    Kumral, Mustafa; Ozer, Umit

    2013-03-01

    Grade and tonnage are the most important technical uncertainties in mining ventures because of the use of estimations/simulations, which are mostly generated from drill data. Open pit mines are planned and designed on the basis of the blocks representing the entire orebody. Each block has different estimation/simulation variance reflecting uncertainty to some extent. The estimation/simulation realizations are submitted to mine production scheduling process. However, the use of a block model with varying estimation/simulation variances will lead to serious risk in the scheduling. In the medium of multiple simulations, the dispersion variances of blocks can be thought to regard technical uncertainties. However, the dispersion variance cannot handle uncertainty associated with varying estimation/simulation variances of blocks. This paper proposes an approach that generates the configuration of the best additional drilling campaign to generate more homogenous estimation/simulation variances of blocks. In other words, the objective is to find the best drilling configuration in such a way as to minimize grade uncertainty under budget constraint. Uncertainty measure of the optimization process in this paper is interpolation variance, which considers data locations and grades. The problem is expressed as a minmax problem, which focuses on finding the best worst-case performance i.e., minimizing interpolation variance of the block generating maximum interpolation variance. Since the optimization model requires computing the interpolation variances of blocks being simulated/estimated in each iteration, the problem cannot be solved by standard optimization tools. This motivates to use two-space genetic algorithm (GA) approach to solve the problem. The technique has two spaces: feasible drill hole configuration with minimization of interpolation variance and drill hole simulations with maximization of interpolation variance. Two-space interacts to find a minmax solution

  7. A genetic algorithm approach for assessing soil liquefaction potential based on reliability method

    Indian Academy of Sciences (India)

    M H Bagheripour; I Shooshpasha; M Afzalirad

    2012-02-01

    Deterministic approaches are unable to account for the variations in soil’s strength properties, earthquake loads, as well as source of errors in evaluations of liquefaction potential in sandy soils which make them questionable against other reliability concepts. Furthermore, deterministic approaches are incapable of precisely relating the probability of liquefaction and the factor of safety (FS). Therefore, the use of probabilistic approaches and especially, reliability analysis is considered since a complementary solution is needed to reach better engineering decisions. In this study, Advanced First-Order Second-Moment (AFOSM) technique associated with genetic algorithm (GA) and its corresponding sophisticated optimization techniques have been used to calculate the reliability index and the probability of liquefaction. The use of GA provides a reliable mechanism suitable for computer programming and fast convergence. A new relation is developed here, by which the liquefaction potential can be directly calculated based on the estimated probability of liquefaction (), cyclic stress ratio (CSR) and normalized standard penetration test (SPT) blow counts while containing a mean error of less than 10% from the observational data. The validity of the proposed concept is examined through comparison of the results obtained by the new relation and those predicted by other investigators. A further advantage of the proposed relation is that it relates and FS and hence it provides possibility of decision making based on the liquefaction risk and the use of deterministic approaches. This could be beneficial to geotechnical engineers who use the common methods of FS for evaluation of liquefaction. As an application, the city of Babolsar which is located on the southern coasts of Caspian Sea is investigated for liquefaction potential. The investigation is based primarily on in situ tests in which the results of SPT are analysed.

  8. Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation

    DEFF Research Database (Denmark)

    Khan, Abdul Rauf; Schiøler, Henrik; Knudsen, Torben

    2016-01-01

    on the portioning of information space; and (2) use of the genetic algorithm to solve combinatorial problems for classification. In particular, we will implement our methodology to solve complex classification problems and compare the performance of our classifier with other well-known methods (SVM, KNN, and ANN...

  9. Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation

    DEFF Research Database (Denmark)

    Khan, Abdul Rauf; Schiøler, Henrik; Knudsen, Torben

    on the portioning of information space; and (2) use of the genetic algorithm to solve combinatorial problems for classification. In particular, we will implement our methodology to solve complex classification problems and compare the performance of our classifier with other well-known methods (SVM, KNN, and ANN...

  10. An improved self-calibration approach based on adaptive genetic algorithm for position-based visual servo

    Institute of Scientific and Technical Information of China (English)

    Ding LIU; Xiongjun WU; Yanxi YANG

    2008-01-01

    An improved self-calibrating algorithm for visual servo based on adaptive genetic algorithm is proposed in this paper.Our approach introduces an extension of Mendonca-Cipolla and G.Chesi's self-calibration for the positionbased visual servo technique which exploits the singular value property of the essential matrix.Specifically,a suitable dynamic online cost function is generated according to the property of the three singular values.The visual servo process is carried out simultaneous to the dynamic self-calibration,and then the cost function is minirmzed using the adaptive genetic algorithm instead of the gradient descent method in G.Chesi's approach.Moreover,this method overcomes the limitation that the initial parameters must be selected close to the true value,which is not constant in many cases.It is not necessary to know exactly the camera intrinsic parameters when using our approach,instead,coarse coding bounds ot the five parameters are enough for the algorithm,which can be done once and for all off-line.Besides,this algorithm does not require knowledge of the 3D model of the object.Simulation experiments are carried out and the results demonstrate that the proposed approach provides a fast convergence speed and robustness against unpredictable perturbalaons of camera parameters,and it is an effective and efficient visual scrvo algorithm.

  11. Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

    Science.gov (United States)

    Erguzel, Turker Tekin; Ozekes, Serhat; Tan, Oguz; Gultekin, Selahattin

    2015-10-01

    Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) treated with repetitive transcranial magnetic stimulation (rTMS). The performance of the combination of a genetic algorithm (GA) and a back-propagation (BP) neural network (BPNN) was evaluated using 6-channel pre-rTMS electroencephalographic (EEG) patterns of theta and delta frequency bands. The GA was first used to eliminate the redundant and less discriminant features to maximize classification performance. The BPNN was then applied to test the performance of the feature subset. Finally, classification performance using the subset was evaluated using 6-fold cross-validation. Although the slow bands of the frontal electrodes are widely used to collect EEG data for patients with MDD and provide quite satisfactory classification results, the outcomes of the proposed approach indicate noticeably increased overall accuracy of 89.12% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.904 using the reduced feature set.

  12. Voice Matching Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Abhishek Bal

    2014-03-01

    Full Text Available In this paper, the use of Genetic Algorithm (GA for voice recognition is described. The practical application of Genetic Algorithm (GA to the solution of engineering problem is a rapidly emerging approach in the field of control engineering and signal processing. Genetic algorithms are useful for searching a space in multi-directional way from large spaces and poorly defined space. Voice is a signal of infinite information. Digital processing of voice signal is very important for automatic voice recognition technology. Nowadays, voice processing is very much important in security mechanism due to mimicry characteristic. So studying the voice feature extraction in voice processing is very necessary in military, hospital, telephone system, investigation bureau and etc. In order to extract valuable information from the voice signal, make decisions on the process, and obtain results, the data needs to be manipulated and analyzed. In this paper, if the instant voice is not matched with same person’s reference voices in the database, then Genetic Algorithm (GA is applied between two randomly chosen reference voices. Again the instant voice is compared with the result of Genetic Algorithm (GA which is used, including its three main steps: selection, crossover and mutation. We illustrate our approach with different sample of voices from human in our institution.

  13. A Genetic Algorithm Based Approach for Solving the Minimum Dominating Set of Queens Problem

    Directory of Open Access Journals (Sweden)

    Saad Alharbi

    2017-01-01

    Full Text Available In the field of computing, combinatorics, and related areas, researchers have formulated several techniques for the Minimum Dominating Set of Queens Problem (MDSQP pertaining to the typical chessboard based puzzles. However, literature shows that limited research has been carried out to solve the MDSQP using bioinspired algorithms. To fill this gap, this paper proposes a simple and effective solution based on genetic algorithms to solve this classical problem. We report results which demonstrate that near optimal solutions have been determined by the GA for different board sizes ranging from 8 × 8 to 11 × 11.

  14. Exploiting problem structure in a genetic algorithm approach to a nurse rostering problem

    CERN Document Server

    Uwe, Aickelin

    2008-01-01

    There is considerable interest in the use of genetic algorithms to solve problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle the conflict between objectives and constraints that typically occurs in such problems. In order to overcome this, successful implementations frequently make use of problem specific knowledge. This paper is concerned with the development of a GA for a nurse rostering problem at a major UK hospital. The structure of the constraints is used as the basis for a co-evolutionary strategy using co-operating sub-populations. Problem specific knowledge is also used to define a system of incentives and disincentives, and a complementary mutation operator. Empirical results based on 52 weeks of live data show how these features are able to improve an unsuccessful canonical GA to the point where it is able to provide a practical solution to the problem

  15. Demodulating the Response of Optical Fibre Long-Period Gratings: Genetic Algorithm Approach

    Institute of Scientific and Technical Information of China (English)

    P. S. André; R. A. Sá. Ferreira; C. M. L. Correia; H. Kalinowshy; XIN Xiang-Jun; J. L. Pinto

    2006-01-01

    @@ The extraction of the physical parameters of long period gratings from the spectral response is not an easy process. We present a demodulation technique to synthesize the physical parameters of a long period grating recorded in an optical fibre. The demodulation is achieved through the implementation of a genetic algorithm.The extracted parameters are in agreement with the typical values known for long period gratings.

  16. A Genetic Algorithm Approach to Optimize Parameters in Infrared Guidance System

    Institute of Scientific and Technical Information of China (English)

    周德俊

    2001-01-01

    In the infrared guidance system, the gray level threshold is key for target recognition. After thresholding, a target in the binary image is distinguished from the complex background by three recognition features. Using a genetic algorithm, this paper seeks to find the optimal parameters varied with different sub-images to compute the adaptive segmentation threshold. The experimental results reveal that the GA paradigm is an efficient and effective method of search.

  17. A genetic-algorithm approach for assessing the liquefaction potential of sandy soils

    Directory of Open Access Journals (Sweden)

    G. Sen

    2010-04-01

    Full Text Available The determination of liquefaction potential is required to take into account a large number of parameters, which creates a complex nonlinear structure of the liquefaction phenomenon. The conventional methods rely on simple statistical and empirical relations or charts. However, they cannot characterise these complexities. Genetic algorithms are suited to solve these types of problems. A genetic algorithm-based model has been developed to determine the liquefaction potential by confirming Cone Penetration Test datasets derived from case studies of sandy soils. Software has been developed that uses genetic algorithms for the parameter selection and assessment of liquefaction potential. Then several estimation functions for the assessment of a Liquefaction Index have been generated from the dataset. The generated Liquefaction Index estimation functions were evaluated by assessing the training and test data. The suggested formulation estimates the liquefaction occurrence with significant accuracy. Besides, the parametric study on the liquefaction index curves shows a good relation with the physical behaviour. The total number of misestimated cases was only 7.8% for the proposed method, which is quite low when compared to another commonly used method.

  18. AN APPROACH TO OPTIMIZE QOS ROUTING PROTOCOL USING GENETIC ALGORITHM IN MANET

    Directory of Open Access Journals (Sweden)

    Vikas Siwach

    2012-09-01

    Full Text Available Quality of Service support for Mobile Ad hoc Networks is an exigenttask due to dynamic topology and limited resource. To support QoS,the link state information such as delay, bandwidth, jitter, cost, errorrate and node energy in the network should be available andmanageable. QOS is one the basic requirement of a network andwhen we talk about the Mobile Network this is the highly constraintrequirement of a user. To improve the quality of service we usedifferent changes in MANET protocols, its parameter, routingalgorithm etc. The proposed work is to define a genetic based routingapproach to optimize the routing in MANETs. The genetic approachwill generate an optimized path on the basic of congestion over thenetwork. The result path will improve the data delivery over thenetwork. The focus of the paper is to study about MANET, QOS andtries to develop a network on which genetic algorithm is applied togenerate an optimized path.

  19. A Genetic Algorithm-Based Approach for Process Scheduling In Distributed Operating Systems

    Directory of Open Access Journals (Sweden)

    2012-01-01

    Full Text Available A Distributed Computing System comprising networked heterogeneous processors requires efficient process allocation algorithms to achieve minimum turnaround time and highest possible throughput. To efficiently execute processes on a distributed system, processes must be correctly assigned to processors and determine the execution order of processes so that the overall execution time is minimized. Even when target processors are fully connected and the communication among processors is fast and no dependencies exist among processes the scheduling problem is NP-complete. Complexity of scheduling problem dependent of number of processors, process execution time and the processor network topology. As distributed systems exist in kinds of homogeneous and heterogeneous, in heterogeneous systems the difference between processors leads to different execution time for an individual process on different processors and makes scheduling problem more complex. Our proposed genetic algorithm is applicable for both homogeneous and heterogeneous kinds.

  20. Multi-objective optimization of lithium-ion battery model using genetic algorithm approach

    Science.gov (United States)

    Zhang, Liqiang; Wang, Lixin; Hinds, Gareth; Lyu, Chao; Zheng, Jun; Li, Junfu

    2014-12-01

    A multi-objective parameter identification method for modeling of Li-ion battery performance is presented. Terminal voltage and surface temperature curves at 15 °C and 30 °C are used as four identification objectives. The Pareto fronts of two types of Li-ion battery are obtained using the modified multi-objective genetic algorithm NSGA-II and the final identification results are selected using the multiple criteria decision making method TOPSIS. The simulated data using the final identification results are in good agreement with experimental data under a range of operating conditions. The validation results demonstrate that the modified NSGA-II and TOPSIS algorithms can be used as robust and reliable tools for identifying parameters of multi-physics models for many types of Li-ion batteries.

  1. Diagnosis of Hepatocellular Carcinoma Spectroscopy Based on the Feature Selection Approach of the Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Shao-qing Wang

    2013-06-01

    Full Text Available This paper aims to study the application of medical imaging technology with artificial intelligence technology on how to improve the diagnostic accuracy rate for hepatocellular carcinoma. The   recognition method based on genetic algorithm (GA and Neural Network are presented. GA was used to select 20 optimal features from the 401 initial features. BP (Back-propagation Neural Network, BP and PNN (Probabilistic Neural Network, PNN were used to classify tested samples based on these optimized features, and make comparison between results based on 20 optimal features and the all 401 features. The results of the experiment show that the method can improve the recognition rate.

  2. Genetic Algorithms and Local Search

    Science.gov (United States)

    Whitley, Darrell

    1996-01-01

    The first part of this presentation is a tutorial level introduction to the principles of genetic search and models of simple genetic algorithms. The second half covers the combination of genetic algorithms with local search methods to produce hybrid genetic algorithms. Hybrid algorithms can be modeled within the existing theoretical framework developed for simple genetic algorithms. An application of a hybrid to geometric model matching is given. The hybrid algorithm yields results that improve on the current state-of-the-art for this problem.

  3. Foundations of genetic algorithms 1991

    CERN Document Server

    1991-01-01

    Foundations of Genetic Algorithms 1991 (FOGA 1) discusses the theoretical foundations of genetic algorithms (GA) and classifier systems.This book compiles research papers on selection and convergence, coding and representation, problem hardness, deception, classifier system design, variation and recombination, parallelization, and population divergence. Other topics include the non-uniform Walsh-schema transform; spurious correlations and premature convergence in genetic algorithms; and variable default hierarchy separation in a classifier system. The grammar-based genetic algorithm; condition

  4. A Genetic Algorithm Based Approach for Segmentingand Identifying Defects in Glass Bottles

    Directory of Open Access Journals (Sweden)

    George Mathew

    2016-07-01

    Full Text Available This work mainly aims at designing and developing a suitable tool for identifying defects in glass bottles through visual inspection based on Segmentation algorithm. The defect identification is done in three stages. These are Image acquisition, Pre-processing and filtering and Segmentation. In the Image acquisition stage, samples of real time images are taken and are converted into 512x512 monochrome images. In the Preprocessing and filtering stage, the image acquired is passed through median filters. The Proposed filter is Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF because it produces a high value of Peak to Signal Ratio (PSNR of 60-75db.The de-noised images is further sent to the third stage which is Segmentation. In this work, Segmentation is done using Genetic Algorithm (GA. The defects in the images are segmented and highlighted. Thus the areas of defects are spotted out. The Genetic segmentation has produced high Sensitivity, high Specificity and high Accuracy of 92%, 93% and 93% respectively. Thus the Proposed work produced effective results and hence this tool shall be useful for food processing industries for the Quality Inspection of the glass bottles

  5. An Approach for Software Effort Estimation Using Fuzzy Numbers and Genetic Algorithm to Deal with Uncertainity

    Directory of Open Access Journals (Sweden)

    Divya Kashyap

    2013-07-01

    Full Text Available One of the most critical tasks during the software development life cycle is that of estimating the effort and time involved in the development of the software product. Estimation may be performed by many ways such as: Expert judgments, A lgorithmic effort estimation, Machine learning and Analogy-based estimation. In which Ana logy-based software effort estimation is the process of identifying one or more historical p rojects that are similar to the project being developed and then using the estimates from them. A nalogy-based estimation is integrated with Fuzzy numbers in order to improve the performance o f software project effort estimation during the early stages of a software development lifecycl e. Because of uncertainty associated with attribute measurement and data availability, fuzzy logic is introduced in the proposed model. But hardly a historical project is exactly same as the project being estimated due to some distance associated in similarity distance. This me ans that the most similar project still has a similarity distance with the project being estimate d in most of the cases. Therefore, the effort needs to be adjusted when the most similar project has a similarity distance with the project being estimated. To adjust the reused effort, we bu ild an adjustment mechanism whose algorithm can derive the optimal adjustment on the reused effort using Genetic Algorithm. The proposed model Combine the fuzzy logic to estimate software effort in early stages with Genetic algorithm based adjustment mechanism may result to near the correct effort estimation.

  6. A Hierarchical and Distributed Approach for Mapping Large Applications to Heterogeneous Grids using Genetic Algorithms

    Science.gov (United States)

    Sanyal, Soumya; Jain, Amit; Das, Sajal K.; Biswas, Rupak

    2003-01-01

    In this paper, we propose a distributed approach for mapping a single large application to a heterogeneous grid environment. To minimize the execution time of the parallel application, we distribute the mapping overhead to the available nodes of the grid. This approach not only provides a fast mapping of tasks to resources but is also scalable. We adopt a hierarchical grid model and accomplish the job of mapping tasks to this topology using a scheduler tree. Results show that our three-phase algorithm provides high quality mappings, and is fast and scalable.

  7. Cost effective approach on feature selection using genetic algorithms and fuzzy logic for diabetes diagnosis

    CERN Document Server

    Ephzibah, E P

    2011-01-01

    A way to enhance the performance of a model that combines genetic algorithms and fuzzy logic for feature selection and classification is proposed. Early diagnosis of any disease with less cost is preferable. Diabetes is one such disease. Diabetes has become the fourth leading cause of death in developed countries and there is substantial evidence that it is reaching epidemic proportions in many developing and newly industrialized nations. In medical diagnosis, patterns consist of observable symptoms along with the results of diagnostic tests. These tests have various associated costs and risks. In the automated design of pattern classification, the proposed system solves the feature subset selection problem. It is a task of identifying and selecting a useful subset of pattern-representing features from a larger set of features. Using fuzzy rule-based classification system, the proposed system proves to improve the classification accuracy.

  8. Classification of Noisy Data: An Approach Based on Genetic Algorithms and Voronoi Tessellation

    DEFF Research Database (Denmark)

    Khan, Abdul Rauf; Schiøler, Henrik; Knudsen, Torben;

    2016-01-01

    Classification is one of the major constituents of the data-mining toolkit. The well-known methods for classification are built on either the principle of logic or statistical/mathematical reasoning for classification. In this article we propose: (1) a different strategy, which is based......). The results of this study suggest that our proposed methodology is specialized to deal with the classification problem of highly imbalanced classes with significant overlap....... on the portioning of information space; and (2) use of the genetic algorithm to solve combinatorial problems for classification. In particular, we will implement our methodology to solve complex classification problems and compare the performance of our classifier with other well-known methods (SVM, KNN, and ANN...

  9. M51 revisited: a genetic algorithm approach of its interaction history

    CERN Document Server

    Theis, C; Spinneker, Ch.; Theis, Ch.

    2003-01-01

    Detailed models of observed interacting galaxies suffer from the extended parameter space. Here, we present results from our code MINGA which couples an evolutionary optimization strategy (a genetic algorithm) with a fast N-body method. MINGA allows for an automatic search of the optimal region(s) in parameter space within a few hours to a few days of CPU time on a modern PC by investigating of the order of 10^5 models. We demonstrate its applicability by modelling the HI intensity and velocity maps of the interacting system M51 and NGC 5195. We get a good fit for the HI intensity map and we can reproduce the counter-rotation feature of the HI arm. Our result corroborates the results of Salo & Laurikainen (2000) who favour a model with multiple passages through M51's disk.

  10. M51 revisited: A genetic algorithm approach of its interaction history

    Science.gov (United States)

    Theis, Christian; Spinneker, Christian

    2003-04-01

    Detailed models of observed interacting galaxies suffer from the extended parameter space. Here, we present results from our code MINGA which couples an evolutionary optimization strategy (a genetic algorithm) with a fast N-body method. MINGA allows for an automatic search of the optimal region(s) in parameter space within a few hours to a few days of CPU time on a modern PC by investigating of the order of 105 models. We demonstrate its applicability by modelling the HI intensity and velocity maps of the interacting system M51 and NGC 5195. We get a good fit for the HI intensity map and we can reproduce the counter-rotation feature of the HI arm. Our result corroborates the results of Salo and Laurikainen (2000) who favour a model with multiple passages through M51's disk.

  11. Enhanced Genetic Algorithm approach for Solving Dynamic Shortest Path Routing Problems using Immigrants and Memory Schemes

    CERN Document Server

    Nair, T R Gopalakrishnan; Yashoda, M B

    2011-01-01

    In Internet Routing, the static shortest path (SP) problem has been addressed using well known intelligent optimization techniques like artificial neural networks, genetic algorithms (GAs) and particle swarm optimization. Advancement in wireless communication lead more and more mobile wireless networks, such as mobile networks [mobile ad hoc networks (MANETs)] and wireless sensor networks. Dynamic nature of the network is the main characteristic of MANET. Therefore, the SP routing problem in MANET turns into dynamic optimization problem (DOP). Here the nodes ae made aware of the environmental condition, thereby making it intelligent, which goes as the input for GA. The implementation then uses GAs with immigrants and memory schemes to solve the dynamic SP routing problem (DSPRP) in MANETS. In our paper, once the network topology changes, the optimal solutions in the new environment can be searched using the new immigrants or the useful information stored in the memory. Results shows GA with new immigrants sho...

  12. Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    A. Jayachitra

    2014-01-01

    Full Text Available Genetic algorithm (GA based PID (proportional integral derivative controller has been proposed for tuning optimized PID parameters in a continuous stirred tank reactor (CSTR process using a weighted combination of objective functions, namely, integral square error (ISE, integral absolute error (IAE, and integrated time absolute error (ITAE. Optimization of PID controller parameters is the key goal in chemical and biochemical industries. PID controllers have narrowed down the operating range of processes with dynamic nonlinearity. In our proposed work, globally optimized PID parameters tend to operate the CSTR process in its entire operating range to overcome the limitations of the linear PID controller. The simulation study reveals that the GA based PID controller tuned with fixed PID parameters provides satisfactory performance in terms of set point tracking and disturbance rejection.

  13. OPTIMAL CAPACITORS PLACEMENT IN DISTRIBUTION NETWORKS USING GENETIC ALGORITHM: A DIMENSION REDUCING APPROACH

    Directory of Open Access Journals (Sweden)

    S.NEELIMA

    2011-08-01

    Full Text Available A distribution system is an interface between the bulk power system and the consumers. Among these systems, radial distributions system is popular because of low cost and simple design. In distribution systems, the voltages at buses reduces when moved away from the substation, also the losses are high. The reason for decrease in voltage and high losses is the insufficient amount of reactive power, which can be provided by the shunt capacitors. But the placement of the capacitor with appropriate size is always a challenge. Thus the optimal capacitor placement problem is to determine the location and size of capacitors to be placed in distribution networks in an efficient way to reduce the power losses and improve the voltage profile of the system. For this purpose, in this paper, two stage methodologies are used. In first stage, the load flow of pre-compensated distribution system is carried out using ‘dimension reducing distribution load flow algorithm (DRDLFA’. On the basis of this load flow the potential locations of compensation are computed. In the second stage, Genetic Algorithm (GA technique is used to determine the optimal location and size of the capacitors such that the cost of the energy loss and capacitor cost to be a minimum. The above method is tested on IEEE 69 bus system and compared with other methods in the literature.

  14. A genetic algorithm approach for open-pit mine production scheduling

    Directory of Open Access Journals (Sweden)

    Aref Alipour

    2017-06-01

    Full Text Available In an Open-Pit Production Scheduling (OPPS problem, the goal is to determine the mining sequence of an orebody as a block model. In this article, linear programing formulation is used to aim this goal. OPPS problem is known as an NP-hard problem, so an exact mathematical model cannot be applied to solve in the real state. Genetic Algorithm (GA is a well-known member of evolutionary algorithms that widely are utilized to solve NP-hard problems. Herein, GA is implemented in a hypothetical Two-Dimensional (2D copper orebody model. The orebody is featured as two-dimensional (2D array of blocks. Likewise, counterpart 2D GA array was used to represent the OPPS problem’s solution space. Thereupon, the fitness function is defined according to the OPPS problem’s objective function to assess the solution domain. Also, new normalization method was used for the handling of block sequencing constraint. A numerical study is performed to compare the solutions of the exact and GA-based methods. It is shown that the gap between GA and the optimal solution by the exact method is less than % 5; hereupon GA is found to be efficiently in solving OPPS problem.

  15. Genetic algorithm essentials

    CERN Document Server

    Kramer, Oliver

    2017-01-01

    This book introduces readers to genetic algorithms (GAs) with an emphasis on making the concepts, algorithms, and applications discussed as easy to understand as possible. Further, it avoids a great deal of formalisms and thus opens the subject to a broader audience in comparison to manuscripts overloaded by notations and equations. The book is divided into three parts, the first of which provides an introduction to GAs, starting with basic concepts like evolutionary operators and continuing with an overview of strategies for tuning and controlling parameters. In turn, the second part focuses on solution space variants like multimodal, constrained, and multi-objective solution spaces. Lastly, the third part briefly introduces theoretical tools for GAs, the intersections and hybridizations with machine learning, and highlights selected promising applications.

  16. An Approach to Assembly Sequence Plannning Based on Hierarchical Strategy and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    Niu Xinwen; Ding Han; Xiong Youlun

    2001-01-01

    Using group and subassembly cluster methods, the hierarchical structure of a product is.generated automatically, which largely reduces the complexity of planning. Based on genetic algofithn the optimal of assembly sequence of each stracture level can be obtained by sequence-bysequence search. As a result, a better assembly sequence of the product can be generated by combining the assembly sequences of all hierarchical structures, which provides more parallelism and flexibility for assembly operations. An industrial example is solved by this new approach.

  17. Genetic algorithms for protein threading.

    Science.gov (United States)

    Yadgari, J; Amir, A; Unger, R

    1998-01-01

    Despite many years of efforts, a direct prediction of protein structure from sequence is still not possible. As a result, in the last few years researchers have started to address the "inverse folding problem": Identifying and aligning a sequence to the fold with which it is most compatible, a process known as "threading". In two meetings in which protein folding predictions were objectively evaluated, it became clear that threading as a concept promises a real breakthrough, but that much improvement is still needed in the technique itself. Threading is a NP-hard problem, and thus no general polynomial solution can be expected. Still a practical approach with demonstrated ability to find optimal solutions in many cases, and acceptable solutions in other cases, is needed. We applied the technique of Genetic Algorithms in order to significantly improve the ability of threading algorithms to find the optimal alignment of a sequence to a structure, i.e. the alignment with the minimum free energy. A major progress reported here is the design of a representation of the threading alignment as a string of fixed length. With this representation validation of alignments and genetic operators are effectively implemented. Appropriate data structure and parameters have been selected. It is shown that Genetic Algorithm threading is effective and is able to find the optimal alignment in a few test cases. Furthermore, the described algorithm is shown to perform well even without pre-definition of core elements. Existing threading methods are dependent on such constraints to make their calculations feasible. But the concept of core elements is inherently arbitrary and should be avoided if possible. While a rigorous proof is hard to submit yet an, we present indications that indeed Genetic Algorithm threading is capable of finding consistently good solutions of full alignments in search spaces of size up to 10(70).

  18. Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms.

    Science.gov (United States)

    Azadnia, Amir Hossein; Taheri, Shahrooz; Ghadimi, Pezhman; Saman, Muhamad Zameri Mat; Wong, Kuan Yew

    2013-01-01

    One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

  19. Order Batching in Warehouses by Minimizing Total Tardiness: A Hybrid Approach of Weighted Association Rule Mining and Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Amir Hossein Azadnia

    2013-01-01

    Full Text Available One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

  20. A GENETIC ALGORITHM USING THE LOCAL SEARCH HEURISTIC IN FACILITIES LAYOUT PROBLEM: A MEMETİC ALGORİTHM APPROACH

    Directory of Open Access Journals (Sweden)

    Orhan TÜRKBEY

    2002-02-01

    Full Text Available Memetic algorithms, which use local search techniques, are hybrid structured algorithms like genetic algorithms among evolutionary algorithms. In this study, for Quadratic Assignment Problem (QAP, a memetic structured algorithm using a local search heuristic like 2-opt is developed. Developed in the algorithm, a crossover operator that has not been used before for QAP is applied whereas, Eshelman procedure is used in order to increase thesolution variability. The developed memetic algorithm is applied on test problems taken from QAP-LIB, the results are compared with the present techniques in the literature.

  1. One-qubit quantum gates in a circular graphene quantum dot: genetic algorithm approach.

    Science.gov (United States)

    Amparán, Gibrán; Rojas, Fernando; Pérez-Garrido, Antonio

    2013-05-16

    The aim of this work was to design and control, using genetic algorithm (GA) for parameter optimization, one-charge-qubit quantum logic gates σx, σy, and σz, using two bound states as a qubit space, of circular graphene quantum dots in a homogeneous magnetic field. The method employed for the proposed gate implementation is through the quantum dynamic control of the qubit subspace with an oscillating electric field and an onsite (inside the quantum dot) gate voltage pulse with amplitude and time width modulation which introduce relative phases and transitions between states. Our results show that we can obtain values of fitness or gate fidelity close to 1, avoiding the leakage probability to higher states. The system evolution, for the gate operation, is presented with the dynamics of the probability density, as well as a visualization of the current of the pseudospin, characteristic of a graphene structure. Therefore, we conclude that is possible to use the states of the graphene quantum dot (selecting the dot size and magnetic field) to design and control the qubit subspace, with these two time-dependent interactions, to obtain the optimal parameters for a good gate fidelity using GA.

  2. Modelling molecule-surface interactions--an automated quantum-classical approach using a genetic algorithm.

    Science.gov (United States)

    Herbers, Claudia R; Johnston, Karen; van der Vegt, Nico F A

    2011-06-14

    We present an automated and efficient method to develop force fields for molecule-surface interactions. A genetic algorithm (GA) is used to parameterise a classical force field so that the classical adsorption energy landscape of a molecule on a surface matches the corresponding landscape from density functional theory (DFT) calculations. The procedure performs a sophisticated search in the parameter phase space and converges very quickly. The method is capable of fitting a significant number of structures and corresponding adsorption energies. Water on a ZnO(0001) surface was chosen as a benchmark system but the method is implemented in a flexible way and can be applied to any system of interest. In the present case, pairwise Lennard Jones (LJ) and Coulomb potentials are used to describe the molecule-surface interactions. In the course of the fitting procedure, the LJ parameters are refined in order to reproduce the adsorption energy landscape. The classical model is capable of describing a wide range of energies, which is essential for a realistic description of a fluid-solid interface.

  3. DESIGN AND OPTIMIZATION OF VALVELESS MICROPUMPS BY USING GENETIC ALGORITHMS APPROACH

    Directory of Open Access Journals (Sweden)

    AIDA F. M. SHUKUR

    2015-10-01

    Full Text Available This paper presents a design optimization of valveless micropump using Genetic Algorithms (GA. The micropump is designed with a diaphragm, pumping chamber and diffuser/nozzle element functions as inlet and outlet of micropump with outer dimension of (5×1.75×5 mm3. The main objectives of this research are to determine the optimum pressure to be applied at micropump’s diaphragm and to find the optimum coupling parameters of the micropump to achieve high flow rate with low power consumption. In order to determine the micropump design performance, the total deformation, strain energy density, equivalent stress for diaphragm, velocity and net flow rate of micropump are investigated. An optimal resonant frequency range for the diaphragm of valveless micropump is obtained through the result assessment. With the development of GA-ANSYS model, a maximum total displacement of diaphragm, 5.3635 µm, with 12 kPa actuation pressure and optimum net flowrate of 7.467 mL/min are achieved.

  4. One-qubit quantum gates in a circular graphene quantum dot: genetic algorithm approach

    Science.gov (United States)

    Amparán, Gibrán; Rojas, Fernando; Pérez-Garrido, Antonio

    2013-05-01

    The aim of this work was to design and control, using genetic algorithm (GA) for parameter optimization, one-charge-qubit quantum logic gates σ x, σ y, and σ z, using two bound states as a qubit space, of circular graphene quantum dots in a homogeneous magnetic field. The method employed for the proposed gate implementation is through the quantum dynamic control of the qubit subspace with an oscillating electric field and an onsite (inside the quantum dot) gate voltage pulse with amplitude and time width modulation which introduce relative phases and transitions between states. Our results show that we can obtain values of fitness or gate fidelity close to 1, avoiding the leakage probability to higher states. The system evolution, for the gate operation, is presented with the dynamics of the probability density, as well as a visualization of the current of the pseudospin, characteristic of a graphene structure. Therefore, we conclude that is possible to use the states of the graphene quantum dot (selecting the dot size and magnetic field) to design and control the qubit subspace, with these two time-dependent interactions, to obtain the optimal parameters for a good gate fidelity using GA.

  5. A Genetic Algorithm Approach for Solving a Flexible Job ShopScheduling Problem

    Directory of Open Access Journals (Sweden)

    Sayedmohammadreza Vaghefinezhad

    2012-05-01

    Full Text Available Flexible job shop scheduling has been noticed as an effective manufacturing system to cope with rapid development in todays competitive environment. Flexible job shop scheduling problem (FJSSP is known as a NP-hard in the field of the optimization problem. Assuming the dynamic state of the real world, make these problems more and more complicated. Most studies in the field of FJSSP have only focused on minimizing the total makespan. In this paper, a mathematical model for FJSSP has been developed. The objective function is maximizing the total profit while meeting some constraints. Considering time-varying raw material and selling price and dissimilar demand for each period, are attempts that have been done to decrease gaps between reality and the model. A manufacturer that produces various parts of gas valves has been used as a case study. The scheduling problem for multi part, multi period, and multi operation with parallel machines has been solved by genetic algorithm (GA. The best obtained answer determines the economic amount of production by different machines that belong to predefined operations for each part to satisfy customer demand in each period.

  6. New Approach to Optimize the Apfs Placement Based on Instantaneous Reactive Power Theory by Genetic Algorithm

    Science.gov (United States)

    Hashemi-Dezaki, Hamed; Mohammadalizadeh-Shabestary, Masoud; Askarian-Abyaneh, Hossein; Rezaei-Jegarluei, Mohammad

    2014-01-01

    In electrical distribution systems, a great amount of power are wasting across the lines, also nowadays power factors, voltage profiles and total harmonic distortions (THDs) of most loads are not as would be desired. So these important parameters of a system play highly important role in wasting money and energy, and besides both consumers and sources are suffering from a high rate of distortions and even instabilities. Active power filters (APFs) are innovative ideas for solving of this adversity which have recently used instantaneous reactive power theory. In this paper, a novel method is proposed to optimize the allocation of APFs. The introduced method is based on the instantaneous reactive power theory in vectorial representation. By use of this representation, it is possible to asses different compensation strategies. Also, APFs proper placement in the system plays a crucial role in either reducing the losses costs and power quality improvement. To optimize the APFs placement, a new objective function has been defined on the basis of five terms: total losses, power factor, voltage profile, THD and cost. Genetic algorithm has been used to solve the optimization problem. The results of applying this method to a distribution network illustrate the method advantages.

  7. A Genetic Algorithm Approach for the TV Self-Promotion Assignment Problem

    Science.gov (United States)

    Pereira, Paulo A.; Fontes, Fernando A. C. C.; Fontes, Dalila B. M. M.

    2009-09-01

    We report on the development of a Genetic Algorithm (GA), which has been integrated into a Decision Support System to plan the best assignment of the weekly self-promotion space for a TV station. The problem addressed consists on deciding which shows to advertise and when such that the number of viewers, of an intended group or target, is maximized. The GA proposed incorporates a greedy heuristic to find good initial solutions. These solutions, as well as the solutions later obtained through the use of the GA, go then through a repair procedure. This is used with two objectives, which are addressed in turn. Firstly, it checks the solution feasibility and if unfeasible it is fixed by removing some shows. Secondly, it tries to improve the solution by adding some extra shows. Since the problem faced by the commercial TV station is too big and has too many features it cannot be solved exactly. Therefore, in order to test the quality of the solutions provided by the proposed GA we have randomly generated some smaller problem instances. For these problems we have obtained solutions on average within 1% of the optimal solution value.

  8. Genetic Algorithms Based Approach for Designing Spring Brake Orthosis – Part I: Spring Parameters

    Directory of Open Access Journals (Sweden)

    M. S. Huq

    2012-01-01

    Full Text Available Spring brake orthosis (SBO concentrates purely on the knee to generate the swing phase of the paraplegic gait with the required hip flexion occurring passively as a consequence of the ipsilateral knee flexion, generated by releasing the torsion spring mounted at the knee joint. Electrical stimulation then drives the knee back to full extension, as well as restores the spring potential energy. In this paper, genetic algorithm (GA and its variant multi-objective GA (MOGA is used to perform the search operation for the ‘best’ spring parameters for the SBO spring mounted on an average sized subject simulated in the sagittal plane. Conventional torsion spring is tested against constant torque type spring in terms of swing duration as, based on first principles, it is hypothesized that constant torque spring would be able to produce slower SBO swing phase as might be preferred in assisted paraplegic gait. In line with the hypothesis, it is found that it is not possible to delay the occurrence of the flexion peak of the SBO swing phase further than its occurrence in the natural gait. The use of conventional torsion spring causes the swing knee flexion peak to appear rather faster than that of the natural gait, resulting in a potentially faster swing phase and hence gait cycle. The constant torque type spring on the other hand is able to stretch duration of the swing phase to some extent, rendering it the preferable spring type in SBO.

  9. Control of the sensitivity of CRLH interdigital microstrip balanced structures using a co-design genetic algorithm approach

    Science.gov (United States)

    Siragusa, R.; Perret, E.; Nguyen, H. V.; Lemaître-Auger, P.; Tedjini, S.; Caloz, C.

    2011-06-01

    A fully automated tool for designing CRLH interdigital microstrip structures using a co-design synthesis computational approach is proposed and demonstrated experimentally. This approach uses an electromagnetic simulator in conjunction with a genetic algorithm to synthesize and optimize a balanced CRLH interdigital microstrip transmission line. The high sensitivity of a long balanced transmission line to fabrication tolerances is controlled by the use of a high precision 3D simulator. The 2.5D simulator used was found insufficient for a large number of unit cells. A 13 UC CRLH transmission line is designed with the proposed approach. The response sensitivity of the balanced transmission lines to the over/under-etching factor is highlighted by comparing the measurements of four lines with different factors. The effect of over/under-etching is significant for values larger than 10 μm.

  10. Multi-Objective Genetic Algorithm Optimisation Approach for the Geometrical Design of an Active Noise Control Systems

    Directory of Open Access Journals (Sweden)

    N. Jafferi

    2009-09-01

    Full Text Available This paper focuses on the geometrical design of active noise control (ANC in free- field propagation medium. The development and performance assessment uses genetic optimisation techniques to arrange system components so as to satisfy several performance requirements, such as physical extent of cancellation, controller design restriction and system stability. The ANC system design can be effectively addressed if it is considered as multi – objective optimisation problems. The multi-objective genetic algorithms (MOGAs are well suited to the design of an ANC system and the approach used for it is based on a multi - objective method, with which the physical extent of cancellation and relative stability assessment are dealt with simultaneously.

  11. An Approach to automatically optimize the Hydraulic performance of Blade System for Hydraulic Machines using Multi-objective Genetic Algorithm

    Science.gov (United States)

    Lai, Xide; Chen, Xiaoming; Zhang, Xiang; Lei, Mingchuan

    2016-11-01

    This paper presents an approach to automatic hydraulic optimization of hydraulic machine's blade system combining a blade geometric modeller and parametric generator with automatic CFD solution procedure and multi-objective genetic algorithm. In order to evaluate a plurality of design options and quickly estimate the blade system's hydraulic performance, the approximate model which is able to substitute for the original inside optimization loop has been employed in the hydraulic optimization of blade by using function approximation. As the approximate model is constructed through the database samples containing a set of blade geometries and their resulted hydraulic performances, it can ensure to correctly imitate the real blade's performances predicted by the original model. As hydraulic machine designers are accustomed to do design with 2D blade profiles on stream surface that are then stacked to 3D blade geometric model in the form of NURBS surfaces, geometric variables to be optimized were defined by a series profiles on stream surfaces. The approach depends on the cooperation between a genetic algorithm, a database and user defined objective functions and constraints which comprises hydraulic performances, structural and geometric constraint functions. Example covering optimization design of a mixed-flow pump impeller is presented.

  12. Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms

    CERN Document Server

    Siddique, Nazmul

    2014-01-01

    Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller.  The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...

  13. Multidimensional Scaling and Genetic Algorithms : A Solution Approach to Avoid Local Minima

    OpenAIRE

    Etschberger, Stefan; Hilbert, Andreas

    2002-01-01

    Multidimensional scaling is very common in exploratory data analysis. It is mainly used to represent sets of objects with respect to their proximities in a low dimensional Euclidean space. Widely used optimization algorithms try to improve the representation via shifting its coordinates in direction of the negative gradient of a corresponding fit function. Depending on the initial configuration, the chosen algorithm and its parameter settings there is a possibility for the algorithm to termin...

  14. Genetic algorithm approaches for conceptual design of spacecraft systems including multi-objective optimization and design under uncertainty

    Science.gov (United States)

    Hassan, Rania A.

    In the design of complex large-scale spacecraft systems that involve a large number of components and subsystems, many specialized state-of-the-art design tools are employed to optimize the performance of various subsystems. However, there is no structured system-level concept-architecting process. Currently, spacecraft design is heavily based on the heritage of the industry. Old spacecraft designs are modified to adapt to new mission requirements, and feasible solutions---rather than optimal ones---are often all that is achieved. During the conceptual phase of the design, the choices available to designers are predominantly discrete variables describing major subsystems' technology options and redundancy levels. The complexity of spacecraft configurations makes the number of the system design variables that need to be traded off in an optimization process prohibitive when manual techniques are used. Such a discrete problem is well suited for solution with a Genetic Algorithm, which is a global search technique that performs optimization-like tasks. This research presents a systems engineering framework that places design requirements at the core of the design activities and transforms the design paradigm for spacecraft systems to a top-down approach rather than the current bottom-up approach. To facilitate decision-making in the early phases of the design process, the population-based search nature of the Genetic Algorithm is exploited to provide computationally inexpensive---compared to the state-of-the-practice---tools for both multi-objective design optimization and design optimization under uncertainty. In terms of computational cost, those tools are nearly on the same order of magnitude as that of standard single-objective deterministic Genetic Algorithm. The use of a multi-objective design approach provides system designers with a clear tradeoff optimization surface that allows them to understand the effect of their decisions on all the design objectives

  15. A Novel Approach for Solving Travelling Salesman Problem and Compare Its Performance with Inver-Over Operator of Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Syed Tauhid Zuhori

    2012-02-01

    Full Text Available The traveling salesman problem (TSP is one of the most widely studied NP hard combinatorial optimization problems and has already solved in the semi-optimal manners using numbers of different methods. Among them, Genetic Algorithms (GA are pre-dominating. In this paper I solve the problem with a new operator, Inver-over, for an evolutionary algorithm for the TSP. This operator outperforms all other 'genetic' operators, whether unary or binary, which was first introduced by Guo Tao and Zbigniew Michalewicz. I also propose a new algorithm for solving TSP. To get a comparative idea of the performance of these algorithms I solve same problems with the two algorithms. The performance analysis shows that my proposed algorithm produces relatively better solutions in the case of the tour length every time. But when we increase the cities it takes more time to solve than the Inver-Over operator for TSP.

  16. A parameter-tuned genetic algorithm for statistically constrained economic design of multivariate CUSUM control charts: a Taguchi loss approach

    Science.gov (United States)

    Niaki, Seyed Taghi Akhavan; Javad Ershadi, Mohammad

    2012-12-01

    In this research, the main parameters of the multivariate cumulative sum (CUSUM) control chart (the reference value k, the control limit H, the sample size n and the sampling interval h) are determined by minimising the Lorenzen-Vance cost function [Lorenzen, T.J., and Vance, L.C. (1986), 'The Economic Design of Control Charts: A Unified Approach', Technometrics, 28, 3-10], in which the external costs of employing the chart are added. In addition, the model is statistically constrained to achieve desired in-control and out-of-control average run lengths. The Taguchi loss approach is used to model the problem and a genetic algorithm, for which its main parameters are tuned using the response surface methodology (RSM), is proposed to solve it. At the end, sensitivity analyses on the main parameters of the cost function are presented and their practical conclusions are drawn. The results show that RSM significantly improves the performance of the proposed algorithm and the external costs of applying the chart, which are due to real-world constraints, do not increase the average total loss very much.

  17. Contrast improvement of continuous wave diffuse optical tomography reconstruction by hybrid approach using least square and genetic algorithm.

    Science.gov (United States)

    Patra, Rusha; Dutta, Pranab K

    2015-07-01

    Reconstruction of the absorption coefficient of tissue with good contrast is of key importance in functional diffuse optical imaging. A hybrid approach using model-based iterative image reconstruction and a genetic algorithm is proposed to enhance the contrast of the reconstructed image. The proposed method yields an observed contrast of 98.4%, mean square error of 0.638×10⁻³, and object centroid error of (0.001 to 0.22) mm. Experimental validation of the proposed method has also been provided with tissue-like phantoms which shows a significant improvement in image quality and thus establishes the potential of the method for functional diffuse optical tomography reconstruction with continuous wave setup. A case study of finger joint imaging is illustrated as well to show the prospect of the proposed method in clinical diagnosis. The method can also be applied to the concentration measurement of a region of interest in a turbid medium.

  18. Automatic Case-Based Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Jie Dou

    2015-04-01

    Full Text Available This paper proposes an automatic method for detecting landslides by using an integrated approach comprising object-oriented image analysis (OOIA, a genetic algorithm (GA, and a case-based reasoning (CBR technique. It consists of three main phases: (1 image processing and multi-image segmentation; (2 feature optimization; and (3 detecting landslides. The proposed approach was employed in a fast-growing urban region, the Pearl River Delta in South China. The results of detection were validated with the help of field surveys. The experimental results indicated that the proposed OOIA-GA-CBR (0.87 demonstrates higher classification performance than the stand-alone OOIA (0.75 method for detecting landslides. The area under curve (AUC value was also higher than that of the simple OOIA, indicating the high efficiency of the proposed landslide detection approach. The case library created using the integrated model can be reused for time-independent analysis, thus rendering our approach superior in comparison to other traditional methods, such as the maximum likelihood classifier. The results of this study thus facilitate fast generation of accurate landslide inventory maps, which will eventually extend our understanding of the evolution of landscapes shaped by landslide processes.

  19. Evolving evolutionary algorithms using linear genetic programming.

    Science.gov (United States)

    Oltean, Mihai

    2005-01-01

    A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.

  20. Incremental multiple objective genetic algorithms.

    Science.gov (United States)

    Chen, Qian; Guan, Sheng-Uei

    2004-06-01

    This paper presents a new genetic algorithm approach to multiobjective optimization problems--incremental multiple objective genetic algorithms (IMOGA). Different from conventional MOGA methods, it takes each objective into consideration incrementally. The whole evolution is divided into as many phases as the number of objectives, and one more objective is considered in each phase. Each phase is composed of two stages. First, an independent population is evolved to optimize one specific objective. Second, the better-performing individuals from the single-objecive population evolved in the above stage and the multiobjective population evolved in the last phase are joined together by the operation of integration. The resulting population then becomes an initial multiobjective population, to which a multiobjective evolution based on the incremented objective set is applied. The experiment results show that, in most problems, the performance of IMOGA is better than that of three other MOGAs, NSGA-II, SPEA, and PAES. IMOGA can find more solutions during the same time span, and the quality of solutions is better.

  1. Hybrid Genetic Algorithms with Fuzzy Logic Controller

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    In this paper, a new implementation of genetic algorithms (GAs) is developed for the machine scheduling problem, which is abundant among the modern manufacturing systems. The performance measure of early and tardy completion of jobs is very natural as one's aim, which is usually to minimize simultaneously both earliness and tardiness of all jobs. As the problem is NP-hard and no effective algorithms exist, we propose a hybrid genetic algorithms approach to deal with it. We adjust the crossover and mutation probabilities by fuzzy logic controller whereas the hybrid genetic algorithm does not require preliminary experiments to determine probabilities for genetic operators. The experimental results show the effectiveness of the GAs method proposed in the paper.``

  2. On the local optimal solutions of metabolic regulatory networks using information guided genetic algorithm approach and clustering analysis.

    Science.gov (United States)

    Zheng, Ying; Yeh, Chen-Wei; Yang, Chi-Da; Jang, Shi-Shang; Chu, I-Ming

    2007-08-31

    Biological information generated by high-throughput technology has made systems approach feasible for many biological problems. By this approach, optimization of metabolic pathway has been successfully applied in the amino acid production. However, in this technique, gene modifications of metabolic control architecture as well as enzyme expression levels are coupled and result in a mixed integer nonlinear programming problem. Furthermore, the stoichiometric complexity of metabolic pathway, along with strong nonlinear behaviour of the regulatory kinetic models, directs a highly rugged contour in the whole optimization problem. There may exist local optimal solutions wherein the same level of production through different flux distributions compared with global optimum. The purpose of this work is to develop a novel stochastic optimization approach-information guided genetic algorithm (IGA) to discover the local optima with different levels of modification of the regulatory loop and production rates. The novelties of this work include the information theory, local search, and clustering analysis to discover the local optima which have physical meaning among the qualified solutions.

  3. Genetic Algorithms for multiple objective vehicle routing

    CERN Document Server

    Geiger, Martin Josef

    2008-01-01

    The talk describes a general approach of a genetic algorithm for multiple objective optimization problems. A particular dominance relation between the individuals of the population is used to define a fitness operator, enabling the genetic algorithm to adress even problems with efficient, but convex-dominated alternatives. The algorithm is implemented in a multilingual computer program, solving vehicle routing problems with time windows under multiple objectives. The graphical user interface of the program shows the progress of the genetic algorithm and the main parameters of the approach can be easily modified. In addition to that, the program provides powerful decision support to the decision maker. The software has proved it's excellence at the finals of the European Academic Software Award EASA, held at the Keble college/ University of Oxford/ Great Britain.

  4. Adaptive Genetic Algorithm Model for Intrusion Detection

    Directory of Open Access Journals (Sweden)

    K. S. Anil Kumar

    2012-09-01

    Full Text Available Intrusion detection systems are intelligent systems designed to identify and prevent the misuse of computer networks and systems. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Thus the emerging network security systems need be part of the life system and this ispossible only by embedding knowledge into the network. The Adaptive Genetic Algorithm Model - IDS comprising of K-Means clustering Algorithm, Genetic Algorithm and Neural Network techniques. Thetechnique is tested using multitude of background knowledge sets in DARPA network traffic datasets.

  5. A New Approach to Generate the Recursive Sequence in Image Cryptography Using Genetic AlgorithmA New Approach to Generate the Recursive Sequence in Image Cryptography Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    z. talebi

    2014-07-01

    Full Text Available Digital image has special cryptography algorithms for its specific properties. A mathematics sequence in most image cryptography has been used for image scrambling. The used mathematics sequence has a recursive equation which it has some coefficients that changes of these coefficients can generate different sequences. Performance of this sequence in image cryptography is evaluated with different standard criteria. Due to complexity of system and no direct relation between the coefficient and evaluation criteria, selection of the suitable coefficient is not easily possible. In this article, by considering a general form of recursive equation and define a fitness function, the proper coefficients are calculated by genetic algorithm that satisfies the evaluation criteria. The experimental results show that recursive equation that is computed by the genetic algorithm has satisfactory performance from some schemes.

  6. Multicast Routing Based on Hybrid Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    CAO Yuan-da; CAI Gui

    2005-01-01

    A new multicast routing algorithm based on the hybrid genetic algorithm (HGA) is proposed. The coding pattern based on the number of routing paths is used. A fitness function that is computed easily and makes algorithm quickly convergent is proposed. A new approach that defines the HGA's parameters is provided. The simulation shows that the approach can increase largely the convergent ratio, and the fitting values of the parameters of this algorithm are different from that of the original algorithms. The optimal mutation probability of HGA equals 0.50 in HGA in the experiment, but that equals 0.07 in SGA. It has been concluded that the population size has a significant influence on the HGA's convergent ratio when it's mutation probability is bigger. The algorithm with a small population size has a high average convergent rate. The population size has little influence on HGA with the lower mutation probability.

  7. Multi-objective optimization of traf﬿c externalities using tolls: A comparison of genetic algorithm and game theoretical approach

    NARCIS (Netherlands)

    Ohazulike, Anthony; Brands, T.

    2013-01-01

    Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally

  8. Multi-objective Optimization of Traffic Externalities using Tolls: A Comparison of Genetic Algorithm with Game Theoretical Approach.

    NARCIS (Netherlands)

    Ohazulike, Anthony; Brands, Ties

    2013-01-01

    Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally

  9. Multi-objective Optimization of Traffic Externalities using Tolls: A Comparison of Genetic Algorithm with Game Theoretical Approach.

    NARCIS (Netherlands)

    Ohazulike, A.E.; Brands, T.

    2013-01-01

    Genetic algorithms (GAs) are widely accepted by researchers as a method of solving multi-objective optimization problems (MOPs), at least for listing a high quality approximation of the Pareto front of a MOP. In traffic management, it has been long established that tolls can be used to optimally dis

  10. An Improved Genetic Algorithm for Power Losses Minimization using Distribution Network Reconfiguration Based on Re-rank Approach

    Directory of Open Access Journals (Sweden)

    N.H. Shamsudin

    2014-08-01

    Full Text Available This study presents the implementation of Improved Genetic Algorithm (IGA to minimize the power losses in the distribution network by improving selection operator pertaining to the least losses generated from the algorithm. The major part of power losses in electrical power network was highly contributed from the distribution system. Thus, the need of restructuring the topological of distribution network configuration from its primary feeders should be considered. The switches identification within different probabilities cases for reconfiguration purposes are comprehensively implemented through the proposed algorithm. The investigation was conducted to test the proposed algorithm on the 33 radial busses system and found to give the better results in minimizing power losses and voltage profile.

  11. New Hybrid Genetic Algorithm for Vertex Cover Problems

    Institute of Scientific and Technical Information of China (English)

    霍红卫; 许进

    2003-01-01

    This paper presents a new hybrid genetic algorithm for the vertex cover problems in which scan-repair and local improvement techniques are used for local optimization. With the hybrid approach, genetic algorithms are used to perform global exploration in a population, while neighborhood search methods are used to perform local exploitation around the chromosomes. The experimental results indicate that hybrid genetic algorithms can obtain solutions of excellent quality to the problem instances with different sizes. The pure genetic algorithms are outperformed by the neighborhood search heuristics procedures combined with genetic algorithms.

  12. Genetic algorithm approach to global optimization of the full-dimensional potential energy surface for hydrogen atom at fcc-metal surfaces

    Science.gov (United States)

    Kammler, Marvin; Janke, Svenja M.; Kandratsenka, Alexander; Wodtke, Alec M.

    2017-09-01

    We have developed a genetic algorithm approach for the parametrization of a multi-dimensional potential energy surface based on the analytical expression for energy derived from Effective Medium Theory by fitting it to DFT data. This approach yields consistent results for the H-atom interaction energy with a number of fcc-metal surfaces (Al, Ag, Au, Cu, Ni, Pd, Pt and Rh) and provides reasonable energy values for virtually any system geometry including various facets.

  13. An investigation of messy genetic algorithms

    Science.gov (United States)

    Goldberg, David E.; Deb, Kalyanmoy; Korb, Bradley

    1990-01-01

    Genetic algorithms (GAs) are search procedures based on the mechanics of natural selection and natural genetics. They combine the use of string codings or artificial chromosomes and populations with the selective and juxtapositional power of reproduction and recombination to motivate a surprisingly powerful search heuristic in many problems. Despite their empirical success, there has been a long standing objection to the use of GAs in arbitrarily difficult problems. A new approach was launched. Results to a 30-bit, order-three-deception problem were obtained using a new type of genetic algorithm called a messy genetic algorithm (mGAs). Messy genetic algorithms combine the use of variable-length strings, a two-phase selection scheme, and messy genetic operators to effect a solution to the fixed-coding problem of standard simple GAs. The results of the study of mGAs in problems with nonuniform subfunction scale and size are presented. The mGA approach is summarized, both its operation and the theory of its use. Experiments on problems of varying scale, varying building-block size, and combined varying scale and size are presented.

  14. Hierarchical On-line Scheduling of Multiproduct Batch Plants with a Combined Approach of Mathematical Programming and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    陈理; 王克峰; 徐霄羽; 姚平经

    2004-01-01

    In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant. The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.

  15. Restarting and recentering genetic algorithm variations for DNA fragment assembly: The necessity of a multi-strategy approach.

    Science.gov (United States)

    Hughes, James Alexander; Houghten, Sheridan; Ashlock, Daniel

    2016-12-01

    DNA Fragment assembly - an NP-Hard problem - is one of the major steps in of DNA sequencing. Multiple strategies have been used for this problem, including greedy graph-based algorithms, deBruijn graphs, and the overlap-layout-consensus approach. This study focuses on the overlap-layout-consensus approach. Heuristics and computational intelligence methods are combined to exploit their respective benefits. These algorithm combinations were able to produce high quality results surpassing the best results obtained by a number of competitive algorithms specially designed and tuned for this problem on thirteen of sixteen popular benchmarks. This work also reinforces the necessity of using multiple search strategies as it is clearly observed that algorithm performance is dependent on problem instance; without a deeper look into many searches, top solutions could be missed entirely. Copyright © 2016. Published by Elsevier Ireland Ltd.

  16. Genetic Algorithm for Chinese Postman Problems

    Institute of Scientific and Technical Information of China (English)

    Jiang Hua; Kang Li-shan

    2003-01-01

    Chinese Postman Problem is an unsettled graphic problem. It was approached seldom by evolutionary computation. Now we use genetic algorithm to solve Chinese Postman Problem in undirected graph and get good results. It could be extended to solve Chinese postman problem in directed graph. We make these efforts for exploring in optimizing the mixed Chinese postman problem.

  17. Combinatorial Multiobjective Optimization Using Genetic Algorithms

    Science.gov (United States)

    Crossley, William A.; Martin. Eric T.

    2002-01-01

    The research proposed in this document investigated multiobjective optimization approaches based upon the Genetic Algorithm (GA). Several versions of the GA have been adopted for multiobjective design, but, prior to this research, there had not been significant comparisons of the most popular strategies. The research effort first generalized the two-branch tournament genetic algorithm in to an N-branch genetic algorithm, then the N-branch GA was compared with a version of the popular Multi-Objective Genetic Algorithm (MOGA). Because the genetic algorithm is well suited to combinatorial (mixed discrete / continuous) optimization problems, the GA can be used in the conceptual phase of design to combine selection (discrete variable) and sizing (continuous variable) tasks. Using a multiobjective formulation for the design of a 50-passenger aircraft to meet the competing objectives of minimizing takeoff gross weight and minimizing trip time, the GA generated a range of tradeoff designs that illustrate which aircraft features change from a low-weight, slow trip-time aircraft design to a heavy-weight, short trip-time aircraft design. Given the objective formulation and analysis methods used, the results of this study identify where turboprop-powered aircraft and turbofan-powered aircraft become more desirable for the 50 seat passenger application. This aircraft design application also begins to suggest how a combinatorial multiobjective optimization technique could be used to assist in the design of morphing aircraft.

  18. Golden ratio genetic algorithm based approach for modelling and analysis of the capacity expansion of urban road traffic network.

    Science.gov (United States)

    Zhang, Lun; Zhang, Meng; Yang, Wenchen; Dong, Decun

    2015-01-01

    This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers' route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity.

  19. Simultaneous stabilization using genetic algorithms

    Energy Technology Data Exchange (ETDEWEB)

    Benson, R.W.; Schmitendorf, W.E. (California Univ., Irvine, CA (USA). Dept. of Mechanical Engineering)

    1991-01-01

    This paper considers the problem of simultaneously stabilizing a set of plants using full state feedback. The problem is converted to a simple optimization problem which is solved by a genetic algorithm. Several examples demonstrate the utility of this method. 14 refs., 8 figs.

  20. Function Optimization Based on Quantum Genetic Algorithm

    OpenAIRE

    Ying Sun; Hegen Xiong

    2014-01-01

    Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded c...

  1. Function Optimization Based on Quantum Genetic Algorithm

    OpenAIRE

    Ying Sun; Yuesheng Gu; Hegen Xiong

    2013-01-01

    Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on.It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed ,which is called variable-boundary-coded quantum genetic algorithm (vbQGA) in which qubit chromosomes are collapsed into variableboundary- coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained.The m...

  2. Genetic algorithms for route discovery.

    Science.gov (United States)

    Gelenbe, Erol; Liu, Peixiang; Lainé, Jeremy

    2006-12-01

    Packet routing in networks requires knowledge about available paths, which can be either acquired dynamically while the traffic is being forwarded, or statically (in advance) based on prior information of a network's topology. This paper describes an experimental investigation of path discovery using genetic algorithms (GAs). We start with the quality-of-service (QoS)-driven routing protocol called "cognitive packet network" (CPN), which uses smart packets (SPs) to dynamically select routes in a distributed autonomic manner based on a user's QoS requirements. We extend it by introducing a GA at the source routers, which modifies and filters the paths discovered by the CPN. The GA can combine the paths that were previously discovered to create new untested but valid source-to-destination paths, which are then selected on the basis of their "fitness." We present an implementation of this approach, where the GA runs in background mode so as not to overload the ingress routers. Measurements conducted on a network test bed indicate that when the background-traffic load of the network is light to medium, the GA can result in improved QoS. When the background-traffic load is high, it appears that the use of the GA may be detrimental to the QoS experienced by users as compared to CPN routing because the GA uses less timely state information in its decision making.

  3. Hybrid Genetic Algorithms for University Course Timetabling

    Directory of Open Access Journals (Sweden)

    Meysam Shahvali Kohshori

    2012-03-01

    Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP are proposed: FGARI, FGASA and FGATS. In proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainly and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP.

  4. A numerical approach to ion channel modelling using whole-cell voltage-clamp recordings and a genetic algorithm.

    Directory of Open Access Journals (Sweden)

    Meron Gurkiewicz

    2007-08-01

    Full Text Available The activity of trans-membrane proteins such as ion channels is the essence of neuronal transmission. The currently most accurate method for determining ion channel kinetic mechanisms is single-channel recording and analysis. Yet, the limitations and complexities in interpreting single-channel recordings discourage many physiologists from using them. Here we show that a genetic search algorithm in combination with a gradient descent algorithm can be used to fit whole-cell voltage-clamp data to kinetic models with a high degree of accuracy. Previously, ion channel stimulation traces were analyzed one at a time, the results of these analyses being combined to produce a picture of channel kinetics. Here the entire set of traces from all stimulation protocols are analysed simultaneously. The algorithm was initially tested on simulated current traces produced by several Hodgkin-Huxley-like and Markov chain models of voltage-gated potassium and sodium channels. Currents were also produced by simulating levels of noise expected from actual patch recordings. Finally, the algorithm was used for finding the kinetic parameters of several voltage-gated sodium and potassium channels models by matching its results to data recorded from layer 5 pyramidal neurons of the rat cortex in the nucleated outside-out patch configuration. The minimization scheme gives electrophysiologists a tool for reproducing and simulating voltage-gated ion channel kinetics at the cellular level.

  5. GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design.

    Science.gov (United States)

    Pérez-Castillo, Yunierkis; Lazar, Cosmin; Taminau, Jonatan; Froeyen, Mathy; Cabrera-Pérez, Miguel Ángel; Nowé, Ann

    2012-09-24

    Computer-aided drug design has become an important component of the drug discovery process. Despite the advances in this field, there is not a unique modeling approach that can be successfully applied to solve the whole range of problems faced during QSAR modeling. Feature selection and ensemble modeling are active areas of research in ligand-based drug design. Here we introduce the GA(M)E-QSAR algorithm that combines the search and optimization capabilities of Genetic Algorithms with the simplicity of the Adaboost ensemble-based classification algorithm to solve binary classification problems. We also explore the usefulness of Meta-Ensembles trained with Adaboost and Voting schemes to further improve the accuracy, generalization, and robustness of the optimal Adaboost Single Ensemble derived from the Genetic Algorithm optimization. We evaluated the performance of our algorithm using five data sets from the literature and found that it is capable of yielding similar or better classification results to what has been reported for these data sets with a higher enrichment of active compounds relative to the whole actives subset when only the most active chemicals are considered. More important, we compared our methodology with state of the art feature selection and classification approaches and found that it can provide highly accurate, robust, and generalizable models. In the case of the Adaboost Ensembles derived from the Genetic Algorithm search, the final models are quite simple since they consist of a weighted sum of the output of single feature classifiers. Furthermore, the Adaboost scores can be used as ranking criterion to prioritize chemicals for synthesis and biological evaluation after virtual screening experiments.

  6. Results of Evolution Supervised by Genetic Algorithms

    CERN Document Server

    Jäntschi, Lorentz; Bălan, Mugur C; Sestraş, Radu E

    2010-01-01

    A series of results of evolution supervised by genetic algorithms with interest to agricultural and horticultural fields are reviewed. New obtained original results from the use of genetic algorithms on structure-activity relationships are reported.

  7. An integrated approach for risk object re-entry predictions in terms of KS elements and genetic algorithm

    Science.gov (United States)

    Sharma, R. K.; Anil Kumar, A. K.; Xavier James Raj, M.

    strategy (Sharma & Anilkumar 2003) adopted for the re-entry prediction of the risk objects by estimating the ballistic coefficient based on the TLEs. The estimation of the ballistic coefficient Bn = m/(CDA), where CD is the drag coefficient, A is the reference area, and m is the mass of the object, is done by minimizing a cost function (variation in the re-entry prediction time) using Genetic algorithm. The KS element equations of motion are numerically integrated with a suitable integration step size with the 4th - order Runge-Kutta-Gill method till the end of the orbital life, by including the Earth's oblateness with J2 to J6 terms, and modelling the air drag forces through an analytical oblate diurnal atmosphere with the density scale height varying with altitude. Jacchia (1977) atmospheric model, which takes into consideration the epoch, daily solar flux (F10.7) and geomagnetic index (Ap) for computation of density and density scale height, is utilized. The basic feature of the present approach is that the model and measurement errors are accountable in terms of adjusting the ballistic coefficient and hence the estimated Bn is not the actual ballistic coefficient but an effective ballistic coefficient. It is demonstrated that the inaccuracies or deficiencies in the inputs, like F10.7 and Ap values, are absorbed in the estimated Bn. The details of the re-entry results based on this approach, utilizing the TLE of debris objects, US Sat No. 25947 and SROSS-C2 Satellite, which re-entered the Earth's atmosphere on 4th March 2000 and 12th July 2001, are provided. Details of the re-entry predictions with respect to the 4th and 5th IADC re-entry campaigns, related to COSMOS 1043 rocket body and COSMOS 389 satellite, which re-entered the Earth's atmosphere on 19 January 2002 and 24 November 2003, respectively, are described. The predicted re-entries were found to be all along quite close to the actual re-entry time, with quite less uncertainties bands on the predictions. A

  8. Solving the Dial-a-Ride Problem using Genetic algorithms

    DEFF Research Database (Denmark)

    Bergvinsdottir, Kristin Berg; Larsen, Jesper; Jørgensen, Rene Munk

    service level constraints (Quality of Service). In this paper we present a genetic algorithm for solving the DARP. The algorithm is based on the classical cluster-first route-second approach, where it alternates between assigning customers to vehicles using a genetic algorithm and solving independent...... routing problems for the vehicles using a routing heuristic. The algorithm is implemented in Java and tested on publicly available data sets....

  9. Solving the Dial-a-Ride Problem using Genetic algorithms

    DEFF Research Database (Denmark)

    Bergvinsdottir, Kristin Berg; Larsen, Jesper; Jørgensen, Rene Munk

    service level constraints (Quality of Service). In this paper we present a genetic algorithm for solving the DARP. The algorithm is based on the classical cluster-first route-second approach, where it alternates between assigning customers to vehicles using a genetic algorithm and solving independent...... routing problems for the vehicles using a routing heuristic. The algorithm is implemented in Java and tested on publicly available data sets....

  10. Learning Bayesian networks using genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Chen Fei; Wang Xiufeng; Rao Yimei

    2007-01-01

    A new method to evaluate the fitness of the Bayesian networks according to the observed data is provided. The main advantage of this criterion is that it is suitable for both the complete and incomplete cases while the others not.Moreover it facilitates the computation greatly. In order to reduce the search space, the notation of equivalent class proposed by David Chickering is adopted. Instead of using the method directly, the novel criterion, variable ordering, and equivalent class are combined,moreover the proposed mthod avoids some problems caused by the previous one. Later, the genetic algorithm which allows global convergence, lack in the most of the methods searching for Bayesian network is applied to search for a good model in thisspace. To speed up the convergence, the genetic algorithm is combined with the greedy algorithm. Finally, the simulation shows the validity of the proposed approach.

  11. Routine Discovery of Complex Genetic Models using Genetic Algorithms.

    Science.gov (United States)

    Moore, Jason H; Hahn, Lance W; Ritchie, Marylyn D; Thornton, Tricia A; White, Bill C

    2004-02-01

    Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes.

  12. Improving the accuracy of low level quantum chemical calculation for absorption energies: the genetic algorithm and neural network approach.

    Science.gov (United States)

    Gao, Ting; Shi, Li-Li; Li, Hai-Bin; Zhao, Shan-Shan; Li, Hui; Sun, Shi-Ling; Su, Zhong-Min; Lu, Ying-Hua

    2009-07-07

    The combination of genetic algorithm and back-propagation neural network correction approaches (GABP) has successfully improved the calculation accuracy of absorption energies. In this paper, the absorption energies of 160 organic molecules are corrected to test this method. Firstly, the GABP1 is introduced to determine the quantitative relationship between the experimental results and calculations obtained by using quantum chemical methods. After GABP1 correction, the root-mean-square (RMS) deviations of the calculated absorption energies reduce from 0.32, 0.95 and 0.46 eV to 0.14, 0.19 and 0.18 eV for B3LYP/6-31G(d), B3LYP/STO-3G and ZINDO methods, respectively. The corrected results of B3LYP/6-31G(d)-GABP1 are in good agreement with experimental results. Then, the GABP2 is introduced to determine the quantitative relationship between the results of B3LYP/6-31G(d)-GABP1 method and calculations of the low accuracy methods (B3LYP/STO-3G and ZINDO). After GABP2 correction, the RMS deviations of the calculated absorption energies reduce to 0.20 and 0.19 eV for B3LYP/STO-3G and ZINDO methods, respectively. The results show that the RMS deviations after GABP1 and GABP2 correction are similar for B3LYP/STO-3G and ZINDO methods. Thus, the B3LYP/6-31G(d)-GABP1 is a better method to predict absorption energies and can be used as the approximation of experimental results where the experimental results are unknown or uncertain by experimental method. This method may be used for predicting absorption energies of larger organic molecules that are unavailable by experimental methods and by high-accuracy theoretical methods with larger basis sets. The performance of this method was demonstrated by application to the absorption energy of the aldehyde carbazole precursor.

  13. Nurse Rostering with Genetic Algorithms

    CERN Document Server

    Aickelin, Uwe

    2010-01-01

    In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle constraints and successful implementations usually require some sort of modification to enable the search to exploit problem specific knowledge in order to overcome this shortcoming. This paper is concerned with the development of a family of genetic algorithms for the solution of a nurse rostering problem at a major UK hospital. The hospital is made up of wards of up to 30 nurses. Each ward has its own group of nurses whose shifts have to be scheduled on a weekly basis. In addition to fulfilling the minimum demand for staff over three daily shifts, nurses' wishes and qualifications have to be taken into account. The schedules must also be seen to be fair, in tha...

  14. Focused Crawler Optimization Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Hartanto Kusuma Wardana

    2011-12-01

    Full Text Available As the size of the Web continues to grow, searching it for useful information has become more difficult. Focused crawler intends to explore the Web conform to a specific topic. This paper discusses the problems caused by local searching algorithms. Crawler can be trapped within a limited Web community and overlook suitable Web pages outside its track. A genetic algorithm as a global searching algorithm is modified to address the problems. The genetic algorithm is used to optimize Web crawling and to select more suitable Web pages to be fetched by the crawler. Several evaluation experiments are conducted to examine the effectiveness of the approach. The crawler delivers collections consist of 3396 Web pages from 5390 links which had been visited, or filtering rate of Roulette-Wheel selection at 63% and precision level at 93% in 5 different categories. The result showed that the utilization of genetic algorithm had empowered focused crawler to traverse the Web comprehensively, despite it relatively small collections. Furthermore, it brought up a great potential for building an exemplary collections compared to traditional focused crawling methods.

  15. Design of hyperbolic metamaterials by genetic algorithm

    Science.gov (United States)

    Goforth, Ian A.; Alisafaee, Hossein; Fullager, Daniel B.; Rosenbury, Chris; Fiddy, Michael A.

    2014-09-01

    We explain the design of one dimensional Hyperbolic Metamaterials (HMM) using a genetic algorithm (GA) and provide sample applications including the realization of negative refraction. The design method is a powerful optimization approach to find the optimal performance of such structures, which "naturally" finds HMM structures that are globally optimized for specific applications. We explain how a fitness function can be incorporated into the GA for different metamaterial properties.

  16. Web Based Genetic Algorithm Using Data Mining

    OpenAIRE

    Ashiqur Rahman; Asaduzzaman Noman; Md. Ashraful Islam; Al-Amin Gaji

    2016-01-01

    This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize the prediction accuracy and get a marked improvement over raw classification. It further shows that when the number of features is few; fea...

  17. Genetic Algorithms in Dynamical Systems Optimisation and Adaptation

    NARCIS (Netherlands)

    Reus, N.M. de; Visser, E.K.; Bruggeman, B.

    1998-01-01

    Both in the design of dynamical systems, ranging from control systems to state estimators as in the adaptation of these systems the use of genetic algorithms is worth studying. This paper presents some approaches for using genetic algorithms in dynamical systems. The layouts and specific uses are di

  18. A comparison of genetic algorithm and artificial bee colony approaches in solving blocking hybrid flowshop scheduling problem with sequence dependent setup/changeover times

    Directory of Open Access Journals (Sweden)

    Pongpan Nakkaew

    2016-06-01

    Full Text Available In manufacturing process where efficiency is crucial in order to remain competitive, flowshop is a common configuration in which machines are arranged in series and products are produced through the stages one by one. In certain production processes, the machines are frequently configured in the way that each production stage may contain multiple processing units in parallel or hybrid. Moreover, along with precedent conditions, the sequence dependent setup times may exist. Finally, in case there is no buffer, a machine is said to be blocked if the next stage to handle its output is being occupied. Such NP-Hard problem, referred as Blocking Hybrid Flowshop Scheduling Problem with Sequence Dependent Setup/Changeover Times, is usually not possible to find the best exact solution to satisfy optimization objectives such as minimization of the overall production time. Thus, it is usually solved by approximate algorithms such as metaheuristics. In this paper, we investigate comparatively the effectiveness of the two approaches: a Genetic Algorithm (GA and an Artificial Bee Colony (ABC algorithm. GA is inspired by the process of natural selection. ABC, in the same manner, resembles the way types of bees perform specific functions and work collectively to find their foods by means of division of labor. Additionally, we apply an algorithm to improve the GA and ABC algorithms so that they can take advantage of parallel processing resources of modern multiple core processors while eliminate the need for screening the optimal parameters of both algorithms in advance.

  19. THE APPROACHING TRAIN DETECTION ALGORITHM

    Directory of Open Access Journals (Sweden)

    S. V. Bibikov

    2015-09-01

    Full Text Available The paper deals with detection algorithm for rail vibroacoustic waves caused by approaching train on the background of increased noise. The urgency of algorithm development for train detection in view of increased rail noise, when railway lines are close to roads or road intersections is justified. The algorithm is based on the method of weak signals detection in a noisy environment. The information statistics ultimate expression is adjusted. We present the results of algorithm research and testing of the train approach alarm device that implements the proposed algorithm. The algorithm is prepared for upgrading the train approach alarm device “Signalizator-P".

  20. Preparation of agar nanospheres: comparison of response surface and artificial neural network modeling by a genetic algorithm approach.

    Science.gov (United States)

    Zaki, Mohammad Reza; Varshosaz, Jaleh; Fathi, Milad

    2015-05-20

    Multivariate nature of drug loaded nanospheres manufacturing in term of multiplicity of involved factors makes it a time consuming and expensive process. In this study genetic algorithm (GA) and artificial neural network (ANN), two tools inspired by natural process, were employed to optimize and simulate the manufacturing process of agar nanospheres. The efficiency of GA was evaluated against the response surface methodology (RSM). The studied responses included particle size, poly dispersity index, zeta potential, drug loading and release efficiency. GA predicted greater extremum values for response factors compared to RSM. However, real values showed some deviations from predicted data. Appropriate agreement was found between ANN model predicted and real values for all five response factors with high correlation coefficients. GA was more successful than RSM in optimization and along with ANN were efficient tools in optimizing and modeling the fabrication process of drug loaded in agar nanospheres.

  1. Genetic Algorithm for Optimization: Preprocessor and Algorithm

    Science.gov (United States)

    Sen, S. K.; Shaykhian, Gholam A.

    2006-01-01

    Genetic algorithm (GA) inspired by Darwin's theory of evolution and employed to solve optimization problems - unconstrained or constrained - uses an evolutionary process. A GA has several parameters such the population size, search space, crossover and mutation probabilities, and fitness criterion. These parameters are not universally known/determined a priori for all problems. Depending on the problem at hand, these parameters need to be decided such that the resulting GA performs the best. We present here a preprocessor that achieves just that, i.e., it determines, for a specified problem, the foregoing parameters so that the consequent GA is a best for the problem. We stress also the need for such a preprocessor both for quality (error) and for cost (complexity) to produce the solution. The preprocessor includes, as its first step, making use of all the information such as that of nature/character of the function/system, search space, physical/laboratory experimentation (if already done/available), and the physical environment. It also includes the information that can be generated through any means - deterministic/nondeterministic/graphics. Instead of attempting a solution of the problem straightway through a GA without having/using the information/knowledge of the character of the system, we would do consciously a much better job of producing a solution by using the information generated/created in the very first step of the preprocessor. We, therefore, unstintingly advocate the use of a preprocessor to solve a real-world optimization problem including NP-complete ones before using the statistically most appropriate GA. We also include such a GA for unconstrained function optimization problems.

  2. Improved genetic operator for genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    林峰; 杨启文

    2002-01-01

    The mutation operator has been seldom improved because researchers ha rdly suspect its ability to prevent genetic algorithm (GA) from converging prema turely. Due to its i mportance to GA, the authors of this paper study its influence on the diversity of genes in the same locus, and point out that traditional mutation, to some ext ent, can result in premature convergence of genes (PCG) in the same locus. The a bove drawback of the traditional mutation operator causes the loss of critical a lleles. Inspired by digital technique, we introduce two kinds of boolean operati on into GA to develop a novel mutation operator and discuss its contribution to preventing the loss of critical alleles. The experimental results of function op timization show that the improved mutation operator can effectively prevent prem ature convergence, and can provide a wide selection range of control parameters for GA.

  3. Improved genetic operator for genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    林峰; 杨启文

    2002-01-01

    The mutation operator has been seldom improved because ressearchers hardly suspect its ability to prevent genetic algorithm(GA) from converging prematurely.Due to its importance to GA,the authors of this paper study influence on the diversity of genes in the same locus,and point out that traditional mutation,to some extent,can result in premature convergence of genes(PCG) in the same locus.The above drawback of the traditional mutation operator causes the loss of critical alleles.Inspired by digital technique,we introduce two kinds of boolean operation into GA to develop a novel mutation operator and discuss its contribution of preventing the loss of critical alleles.The experimental results of function optimizatioin show that the improved mutation operator can effectively prevent premature convegence,and can provide a wide selection range of control parameters for GA.

  4. Genetic Algorithm Based Proportional Integral Controller Design for Induction Motor

    Directory of Open Access Journals (Sweden)

    Mohanasundaram Kuppusamy

    2011-01-01

    Full Text Available Problem statement: This study has expounded the application of evolutionary computation method namely Genetic Algorithm (GA for estimation of feedback controller parameters for induction motor. GA offers certain advantages such as simple computational steps, derivative free optimization, reduced number of iterations and assured near global optima. The development of the method is well documented and computed and measured results are presented. Approach: The design of PI controller parameter for three phase induction motor drives was done using Genetic Algorithm. The objective function of motor current reduction, using PI controller, at starting is formulated as an optimization problem and solved with Genetic Algorithm. Results: The results showed the selected values of PI controller parameter using genetic algorithm approach, with objective of induction motor starting current reduction. Conclusions/Recommendation: The results proved the robustness and easy implementation of genetic algorithm selection of PI parameters for induction motor starting.

  5. Genetic algorithms for minimal source reconstructions

    Energy Technology Data Exchange (ETDEWEB)

    Lewis, P.S.; Mosher, J.C.

    1993-12-01

    Under-determined linear inverse problems arise in applications in which signals must be estimated from insufficient data. In these problems the number of potentially active sources is greater than the number of observations. In many situations, it is desirable to find a minimal source solution. This can be accomplished by minimizing a cost function that accounts from both the compatibility of the solution with the observations and for its ``sparseness``. Minimizing functions of this form can be a difficult optimization problem. Genetic algorithms are a relatively new and robust approach to the solution of difficult optimization problems, providing a global framework that is not dependent on local continuity or on explicit starting values. In this paper, the authors describe the use of genetic algorithms to find minimal source solutions, using as an example a simulation inspired by the reconstruction of neural currents in the human brain from magnetoencephalographic (MEG) measurements.

  6. 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…

  7. 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…

  8. Filter selection using genetic algorithms

    Science.gov (United States)

    Patel, Devesh

    1996-03-01

    Convolution operators act as matched filters for certain types of variations found in images and have been extensively used in the analysis of images. However, filtering through a bank of N filters generates N filtered images, consequently increasing the amount of data considerably. Moreover, not all these filters have the same discriminatory capabilities for the individual images, thus making the task of any classifier difficult. In this paper, we use genetic algorithms to select a subset of relevant filters. Genetic algorithms represent a class of adaptive search techniques where the processes are similar to natural selection of biological evolution. The steady state model (GENITOR) has been used in this paper. The reduction of filters improves the performance of the classifier (which in this paper is the multi-layer perceptron neural network) and furthermore reduces the computational requirement. In this study we use the Laws filters which were proposed for the analysis of texture images. Our aim is to recognize the different textures on the images using the reduced filter set.

  9. Transonic Wing Shape Optimization Using a Genetic Algorithm

    Science.gov (United States)

    Holst, Terry L.; Pulliam, Thomas H.; Kwak, Dochan (Technical Monitor)

    2002-01-01

    A method for aerodynamic shape optimization based on a genetic algorithm approach is demonstrated. The algorithm is coupled with a transonic full potential flow solver and is used to optimize the flow about transonic wings including multi-objective solutions that lead to the generation of pareto fronts. The results indicate that the genetic algorithm is easy to implement, flexible in application and extremely reliable.

  10. Genetic Algorithms Principles Towards Hidden Markov Model

    Directory of Open Access Journals (Sweden)

    Nabil M. Hewahi

    2011-10-01

    Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
    out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.

  11. Excursion-Set-Mediated Genetic Algorithm

    Science.gov (United States)

    Noever, David; Baskaran, Subbiah

    1995-01-01

    Excursion-set-mediated genetic algorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other genetic algorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.

  12. Excursion-Set-Mediated Genetic Algorithm

    Science.gov (United States)

    Noever, David; Baskaran, Subbiah

    1995-01-01

    Excursion-set-mediated genetic algorithm (ESMGA) is embodiment of method of searching for and optimizing computerized mathematical models. Incorporates powerful search and optimization techniques based on concepts analogous to natural selection and laws of genetics. In comparison with other genetic algorithms, this one achieves stronger condition for implicit parallelism. Includes three stages of operations in each cycle, analogous to biological generation.

  13. Application of Genetic Algorithms in Seismic Tomography

    Science.gov (United States)

    Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet; Papazachos, Constantinos

    2010-05-01

    In the earth sciences several inverse problems that require data fitting and parameter estimation are nonlinear and can involve a large number of unknown parameters. Consequently, the application of analytical inversion or optimization techniques may be quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem in question, adopting an iterative procedure using partial derivatives to improve an initial model. This approach can lead to a dependence of the final model solution on the starting model and is prone to entrapment in local misfit minima. Moreover, the calculation of derivatives can be computationally inefficient and create instabilities when numerical approximations are used. In contrast to these local minimization methods, global techniques that do not rely on partial derivatives, are independent of the form of the data misfit criterion, and are computationally robust. Such methods often use random processes to sample a selected wider span of the model space. In this situation, randomly generated models are assessed in terms of their data-fitting quality and the process may be stopped after a certain number of acceptable models is identified or continued until a satisfactory data fit is achieved. A new class of methods known as genetic algorithms achieves the aforementioned approximation through novel model representation and manipulations. Genetic algorithms (GAs) were originally developed in the field of artificial intelligence by John Holland more than 20 years ago, but even in this field it is less than a decade that the methodology has been more generally applied and only recently did the methodology attract the attention of the earth sciences community. Applications have been generally concentrated in geophysics and in particular seismology. As awareness of genetic algorithms grows there surely will be many more and varied applications to earth science problems. In the present work, the

  14. Genetic Algorithms: Basic Concept and Applications

    Directory of Open Access Journals (Sweden)

    Ms. Amninder Kaur

    2013-07-01

    Full Text Available Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. Genetic algorithms have been applied to a wide range of practical problems often with valuable results. Genetic algorithms are often viewed as function optimizer, although the range of problems to which genetic algorithms have been applied are quite broad. This paper covers the basic concepts of genetic algorithms and their applications to a variety of fields. It also tries to give a solution to the problem of economic load dispatch using Genetic Algorithms. An attempt has been made to explain when and why GA should be used as an optimization tool. Finally, the paper points to future directions

  15. Genetic algorithms and fuzzy multiobjective optimization

    CERN Document Server

    Sakawa, Masatoshi

    2002-01-01

    Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...

  16. A NEW GENETIC SIMULATED ANNEALING ALGORITHM FOR FLOOD ROUTING MODEL

    Institute of Scientific and Technical Information of China (English)

    KANG Ling; WANG Cheng; JIANG Tie-bing

    2004-01-01

    In this paper, a new approach, the Genetic Simulated Annealing (GSA), was proposed for optimizing the parameters in the Muskingum routing model. By integrating the simulated annealing method into the genetic algorithm, the hybrid method could avoid some troubles of traditional methods, such as arduous trial-and-error procedure, premature convergence in genetic algorithm and search blindness in simulated annealing. The principle and implementing procedure of this algorithm were described. Numerical experiments show that the GSA can adjust the optimization population, prevent premature convergence and seek the global optimal result.Applications to the Nanyunhe River and Qingjiang River show that the proposed approach is of higher forecast accuracy and practicability.

  17. Distribution network design under demand uncertainty using genetic algorithm and Monte Carlo simulation approach: a case study in pharmaceutical industry

    Science.gov (United States)

    Izadi, Arman; Kimiagari, Ali Mohammad

    2014-05-01

    Distribution network design as a strategic decision has long-term effect on tactical and operational supply chain management. In this research, the location-allocation problem is studied under demand uncertainty. The purposes of this study were to specify the optimal number and location of distribution centers and to determine the allocation of customer demands to distribution centers. The main feature of this research is solving the model with unknown demand function which is suitable with the real-world problems. To consider the uncertainty, a set of possible scenarios for customer demands is created based on the Monte Carlo simulation. The coefficient of variation of costs is mentioned as a measure of risk and the most stable structure for firm's distribution network is defined based on the concept of robust optimization. The best structure is identified using genetic algorithms and 14 % reduction in total supply chain costs is the outcome. Moreover, it imposes the least cost variation created by fluctuation in customer demands (such as epidemic diseases outbreak in some areas of the country) to the logistical system. It is noteworthy that this research is done in one of the largest pharmaceutical distribution firms in Iran.

  18. Optimizing a multi-echelon supply chain network flow using nonlinear fuzzy multi-objective integer programming: Genetic algorithm approach

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Afshari

    2012-10-01

    Full Text Available The aim of this paper is to present mathematical models optimizing all materials flows in supply chain. In this research a fuzzy multi-objective nonlinear mixed- integer programming model with piecewise linear membership function is applied to design a multi echelon supply chain network (SCN by considering total transportation costs and capacities of all echelons with fuzzy objectives. The model that is proposed in this study has 4 fuzzy functions. The first function is minimizing the total transportation costs between all echelons (suppliers, factories, distribution centers (DCs and customers. The second one is minimizing holding and ordering cost on DCs. The third objective is minimizing the unnecessary and unused capacity of factories and DCs via decreasing variance of transported amounts between echelons. The forth is minimizing the number of total vehicles that ship the materials and products along with SCN. For solving such a problem, as nodes increases in SCN, the traditional method does not have ability to solve large scale problem. So, we applied a Meta heuristic method called Genetic Algorithm. The numerical example is real world applied and compared the results with each other demonstrate the feasibility of applying the proposed model to given problem, and also its advantages are discussed.

  19. Hybrid Genetic Algorithm with PSO Effect for Combinatorial Optimisation Problems

    Directory of Open Access Journals (Sweden)

    M. H. Mehta

    2012-12-01

    Full Text Available In engineering field, many problems are hard to solve in some definite interval of time. These problems known as “combinatorial optimisation problems” are of the category NP. These problems are easy to solve in some polynomial time when input size is small but as input size grows problems become toughest to solve in some definite interval of time. Long known conventional methods are not able to solve the problems and thus proper heuristics is necessary. Evolutionary algorithms based on behaviours of different animals and species have been invented and studied for this purpose. Genetic Algorithm is considered a powerful algorithm for solving combinatorial optimisation problems. Genetic algorithms work on these problems mimicking the human genetics. It follows principle of “survival of the fittest” kind of strategy. Particle swarm optimisation is a new evolutionary approach that copies behaviour of swarm in nature. However, neither traditional genetic algorithms nor particle swarm optimisation alone has been completely successful for solving combinatorial optimisation problems. Here a hybrid algorithm is proposed in which strengths of both algorithms are merged and performance of proposed algorithm is compared with simple genetic algorithm. Results show that proposed algorithm works definitely better than the simple genetic algorithm.

  20. Application of Chaos in Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    YANG Li-Jiang; CHEN Tian-Lun

    2002-01-01

    Through replacing Gaussian mutation operator in real-coded genetic algorithm with a chaotic mapping, wepresent a genetic algorithm with chaotic mutation. To examine this new algorithm, we applied our algorithm to functionoptimization problems and obtained good results. Furthermore the orbital points' distribution of chaotic mapping andthe effects of chaotic mutation with different parameters were studied in order to make the chaotic mutation mechanismbe utilized efficiently.

  1. Genetic Algorithms for Multiple-Choice Problems

    CERN Document Server

    Aickelin, Uwe

    2010-01-01

    This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structur...

  2. Asian Option Pricing Based on Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    YunzhongLiu; HuiyuXuan

    2004-01-01

    The cross-fertilization between artificial intelligence and computational finance has resulted in some of the most active research areas in financial engineering. One direction is the application of machine learning techniques to pricing financial products, which is certainly one of the most complex issues in finance. In the literature, when the interest rate,the mean rate of return and the volatility of the underlying asset follow general stochastic processes, the exact solution is usually not available. In this paper, we shall illustrate how genetic algorithms (GAs), as a numerical approach, can be potentially helpful in dealing with pricing. In particular, we test the performance of basic genetic algorithms by using it to the determination of prices of Asian options, whose exact solutions is known from Black-Scholesoption pricing theory. The solutions found by basic genetic algorithms are compared with the exact solution, and the performance of GAs is ewluated accordingly. Based on these ewluations, some limitations of GAs in option pricing are examined and possible extensions to future works are also proposed.

  3. Biomimetic use of genetic algorithms

    CERN Document Server

    Dessalles, Jean-Louis

    2011-01-01

    Genetic algorithms are considered as an original way to solve problems, probably because of their generality and of their "blind" nature. But GAs are also unusual since the features of many implementations (among all that could be thought of) are principally led by the biological metaphor, while efficiency measurements intervene only afterwards. We propose here to examine the relevance of these biomimetic aspects, by pointing out some fundamental similarities and divergences between GAs and the genome of living beings shaped by natural selection. One of the main differences comes from the fact that GAs rely principally on the so-called implicit parallelism, while giving to the mutation/selection mechanism the second role. Such differences could suggest new ways of employing GAs on complex problems, using complex codings and starting from nearly homogeneous populations.

  4. Nurse Scheduling Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Komgrit Leksakul

    2014-01-01

    Full Text Available This study applied engineering techniques to develop a nurse scheduling model that, while maintaining the highest level of service, simultaneously minimized hospital-staffing costs and equitably distributed overtime pay. In the mathematical model, the objective function was the sum of the overtime payment to all nurses and the standard deviation of the total overtime payment that each nurse received. Input data distributions were analyzed in order to formulate a simulation model to determine the optimal demand for nurses that met the hospital’s service standards. To obtain the optimal nurse schedule with the number of nurses acquired from the simulation model, we proposed a genetic algorithm (GA with two-point crossover and random mutation. After running the algorithm, we compared the expenses and number of nurses between the existing and our proposed nurse schedules. For January 2013, the nurse schedule obtained by GA could save 12% in staffing expenses per month and 13% in number of nurses when compare with the existing schedule, while more equitably distributing overtime pay between all nurses.

  5. Fuzzy Control of Chaotic System with Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FANG Jian-an; GUO Zhao-xia; SHAO Shi-huang

    2002-01-01

    A novel approach to control the unpredictable behavior of chaotic systems is presented. The control algorithm is based on fuzzy logic control technique combined with genetic algorithm. The use of fuzzy logic allows for the implementation of human "rule-of-thumb" approach to decision making by employing linguistic variables. An improved Genetic Algorithm (GA) is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule,and to automatically generate fuzzy control actions under each condition. Simulation results show that such an approach for the control of chaotic systems is both effective and robust.

  6. Evolutionary algorithms in genetic regulatory networks model

    CERN Document Server

    Raza, Khalid

    2012-01-01

    Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes. Modeling GRNs is significantly important in order to reveal fundamental cellular processes, examine gene functions and understanding their complex relationships. Understanding the interactions between genes gives rise to develop better method for drug discovery and diagnosis of the disease since many diseases are characterized by abnormal behaviour of the genes. In this paper we have reviewed various evolutionary algorithms-based approach for modeling GRNs and discussed various opportunities and challenges.

  7. The genetic algorithm for a signal enhancement

    Energy Technology Data Exchange (ETDEWEB)

    Karimova, L. [Laboratory of Computer Modelling, Institute of Mathematics, Pushkin Street 125, 480100 Almaty (Kazakhstan)]. E-mail: karimova@math.kz; Kuadykov, E. [Laboratory of Computer Modelling, Institute of Mathematics, Pushkin Street 125, 480100 Almaty (Kazakhstan); Makarenko, N. [Laboratory of Computer Modelling, Institute of Mathematics, Pushkin Street 125, 480100 Almaty (Kazakhstan)

    2004-11-21

    The paper is devoted to the problem of time series enhancement, which is based on the analysis of local regularity. The model construction using this analysis does not require any a priori assumption on the structure of the noise and the functional relationship between original signal and noise. The signal itself may be nowhere differentiable with rapidly varying local regularity, what is overcome with the help of the new technique of increasing the local Hoelder regularity of the signal under research. A new signal with prescribed regularity is constructed using the genetic algorithm. This approach is applied to enhancement of time series in the paleoclimatology, solar physics, dendrochronology, meteorology and hydrology.

  8. Parallel Genetic Algorithm for Alpha Spectra Fitting

    Science.gov (United States)

    García-Orellana, Carlos J.; Rubio-Montero, Pilar; González-Velasco, Horacio

    2005-01-01

    We present a performance study of alpha-particle spectra fitting using parallel Genetic Algorithm (GA). The method uses a two-step approach. In the first step we run parallel GA to find an initial solution for the second step, in which we use Levenberg-Marquardt (LM) method for a precise final fit. GA is a high resources-demanding method, so we use a Beowulf cluster for parallel simulation. The relationship between simulation time (and parallel efficiency) and processors number is studied using several alpha spectra, with the aim of obtaining a method to estimate the optimal processors number that must be used in a simulation.

  9. Genetic algorithms in seasonal demand forecasting

    OpenAIRE

    Chodak, Grzegorz; Kwaśnicki, Witold

    2000-01-01

    The method of forecasting seasonal demand applying genetic algorithm is presented. Specific form of used demand function is shown in the first section of the article. Next the method of identification of the function parameters using genetic algorithms is discussed. In the final section an example of applying proposed method to forecast real demand process is shown.

  10. Novel Quantum Genetic Algorithm and Its Applications

    Institute of Scientific and Technical Information of China (English)

    ZHANG Ge-xiang; LI Na; JIN Wei-dong; HU Lai-zhao

    2006-01-01

    By introducing strong parallelism of quantum computing into evolutionary algorithm,a novel quantum genetic algorithm (NQGA) is proposed.In NQGA,a novel approach for updating the rotation angles of quantum logic gates and a strategy for enhancing search capability and avoiding premature convergence are adopted.Several typical complex continuous functions are chosen to test the performance of NQGA.Also,NQGA is applied in selecting the best feature subset from a large number of features in radar emitter signal recognition.The testing and experimental results of feature selection show that NQGA presents good search capability,rapid convergence,short computing time,and ability to avoid premature convergence effectively.

  11. Structural and spectroscopic studies of water-alkaline earth ion micro clusters: an alternate approach using genetic algorithm in conjunction with quantum chemical methods

    Science.gov (United States)

    Ganguly Neogi, S.; Chaudhury, P.

    2014-08-01

    We present an approach of using a stochastic optimization technique namely genetic algorithm in association with quantum chemical methods to first elucidate structure and then infrared spectroscopy and thermochemistry of water-alkaline earth metal ion clusters. We show that an initial determination of structure using stochastic techniques and following it up with quantum chemical calculation can lead to much faster convergence to high quality structures for these systems. Infrared spectroscopic, thermochemical calculations and natural population analysis based charges on the central metal ions are done to further ascertain the correctness of the structures using our technique. We have done a comparative study with a pure density functional theory calculation and have shown that even for very poor starting guess geometries genetic algorithm in conjunction with density functional theory indeed converges to global structure while pure density functional theory can encounter problems in certain situations to arrive at global geometry. We have also discussed usefulness of Unimodal Normal distribution crossover for handling situation with real coded variables.

  12. Investigation of Web Mining Optimization Using Microbial Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Dipali Tungar

    2014-02-01

    Full Text Available In today's modern internet era peopleneed searching on the web and finding relevant information on the web to be efficient and fast. But traditional search engines like Google suppose to be more intelligent, still use the traditional crawling algorithms to find data relevant to the search query. But most of the times it returns irrelevant data as well which becomes confusing for the user. In a normal XML data the user inputs the search query in terms of a keyword or a question and the answer to the search query should be more precise and more relevant. So, using the traditional crawling algorithms over XML data would lead to irrelevant results. Genetic algorithms are the modern algorithms which replicates the Darwinian theory of the natural evolution. The genetic algorithms are best suited for the traditional search problem as the genetic algorithms always tend to return quality as solution for any domain data. It would be a good approach to investigate how the genetic algorithms would be suitable for the search over the XML data of different domains. So, this system implements a steady state tournament selection Microbial Genetic Algorithm over the XML data of the different domains. This would be an investigation of how the genetic algorithm would return accurate results over XML data of different domains.

  13. Genetic Algorithm Based Microscale Vehicle Emissions Modelling

    Directory of Open Access Journals (Sweden)

    Sicong Zhu

    2015-01-01

    Full Text Available There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2 are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.

  14. Dynamic Route Guidance Using Improved Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Zhanke Yu

    2013-01-01

    Full Text Available This paper presents an improved genetic algorithm (IGA for dynamic route guidance algorithm. The proposed IGA design a vicinity crossover technique and a greedy backward mutation technique to increase the population diversity and strengthen local search ability. The steady-state reproduction is introduced to protect the optimized genetic individuals. Furthermore the junction delay is introduced to the fitness function. The simulation results show the effectiveness of the proposed algorithm.

  15. Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

    Science.gov (United States)

    Holst, Terry L.

    2005-01-01

    A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

  16. Predicting Protein Structure Using Parallel Genetic Algorithms.

    Science.gov (United States)

    1994-12-01

    34 IEEE Transactions on Systems, Man and Cybernetics, 10(9) (September 1980). 16. De Jong, Kenneth A. "On Using Genetic Algoriths to Search Program...By " Predicting rotein Structure D istribticfiar.. ................ Using Parallel Genetic Algorithms ,Avaiu " ’ •"... Dist THESIS I IGeorge H...iiLite-d Approved for public release; distribution unlimited AFIT/ GCS /ENG/94D-03 Predicting Protein Structure Using Parallel Genetic Algorithms

  17. An adaptive genetic algorithm for solving bilevel linear programming problem

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems.Various methods are proposed for solving this problem. Of all the algorithms, the genetic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes may be infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.

  18. A GREEDY GENETIC ALGORITHM FOR UNCONSTRAINED GLOBAL OPTIMIZATION

    Institute of Scientific and Technical Information of China (English)

    ZHAO Xinchao

    2005-01-01

    The greedy algorithm is a strong local searching algorithm. The genetica lgorithm is generally applied to the global optimization problems. In this paper, we combine the greedy idea and the genetic algorithm to propose the greedy genetic algorithm which incorporates the global exploring ability of the genetic algorithm and the local convergent ability of the greedy algorithm. Experimental results show that greedy genetic algorithm gives much better results than the classical genetic algorithm.

  19. A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem

    OpenAIRE

    Gonçalves, José Fernando; Mendes, J. J. M.; Resende, Maurício G. C.

    2005-01-01

    This paper presents a hybrid genetic algorithm for the Job Shop Scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a priority rule in which the priorities are defined by the genetic algorithm. Schedules are constructed using a procedure that generates parameterized active schedules. After a schedule is obtained a local search heuristic is applied to improve the solution. The approach is tested on a set o...

  20. A genetic algorithm-Bayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid identification of Bacillus spores and classification of Bacillus species

    Directory of Open Access Journals (Sweden)

    Goodacre Royston

    2011-01-01

    Full Text Available Abstract Background The rapid identification of Bacillus spores and bacterial identification are paramount because of their implications in food poisoning, pathogenesis and their use as potential biowarfare agents. Many automated analytical techniques such as Curie-point pyrolysis mass spectrometry (Py-MS have been used to identify bacterial spores giving use to large amounts of analytical data. This high number of features makes interpretation of the data extremely difficult We analysed Py-MS data from 36 different strains of aerobic endospore-forming bacteria encompassing seven different species. These bacteria were grown axenically on nutrient agar and vegetative biomass and spores were analyzed by Curie-point Py-MS. Results We develop a novel genetic algorithm-Bayesian network algorithm that accurately identifies sand selects a small subset of key relevant mass spectra (biomarkers to be further analysed. Once identified, this subset of relevant biomarkers was then used to identify Bacillus spores successfully and to identify Bacillus species via a Bayesian network model specifically built for this reduced set of features. Conclusions This final compact Bayesian network classification model is parsimonious, computationally fast to run and its graphical visualization allows easy interpretation of the probabilistic relationships among selected biomarkers. In addition, we compare the features selected by the genetic algorithm-Bayesian network approach with the features selected by partial least squares-discriminant analysis (PLS-DA. The classification accuracy results show that the set of features selected by the GA-BN is far superior to PLS-DA.

  1. A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

    Directory of Open Access Journals (Sweden)

    Tugrul Talaslioglu

    2009-01-01

    Full Text Available A new genetic algorithm (GA methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS, is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA utilizing a single population, a multipopulation-based genetic algorithm (MPGA proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.

  2. An Indirect Genetic Algorithm for a Nurse Scheduling Problem

    CERN Document Server

    Aickelin, Uwe

    2008-01-01

    This paper describes a Genetic Algorithms approach to a manpower-scheduling problem arising at a major UK hospital. Although Genetic Algorithms have been successfully used for similar problems in the past, they always had to overcome the limitations of the classical Genetic Algorithms paradigm in handling the conflict between objectives and constraints. The approach taken here is to use an indirect coding based on permutations of the nurses, and a heuristic decoder that builds schedules from these permutations. Computational experiments based on 52 weeks of live data are used to evaluate three different decoders with varying levels of intelligence, and four well-known crossover operators. Results are further enhanced by introducing a hybrid crossover operator and by making use of simple bounds to reduce the size of the solution space. The results reveal that the proposed algorithm is able to find high quality solutions and is both faster and more flexible than a recently published Tabu Search approach.

  3. Genetic Algorithm for Solving Simple Mathematical Equality Problem

    OpenAIRE

    Hermawanto, Denny

    2013-01-01

    This paper explains genetic algorithm for novice in this field. Basic philosophy of genetic algorithm and its flowchart are described. Step by step numerical computation of genetic algorithm for solving simple mathematical equality problem will be briefly explained

  4. Solving Maximal Clique Problem through Genetic Algorithm

    Science.gov (United States)

    Rajawat, Shalini; Hemrajani, Naveen; Menghani, Ekta

    2010-11-01

    Genetic algorithm is one of the most interesting heuristic search techniques. It depends basically on three operations; selection, crossover and mutation. The outcome of the three operations is a new population for the next generation. Repeating these operations until the termination condition is reached. All the operations in the algorithm are accessible with today's molecular biotechnology. The simulations show that with this new computing algorithm, it is possible to get a solution from a very small initial data pool, avoiding enumerating all candidate solutions. For randomly generated problems, genetic algorithm can give correct solution within a few cycles at high probability.

  5. Function Optimization Based on Quantum Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Ying Sun

    2014-01-01

    Full Text Available Optimization method is important in engineering design and application. Quantum genetic algorithm has the characteristics of good population diversity, rapid convergence and good global search capability and so on. It combines quantum algorithm with genetic algorithm. A novel quantum genetic algorithm is proposed, which is called Variable-boundary-coded Quantum Genetic Algorithm (vbQGA in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. Therefore much shorter chromosome strings can be gained. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard Genetic Algorithm (sGA and Genetic Quantum Algorithm (GQA. The simulation results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.

  6. Spacecraft Attitude Maneuver Planning Using Genetic Algorithms

    Science.gov (United States)

    Kornfeld, Richard P.

    2004-01-01

    A key enabling technology that leads to greater spacecraft autonomy is the capability to autonomously and optimally slew the spacecraft from and to different attitudes while operating under a number of celestial and dynamic constraints. The task of finding an attitude trajectory that meets all the constraints is a formidable one, in particular for orbiting or fly-by spacecraft where the constraints and initial and final conditions are of time-varying nature. This approach for attitude path planning makes full use of a priori constraint knowledge and is computationally tractable enough to be executed onboard a spacecraft. The approach is based on incorporating the constraints into a cost function and using a Genetic Algorithm to iteratively search for and optimize the solution. This results in a directed random search that explores a large part of the solution space while maintaining the knowledge of good solutions from iteration to iteration. A solution obtained this way may be used as is or as an initial solution to initialize additional deterministic optimization algorithms. A number of representative case examples for time-fixed and time-varying conditions yielded search times that are typically on the order of minutes, thus demonstrating the viability of this method. This approach is applicable to all deep space and planet Earth missions requiring greater spacecraft autonomy, and greatly facilitates navigation and science observation planning.

  7. Robust reactor power control system design by genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Yoon Joon; Cho, Kyung Ho; Kim, Sin [Cheju National University, Cheju (Korea, Republic of)

    1997-12-31

    The H{sub {infinity}} robust controller for the reactor power control system is designed by use of the mixed weight sensitivity. The system is configured into the typical two-port model with which the weight functions are augmented. Since the solution depends on the weighting functions and the problem is of nonconvex, the genetic algorithm is used to determine the weighting functions. The cost function applied in the genetic algorithm permits the direct control of the power tracking performances. In addition, the actual operating constraints such as rod velocity and acceleration can be treated as design parameters. Compared with the conventional approach, the controller designed by the genetic algorithm results in the better performances with the realistic constraints. Also, it is found that the genetic algorithm could be used as an effective tool in the robust design. 4 refs., 6 figs. (Author)

  8. Genetic algorithms at UC Davis/LLNL

    Energy Technology Data Exchange (ETDEWEB)

    Vemuri, V.R. [comp.

    1993-12-31

    A tutorial introduction to genetic algorithms is given. This brief tutorial should serve the purpose of introducing the subject to the novice. The tutorial is followed by a brief commentary on the term project reports that follow.

  9. GARD: a genetic algorithm for recombination detection

    National Research Council Canada - National Science Library

    Kosakovsky Pond, Sergei L; Posada, David; Gravenor, Michael B; Woelk, Christopher H; Frost, Simon D W

    2006-01-01

    .... We developed a likelihood-based model selection procedure that uses a genetic algorithm to search multiple sequence alignments for evidence of recombination breakpoints and identify putative recombinant sequences...

  10. In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach.

    Science.gov (United States)

    Abbasitabar, Fatemeh; Zare-Shahabadi, Vahid

    2017-04-01

    Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for Rtraining(2) and Rtest(2), respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for Rtraining(2) and Rtest(2), respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (Rtraining(2) and Rtest(2) were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615.

  11. Genetic Algorithms, Floating Point Numbers and Applications

    Science.gov (United States)

    Hardy, Yorick; Steeb, Willi-Hans; Stoop, Ruedi

    The core in most genetic algorithms is the bitwise manipulations of bit strings. We show that one can directly manipulate the bits in floating point numbers. This means the main bitwise operations in genetic algorithm mutations and crossings are directly done inside the floating point number. Thus the interval under consideration does not need to be known in advance. For applications, we consider the roots of polynomials and finding solutions of linear equations.

  12. Quantum Genetic Algorithms for Computer Scientists

    OpenAIRE

    Rafael Lahoz-Beltra

    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...

  13. Analog Module Placement Design Using Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    This paper presents a novel genetic algorithm for analog module placement based on ageneralization of the two-dimensional bin packing problem. The genetic encoding and operators assure that allproblem constraints are always satisfied. Thus the potential problems of adding penalty terms to the costfunction are eliminated so that the search configuration space is drastically decreased. The dedicated costfunction is based on the special requirements of analog integrated circuits. A fractional factorial experimentwas conducted using an orthogonal array to study the algorithm parameters. A meta GA was applied todetermine the optimal parameter values. The algorithm was tested with several local benchmark circuits. Theexperimental results show that the algorithm has better performance than the simulated annealing approachwith satisfactory results comparable to manual placement. This study demonstrates the effectiveness of thegenetic algorithm in the analog module placement problem. The algorithm has been successfully used in alayout synthesis tool.

  14. Waypoint planning with Dubins Curves using Genetic Algorithms

    DEFF Research Database (Denmark)

    Hansen, Karl Damkjær; La Cour-Harbo, Anders

    2016-01-01

    , the kinematics of the aircraft ruins the plan. This work describes an approach that uses a genetic algorithm to solve the waypoint planning problem while considering the kinematics of the aircraft in one single step. This approach entails the addition of a heading and target speed along with the position...

  15. Time-Delay System Identification Using Genetic Algorithm

    DEFF Research Database (Denmark)

    Yang, Zhenyu; Seested, Glen Thane

    2013-01-01

    problem through an identification approach using the real coded Genetic Algorithm (GA). The desired FOPDT/SOPDT model is directly identified based on the measured system's input and output data. In order to evaluate the quality and performance of this GA-based approach, the proposed method is compared...

  16. A New Fuzzy Adaptive Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FANG Lei; ZHANG Huan-chun; JING Ya-zhi

    2005-01-01

    Multiple genetic algorithms (GAs) need a large population size, which will take a long time for evolution.A new fuzzy adaptive GA is proposed in this paper. This algorithm is more effective in global search while keeping the overall population size constant. The simulation results of function optimization show that with the proposed algorithm, the phenomenon of premature convergence can be overcome effectively, and a satisfying optimization result is obtained.

  17. PDE Nozzle Optimization Using a Genetic Algorithm

    Science.gov (United States)

    Billings, Dana; Turner, James E. (Technical Monitor)

    2000-01-01

    Genetic algorithms, which simulate evolution in natural systems, have been used to find solutions to optimization problems that seem intractable to standard approaches. In this study, the feasibility of using a GA to find an optimum, fixed profile nozzle for a pulse detonation engine (PDE) is demonstrated. The objective was to maximize impulse during the detonation wave passage and blow-down phases of operation. Impulse of each profile variant was obtained by using the CFD code Mozart/2.0 to simulate the transient flow. After 7 generations, the method has identified a nozzle profile that certainly is a candidate for optimum solution. The constraints on the generality of this possible solution remain to be clarified.

  18. Robot path planning using genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Presents a strategy for soccer robot path planning using genetic algorithms for which, real number coding method is used, to overcome the defects of binary coding method, and the double crossover operation a dopted, to avoid the common defect of early convergence and converge faster than the standard genetic algo rithms concludes from simulation results that the method is effective for robot path planning.

  19. A Survey of Association Rule Mining Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Anubha Sharma

    2012-08-01

    Full Text Available Data mining is the analysis step of the "Knowledge Discovery in Databases" process, or KDD. It is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract knowledge from an existing data set and transform it into a human-understandable structure. In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Association rules are usually required to satisfy a user-specified minimum support and a user-specified minimum confidence at the same time. Genetic algorithm (GA is a search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms, which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover. In previous, many researchers have proposed Genetic Algorithms for mining interesting association rules from quantitative data. In this paper we represent a survey of Association Rule Mining Using Genetic Algorithm. The techniques are categorized based upon different approaches. This paper provides the major advancement in the approaches for association rule mining using genetic algorithms.

  20. WWW portal usage analysis using genetic algorithms

    Directory of Open Access Journals (Sweden)

    Ondřej Popelka

    2009-01-01

    Full Text Available The article proposes a new method suitable for advanced analysis of web portal visits. This is part of retrieving information and knowledge from web usage data (web usage mining. Such information is necessary in order to gain better insight into visitor’s needs and generally consumer behaviour. By le­ve­ra­ging this information a company can optimize the organization of its internet presentations and offer a better end-user experience. The proposed approach is using Grammatical evolution which is computational method based on genetic algorithms. Grammatical evolution is using a context-free grammar in order to generate the solution in arbitrary reusable form. This allows us to describe visitors’ behaviour in different manners depending on desired further processing. In this article we use description with a procedural programming language. Web server access log files are used as source data.The extraction of behaviour patterns can currently be solved using statistical analysis – specifically sequential analysis based methods. Our objective is to develop an alternative algorithm.The article further describes the basic algorithms of two-level grammatical evolution; this involves basic Grammatical Evolution and Differential Evolution, which forms the second phase of the computation. Grammatical evolution is used to generate the basic structure of the solution – in form of a part of application code. Differential evolution is used to find optimal parameters for this solution – the specific pages visited by a random visitor. The grammar used to conduct experiments is described along with explanations of the links to the actual implementation of the algorithm. Furthermore the fitness function is described and reasons which yield to its’ current shape. Finally the process of analyzing and filtering the raw input data is described as it is vital part in obtaining reasonable results.

  1. Genetic algorithms as global random search methods

    Science.gov (United States)

    Peck, Charles C.; Dhawan, Atam P.

    1995-01-01

    Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

  2. Solving constrained traveling salesman problems by genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    WU Chunguo; LIANG Yanchun; LEE Heowpueh; LU Chun; LIN Wuzhong

    2004-01-01

    Three kinds of constrained traveling salesman problems (TSP) arising from application problems, namely the open route TSP, the end-fixed TSP, and the path-constrained TSP, are proposed. The corresponding approaches based on modified genetic algorithms (GA) for solving these constrained TSPs are presented. Numerical experiments demonstrate that the algorithm for the open route TSP shows its advantages when the open route is required, the algorithm for the end-fixed TSP can deal with route optimization with constraint of fixed ends effectively, and the algorithm for the path-constraint could benefit the traffic problems where some cities cannot be visited from each other.

  3. Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm.

    Science.gov (United States)

    Wen, Xian-Bin; Zhang, Hua; Jiang, Ze-Tao

    2008-03-12

    A valid unsupervised and multiscale segmentation of synthetic aperture radar(SAR) imagery is proposed by a combination GA-EM of the Expectation Maximization(EM) algorith with the genetic algorithm (GA). The mixture multiscale autoregressive(MMAR) model is introduced to characterize and exploit the scale-to-scale statisticalvariations and statistical variations in the same scale in SAR imagery due to radar speckle,and a segmentation method is given by combining the GA algorithm with the EMalgorithm. This algorithm is capable of selecting the number of components of the modelusing the minimum description length (MDL) criterion. Our approach benefits from theproperties of the Genetic and the EM algorithm by combination of both into a singleprocedure. The population-based stochastic search of the genetic algorithm (GA) exploresthe search space more thoroughly than the EM method. Therefore, our algorithm enablesescaping from local optimal solutions since the algorithm becomes less sensitive to itsinitialization. Some experiment results are given based on our proposed approach, andcompared to that of the EM algorithms. The experiments on the SAR images show that theGA-EM outperforms the EM method.

  4. Multiscale Unsupervised Segmentation of SAR Imagery Using the Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Ze-Tao Jiang

    2008-03-01

    Full Text Available A valid unsupervised and multiscale segmentation of synthetic aperture radar(SAR imagery is proposed by a combination GA-EM of the Expectation Maximization(EM algorith with the genetic algorithm (GA. The mixture multiscale autoregressive(MMAR model is introduced to characterize and exploit the scale-to-scale statisticalvariations and statistical variations in the same scale in SAR imagery due to radar speckle,and a segmentation method is given by combining the GA algorithm with the EMalgorithm. This algorithm is capable of selecting the number of components of the modelusing the minimum description length (MDL criterion. Our approach benefits from theproperties of the Genetic and the EM algorithm by combination of both into a singleprocedure. The population-based stochastic search of the genetic algorithm (GA exploresthe search space more thoroughly than the EM method. Therefore, our algorithm enablesescaping from local optimal solutions since the algorithm becomes less sensitive to itsinitialization. Some experiment results are given based on our proposed approach, andcompared to that of the EM algorithms. The experiments on the SAR images show that theGA-EM outperforms the EM method.

  5. Adaptive interactive genetic algorithms with individual interval fitness

    Institute of Scientific and Technical Information of China (English)

    Dunwei Gong; Guangsong Guo; Li Lu; Hongmei Ma

    2008-01-01

    It is necessary to enhance the performance of interactive genetic algorithms in order to apply them to complicated optimization problems successfully. An adaptive interactive genetic algorithm with individual interval fitness is proposed in this paper in which an individual fitness is expressed by an interval. Through analyzing the fitness, information reflecting the distribution of an evolutionary population is picked up, namely, the difference of evaluating superior individuals and the difference of evaluating a population. Based on these, the adaptive probabilities of crossover and mutation operators of an individual are presented. The algorithm proposed in this paper is applied to a fashion evolutionary design system, and the results show that it can find many satisfactory solutions per generation. The achievement of the paper provides a new approach to enhance the performance of interactive genetic algorithms.

  6. A Multi-Objective Genetic Algorithm for Optimal Portfolio Problems

    Institute of Scientific and Technical Information of China (English)

    林丹; 赵瑞

    2004-01-01

    This paper concerns with modeling and design of an algorithm for the portfolio selection problems with fixed transaction costs and minimum transaction lots. A mean-variance model for the portfolio selection problem is proposed, and the model is formulated as a non-smooth and nonlinear integer programming problem with multiple objective functions. As it has been proven that finding a feasible solution to the problem only is already NP-hard, based on NSGA-II and genetic algorithm for numerical optimization of constrained problems (Genocop), a multi-objective genetic algorithm (MOGA) is designed to solve the model. Its features comprise integer encoding and corresponding operators, and special treatment of constraints conditions. It is illustrated via a numerical example that the genetic algorithm can efficiently solve portfolio selection models proposed in this paper. This approach offers promise for the portfolio problems in practice.

  7. A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments

    Institute of Scientific and Technical Information of China (English)

    Shengxiang Yang; Renato Tinós

    2007-01-01

    Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.

  8. Protein fold classification with genetic algorithms and feature selection.

    Science.gov (United States)

    Chen, Peng; Liu, Chunmei; Burge, Legand; Mahmood, Mohammad; Southerland, William; Gloster, Clay

    2009-10-01

    Protein fold classification is a key step to predicting protein tertiary structures. This paper proposes a novel approach based on genetic algorithms and feature selection to classifying protein folds. Our dataset is divided into a training dataset and a test dataset. Each individual for the genetic algorithms represents a selection function of the feature vectors of the training dataset. A support vector machine is applied to each individual to evaluate the fitness value (fold classification rate) of each individual. The aim of the genetic algorithms is to search for the best individual that produces the highest fold classification rate. The best individual is then applied to the feature vectors of the test dataset and a support vector machine is built to classify protein folds based on selected features. Our experimental results on Ding and Dubchak's benchmark dataset of 27-class folds show that our approach achieves an accuracy of 71.28%, which outperforms current state-of-the-art protein fold predictors.

  9. Chemometrics: From classical to genetic algorithms

    Directory of Open Access Journals (Sweden)

    Leardi, Riccardo

    2002-03-01

    Full Text Available In this paper the fundamentals of Chemometrics are presented, by means of a quick overview of the most relevant techniques for data display, classification, modeling and calibration. Two emerging techniques such as Genetic Algorithms and Artificial Neural Networks will also be presented. Goal of the paper is to make people aware of the great superiority of multivariate analysis over the commonly used univariate approach. Mathematical and algorithmical details are not presented, since the paper is mainly focused on the general problems to which Chemometrics can be successfully applied in the field of Food Chemistry.En este artículo se muestran los aspectos fundamentales de la Quimiometria por medio de una revisión rápida de las técnicas más relevantes para mostrar los datos, modelar y calibrar. Se describen dos técnicas emergentes como los algoritmos genéticos y las redes neuronales. El objetivo del articulo es que la comunidad científica tome conciencia de la gran superioridad del análisis multivariante sobre el análisis univariante. No se describen los detalles matemáticos y algorítmicos porque el articulo está dirigido a problemas genéricos en los que la Quimiometría puede ser aplicada con éxito dentro del campo de la Química Analítica.

  10. Interactive Genetic Algorithms with Fitness Adjustment

    Institute of Scientific and Technical Information of China (English)

    GUO Guang-song; GONG Dun-wei; HAO Guo-sheng; ZHANG Yong

    2006-01-01

    Noises widely exist in interactive genetic algorithms. However, there is no effective method to solve this problem up to now. There are two kinds of noises, one is the noise existing in visual systems and the other is resulted from user's preference mechanisms. Characteristics of the two noises are presented aiming at the application of interactive genetic algorithms in dealing with images. The evolutionary phases of interactive genetic algorithms are determined according to differences in the same individual's fitness among different generations. Models for noises in different phases are established and the corresponding strategies for reducing noises are given. The algorithm proposed in this paper has been applied to fashion design, which is a typical example of image processing. The results show that the strategies can reduce noises in interactive genetic algorithms and improve the algorithm's performance effectively. However, a further study is needed to solve the problem of determining the evolution phase by using suitable objective methods so as to find out an effective method to decrease noises.

  11. Big Data Clustering Using Genetic Algorithm On Hadoop Mapreduce

    Directory of Open Access Journals (Sweden)

    Nivranshu Hans

    2015-04-01

    Full Text Available Abstract Cluster analysis is used to classify similar objects under same group. It is one of the most important data mining methods. However it fails to perform well for big data due to huge time complexity. For such scenarios parallelization is a better approach. Mapreduce is a popular programming model which enables parallel processing in a distributed environment. But most of the clustering algorithms are not naturally parallelizable for instance Genetic Algorithms. This is so due to the sequential nature of Genetic Algorithms. This paper introduces a technique to parallelize GA based clustering by extending hadoop mapreduce. An analysis of proposed approach to evaluate performance gains with respect to a sequential algorithm is presented. The analysis is based on a real life large data set.

  12. Web Based Genetic Algorithm Using Data Mining

    Directory of Open Access Journals (Sweden)

    Ashiqur Rahman

    2016-09-01

    Full Text Available This paper presents an approach for classifying students in order to predict their final grade based on features extracted from logged data in an education web-based system. A combination of multiple classifiers leads to a significant improvement in classification performance. Through weighting the feature vectors using a Genetic Algorithm we can optimize the prediction accuracy and get a marked improvement over raw classification. It further shows that when the number of features is few; feature weighting is works better than just feature selection. Many leading educational institutions are working to establish an online teaching and learning presence. Several systems with different capabilities and approaches have been developed to deliver online education in an academic setting. In particular, Michigan State University (MSU has pioneered some of these systems to provide an infrastructure for online instruction. The research presented here was performed on a part of the latest online educational system developed at MSU, the Learning Online Network with Computer-Assisted Personalized Approach (LON-CAPA

  13. Genetic warfarin dosing: tables versus algorithms.

    Science.gov (United States)

    Finkelman, Brian S; Gage, Brian F; Johnson, Julie A; Brensinger, Colleen M; Kimmel, Stephen E

    2011-02-01

    The aim of this study was to compare the accuracy of genetic tables and formal pharmacogenetic algorithms for warfarin dosing. Pharmacogenetic algorithms based on regression equations can predict warfarin dose, but they require detailed mathematical calculations. A simpler alternative, recently added to the warfarin label by the U.S. Food and Drug Administration, is to use genotype-stratified tables to estimate warfarin dose. This table may potentially increase the use of pharmacogenetic warfarin dosing in clinical practice; however, its accuracy has not been quantified. A retrospective cohort study of 1,378 patients from 3 anticoagulation centers was conducted. Inclusion criteria were stable therapeutic warfarin dose and complete genetic and clinical data. Five dose prediction methods were compared: 2 methods using only clinical information (empiric 5 mg/day dosing and a formal clinical algorithm), 2 genetic tables (the new warfarin label table and a table based on mean dose stratified by genotype), and 1 formal pharmacogenetic algorithm, using both clinical and genetic information. For each method, the proportion of patients whose predicted doses were within 20% of their actual therapeutic doses was determined. Dosing methods were compared using McNemar's chi-square test. Warfarin dose prediction was significantly more accurate (all p algorithm (52%) than with all other methods: empiric dosing (37%; odds ratio [OR]: 2.2), clinical algorithm (39%; OR: 2.2), warfarin label (43%; OR: 1.8), and genotype mean dose table (44%; OR: 1.9). Although genetic tables predicted warfarin dose better than empiric dosing, formal pharmacogenetic algorithms were the most accurate. Copyright © 2011 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

  14. Genetic Algorithms for Digital Quantum Simulations.

    Science.gov (United States)

    Las Heras, U; Alvarez-Rodriguez, U; Solano, E; Sanz, M

    2016-06-10

    We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors.

  15. Applying a Genetic Algorithm to Reconfigurable Hardware

    Science.gov (United States)

    Wells, B. Earl; Weir, John; Trevino, Luis; Patrick, Clint; Steincamp, Jim

    2004-01-01

    This paper investigates the feasibility of applying genetic algorithms to solve optimization problems that are implemented entirely in reconfgurable hardware. The paper highlights the pe$ormance/design space trade-offs that must be understood to effectively implement a standard genetic algorithm within a modem Field Programmable Gate Array, FPGA, reconfgurable hardware environment and presents a case-study where this stochastic search technique is applied to standard test-case problems taken from the technical literature. In this research, the targeted FPGA-based platform and high-level design environment was the Starbridge Hypercomputing platform, which incorporates multiple Xilinx Virtex II FPGAs, and the Viva TM graphical hardware description language.

  16. Genetic Algorithms for Digital Quantum Simulations

    Science.gov (United States)

    Las Heras, U.; Alvarez-Rodriguez, U.; Solano, E.; Sanz, M.

    2016-06-01

    We propose genetic algorithms, which are robust optimization techniques inspired by natural selection, to enhance the versatility of digital quantum simulations. In this sense, we show that genetic algorithms can be employed to increase the fidelity and optimize the resource requirements of digital quantum simulation protocols while adapting naturally to the experimental constraints. Furthermore, this method allows us to reduce not only digital errors but also experimental errors in quantum gates. Indeed, by adding ancillary qubits, we design a modular gate made out of imperfect gates, whose fidelity is larger than the fidelity of any of the constituent gates. Finally, we prove that the proposed modular gates are resilient against different gate errors.

  17. Genetic algorithm for neural networks optimization

    Science.gov (United States)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  18. The Applications of Genetic Algorithms in Medicine

    Directory of Open Access Journals (Sweden)

    Ali Ghaheri

    2015-11-01

    Full Text Available A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.

  19. The Applications of Genetic Algorithms in Medicine

    Science.gov (United States)

    Ghaheri, Ali; Shoar, Saeed; Naderan, Mohammad; Hoseini, Sayed Shahabuddin

    2015-01-01

    A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algorithms are not known by physicians who may well benefit by applying them to solve complex medical problems. Therefore, in this paper, we introduce the genetic algorithm and its applications in medicine. The use of the genetic algorithm has promising implications in various medical specialties including radiology, radiotherapy, oncology, pediatrics, cardiology, endocrinology, surgery, obstetrics and gynecology, pulmonology, infectious diseases, orthopedics, rehabilitation medicine, neurology, pharmacotherapy, and health care management. This review introduces the applications of the genetic algorithm in disease screening, diagnosis, treatment planning, pharmacovigilance, prognosis, and health care management, and enables physicians to envision possible applications of this metaheuristic method in their medical career.] PMID:26676060

  20. A cost benefit operator for efficient multi level genetic algorithm searches

    OpenAIRE

    Mitchell, George G.; McMullin, Barry; Decraene, James

    2007-01-01

    In this paper we present a novel cost benefit operator that assists multi level genetic algorithm searches. Through the use of the cost benefit operator, it is possible to dynamically constrain the search of the base level genetic algorithm, to suit the user’s requirements. Initially we review meta-evolutionary (multi-level genetic algorithm) approaches. We note that the current literature has abundant studies on meta-evolutionary GAs. However these approaches have not identified an effici...

  1. Concrete Plant Operations Optimization Using Combined Simulation and Genetic Algorithms

    NARCIS (Netherlands)

    Cao, Ming; Lu, Ming; Zhang, Jian-Ping

    2004-01-01

    This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation m

  2. Concrete Plant Operations Optimization Using Combined Simulation and Genetic Algorithms

    NARCIS (Netherlands)

    Cao, Ming; Lu, Ming; Zhang, Jian-Ping

    2004-01-01

    This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation m

  3. Applying Genetic Algorithms To Query Optimization in Document Retrieval.

    Science.gov (United States)

    Horng, Jorng-Tzong; Yeh, Ching-Chang

    2000-01-01

    Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)

  4. Concrete Plant Operations Optimization Using Combined Simulation and Genetic Algorithms

    NARCIS (Netherlands)

    Cao, Ming; Lu, Ming; Zhang, Jian-Ping

    2004-01-01

    This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation

  5. Applying Genetic Algorithms To Query Optimization in Document Retrieval.

    Science.gov (United States)

    Horng, Jorng-Tzong; Yeh, Ching-Chang

    2000-01-01

    Proposes a novel approach to automatically retrieve keywords and then uses genetic algorithms to adapt the keyword weights. Discusses Chinese text retrieval, term frequency rating formulas, vector space models, bigrams, the PAT-tree structure for information retrieval, query vectors, and relevance feedback. (Author/LRW)

  6. Cognitive radio resource allocation based on coupled chaotic genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Zu Yun-Xiao; Zhou Jie; Zeng Chang-Chang

    2010-01-01

    A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

  7. Cognitive radio resource allocation based on coupled chaotic genetic algorithm

    Science.gov (United States)

    Zu, Yun-Xiao; Zhou, Jie; Zeng, Chang-Chang

    2010-11-01

    A coupled chaotic genetic algorithm for cognitive radio resource allocation which is based on genetic algorithm and coupled Logistic map is proposed. A fitness function for cognitive radio resource allocation is provided. Simulations are conducted for cognitive radio resource allocation by using the coupled chaotic genetic algorithm, simple genetic algorithm and dynamic allocation algorithm respectively. The simulation results show that, compared with simple genetic and dynamic allocation algorithm, coupled chaotic genetic algorithm reduces the total transmission power and bit error rate in cognitive radio system, and has faster convergence speed.

  8. Ethanol mediated As(III) adsorption onto Zn-loaded pinecone biochar: Experimental investigation, modeling, and optimization using hybrid artificial neural network-genetic algorithm approach.

    Science.gov (United States)

    Zafar, Mohd; Van Vinh, N; Behera, Shishir Kumar; Park, Hung-Suck

    2017-04-01

    Organic matters (OMs) and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol (EtOH)-mediated As(III) adsorption onto Zn-loaded pinecone (PC) biochar through batch experiments conducted under Box-Behnken design. The effect of EtOH on As(III) adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtOH and pH on As(III) adsorption, whereas neural network revealed the stronger influence of EtOH (64.5%) followed by pH (20.75%) and As(III) concentration (14.75%) on the adsorption phenomena. Besides, the interaction among process variables indicated that EtOH enhances As(III) adsorption over a pH range of 2 to 7, possibly due to facilitation of ligand-metal(Zn) binding complexation mechanism. Eventually, hybrid response surface model-genetic algorithm (RSM-GA) approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As(III) (10.47μg/g) is facilitated at 30.22mg C/L of EtOH with initial As(III) concentration of 196.77μg/L at pH5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As(III) species in the presence of OM. Copyright © 2016. Published by Elsevier B.V.

  9. Multiprocessor Scheduling Using Parallel Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Nourah Al-Angari

    2012-07-01

    Full Text Available Tasks scheduling is the most challenging problem in the parallel computing. Hence, the inappropriate scheduling will reduce or even abort the utilization of the true potential of the parallelization. Genetic algorithm (GA has been successfully applied to solve the scheduling problem. The fitness evaluation is the most time consuming GA operation for the CPU time, which affect the GA performance. The proposed synchronous master-slave algorithm outperforms the sequential algorithm in case of complex and high number of generations problem.

  10. Nested Genetic Algorithm for Resolving Overlapped Spectral Bands

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A nested genetic algorithm, including genetic parameter level and genetic implemented level for peak parameters, was proposed and applied for resolving overlapped spectral bands. By the genetic parameter level, parameters of genetic algorithm were optimized; moreover, the number of overlapped peaks was determined simultaneously. Then parameters of individual peaks were computed with the genetic implemented level.

  11. Scope of Gradient and Genetic Algorithms in Multivariable Function Optimization

    Science.gov (United States)

    Shaykhian, Gholam Ali; Sen, S. K.

    2007-01-01

    Global optimization of a multivariable function - constrained by bounds specified on each variable and also unconstrained - is an important problem with several real world applications. Deterministic methods such as the gradient algorithms as well as the randomized methods such as the genetic algorithms may be employed to solve these problems. In fact, there are optimization problems where a genetic algorithm/an evolutionary approach is preferable at least from the quality (accuracy) of the results point of view. From cost (complexity) point of view, both gradient and genetic approaches are usually polynomial-time; there are no serious differences in this regard, i.e., the computational complexity point of view. However, for certain types of problems, such as those with unacceptably erroneous numerical partial derivatives and those with physically amplified analytical partial derivatives whose numerical evaluation involves undesirable errors and/or is messy, a genetic (stochastic) approach should be a better choice. We have presented here the pros and cons of both the approaches so that the concerned reader/user can decide which approach is most suited for the problem at hand. Also for the function which is known in a tabular form, instead of an analytical form, as is often the case in an experimental environment, we attempt to provide an insight into the approaches focusing our attention toward accuracy. Such an insight will help one to decide which method, out of several available methods, should be employed to obtain the best (least error) output. *

  12. Naturally selecting solutions: the use of genetic algorithms in bioinformatics.

    Science.gov (United States)

    Manning, Timmy; Sleator, Roy D; Walsh, Paul

    2013-01-01

    For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

  13. Particle swarm optimization - Genetic algorithm (PSOGA) on linear transportation problem

    Science.gov (United States)

    Rahmalia, Dinita

    2017-08-01

    Linear Transportation Problem (LTP) is the case of constrained optimization where we want to minimize cost subject to the balance of the number of supply and the number of demand. The exact method such as northwest corner, vogel, russel, minimal cost have been applied at approaching optimal solution. In this paper, we use heurisitic like Particle Swarm Optimization (PSO) for solving linear transportation problem at any size of decision variable. In addition, we combine mutation operator of Genetic Algorithm (GA) at PSO to improve optimal solution. This method is called Particle Swarm Optimization - Genetic Algorithm (PSOGA). The simulations show that PSOGA can improve optimal solution resulted by PSO.

  14. Improved Genetic Algorithm Application in Textile Defect Detection

    Institute of Scientific and Technical Information of China (English)

    GENG Zhao-feng; Li Bei-bei; ZHAO Zhi-hong

    2007-01-01

    Based on an efficient improved genetic algorithm,a pattern recognition approach is represented for textile defects inspection. An image process is developed to automatically detect the drawbacks on textile caused by three circumstances: break, dual, and jump of yams. By statistic method, some texture feature values of the image with defects points can be achieved. Therefore, the textile defects are classified properly. The advanced process of the defect image is done. Image segmentation is realized by an improved genetic algorithm to detect the defects. This method can be used to automatically classify and detect textile defects. According to different users' requirements, ifferent types of textile material can be detected.

  15. Genetic Algorithm Optimisation of a Ship Navigation System

    Directory of Open Access Journals (Sweden)

    E. Alfaro-Cid

    2001-01-01

    Full Text Available The optimisation of the PID controllers' gains for separate propulsion and heading control systems of CyberShip I, a scale model of an oil platform supply ship, using Genetic Algorithms is considered. During the initial design process both PID controllers have been manually tuned to improve their performance. However this tuning approach is a tedious and time consuming process. A solution to this problem is the use of optimisation techniques based on Genetic Algorithms to optimise the controllers' gain values. This investigation has been carried out through computer-generated simulations based on a non-linear hydrodynamic model of CyberShip I.

  16. Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn.

    Science.gov (United States)

    Patra, Tarak K; Meenakshisundaram, Venkatesh; Hung, Jui-Hsiang; Simmons, David S

    2017-02-13

    Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.

  17. Solving the Dial-a-Ride Problem using Genetic Algorithms

    DEFF Research Database (Denmark)

    Jørgensen, Rene Munk; Larsen, Jesper; Bergvinsdottir, Kristin Berg

    2007-01-01

    customer service level constraints (Quality of Service). In this paper, we present a genetic algorithm (GA) for solving the DARP. The algorithm is based on the classical cluster-first, route-second approach, where it alternates between assigning customers to vehicles using a GA and solving independent...... routing problems for the vehicles using a routing heuristic. The algorithm is implemented in Java and tested on publicly available data sets. The new solution method has achieved solutions comparable with the current state-of-the-art methods....

  18. MULTIOBJECTIVE PARALLEL GENETIC ALGORITHM FOR WASTE MINIMIZATION

    Science.gov (United States)

    In this research we have developed an efficient multiobjective parallel genetic algorithm (MOPGA) for waste minimization problems. This MOPGA integrates PGAPack (Levine, 1996) and NSGA-II (Deb, 2000) with novel modifications. PGAPack is a master-slave parallel implementation of a...

  19. Predicting complex mineral structures using genetic algorithms.

    Science.gov (United States)

    Mohn, Chris E; Kob, Walter

    2015-10-28

    We show that symmetry-adapted genetic algorithms are capable of finding the ground state of a range of complex crystalline phases including layered- and incommensurate super-structures. This opens the way for the atomistic prediction of complex crystal structures of functional materials and mineral phases.

  20. Proposed genetic algorithms for construction site layout

    NARCIS (Netherlands)

    Mawdesley, Michael J.; Al-Jibouri, Saad H.

    2003-01-01

    The positioning of temporary facilities on a construction site is an area of research which has been recognised as important but which has received relatively little attention. In this paper, a genetic algorithm is proposed to solve the problem in which m facilities are to be positioned to n availab

  1. Boosting Principal Component Analysis by Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Divya Somvanshi

    2010-07-01

    Full Text Available This paper presents a new method of feature extraction by combining principal component analysis and genetic algorithm. Use of multiple pre-processors in combination with principal component analysis generates alternate feature spaces for data representation. The present method works out the fusion of these multiple spaces to create higher dimensionality feature vectors. The fused feature vectors are given chromosome representation by taking feature components to be genes. Then these feature vectors are allowed to undergo genetic evolution individually. For genetic algorithm, initial population is created by calculating probability distance matrix, and by applying a probability distance metric such that all the genes which lie farther than a defined threshold are tripped to zero. The genetic evolution of fused feature vector brings out most significant feature components (genes as survivours. A measure of significance is adapted on the basis of frequency of occurrence of the surviving genes in the current population. Finally, the feature vector is obtained by weighting the original feature components in proportion to their significance. The present algorithm is validated in combination with a neural network classifier based on error backpropagation algorithm, and by analysing a number of benchmark datasets available in the open sources.Defence Science Journal, 2010, 60(4, pp.392-398, DOI:http://dx.doi.org/10.14429/dsj.60.495

  2. Impatient Task Mapping in Elastic Cloud using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Nawfal A. Mehdi

    2011-01-01

    Full Text Available Problem statement: Task scheduling is the main factor that determines the performance of any distributed system. Cloud computing comes with a paradigm of distributed datacenters. Each datacenter consists of physical machines that host virtual machines to execute customers’ tasks. Resources allocation on the cloud is different from other paradigms and the mapping algorithms need to be adapted to the new characteristics. This study takes the problem of immediate task scheduling under an intercloud infrastructure using a genetic algorithm. An impatient task needs to be scheduled as soon as it enters the system taking into account the input and output files location and its QoS requirements. Approach: This study proposes an algorithm that can find a fast mapping using genetic algorithms with "exist if satisfy" condition to speed up the mapping process and ensures the respecting of all task deadlines. Cloudsim simulator was used to test the proposed algorithm with real datasets collected as a cloud benchmark. Mapping time and makespan are the performance metrics that are used to evaluate the proposed system. Results: The results show an improvement in the proposed system compared to MCT algorithm as illustrated throughout the study. Conclusion: Batch mapping via genetic algorithms with throughput as a fitness function can be used to map jobs to cloud resources.

  3. Designing of a Decision Support System (DSS for resource allocation with genetic algorithm approach (Case Study: Central Library of Tarbiat Modares University

    Directory of Open Access Journals (Sweden)

    Alireza Hasanzadeh

    2014-09-01

    Full Text Available The allocation of information is one of the main responsibilities of a manager. In case there is a limitation in the available resources, the issue of resources allocation is raised. Universities and post graduate centers have faced limitations in accessing resources such as budget, human resources, physical space, etc. This problem results in inappropriate use of the approved budget in buying and sharing the different types of information resources, lack of easy access to information resources by users, and users’ dissatisfaction. This paper is intended to see whether the model of genetic algorithm can be used in helping library heads of university to develop a support system for the proper allocation of resources. The data of the central library of a university as the main core of DSS through using genetic algorithm in MATLAB was used in order to come up with a better distribution of effective resources. Research methodology used in this paper was field study and survey. The findings indicated that genetic algorithm was successful in achieving this end.

  4. Evolving Quantum Circuits using Genetic Algorithms

    CERN Document Server

    Prashant

    2005-01-01

    This paper describes an application of genetic algorithm for evolving quantum computing circuits. The circuits use reversible one qubit and two qubit gates which are applied on a multi-qubit system having some initial state. The genetic algorithm automatically searches the space and comes out with the appropriate circuit design, which yields desired output state. The fitness function used matches the output with desired output and the search stops when it is found. The fitness value becomes higher if the output is close to the desired output. The paper briefly discusses the operation of a quantum gate over the multi-qubit system. The paper also demonstrates some examples of the evolved circuits using the algorithm.

  5. Genetic algorithm optimization for finned channel performance

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Compared to a smooth channel, a finned channel provides a higher heat transfer coefficient; increasing the fin height enhances the heat transfer. However, this heat transfer enhancement is associated with an increase in the pressure drop. This leads to an increased pumping power requirement so that one may seek an optimum design for such systems. The main goal of this paper is to define the exact location and size of fins in such a way that a minimal pressure drop coincides with an optimal heat transfer based on the genetic algorithm. Each fin arrangement is considered a solution to the problem(an individual for genetic algorithm). An initial population is generated randomly at the first step. Then the algorithm has been searched among these solutions and made new solutions iteratively by its functions to find an optimum design as reported in this article.

  6. Genetic algorithm-based evaluation of spatial straightness error

    Institute of Scientific and Technical Information of China (English)

    崔长彩; 车仁生; 黄庆成; 叶东; 陈刚

    2003-01-01

    A genetic algorithm ( GA ) -based approach is proposed to evaluate the straightness error of spatial lines. According to the mathematical definition of spatial straightness, a verification model is established for straightness error, and the fitness function of GA is then given and the implementation techniques of the proposed algorithm is discussed in detail. The implementation techniques include real number encoding, adaptive variable range choosing, roulette wheel and elitist combination selection strategies, heuristic crossover and single point mutation schemes etc. An application example is quoted to validate the proposed algorithm. The computation result shows that the GA-based approach is a superior nonlinear parallel optimization method. The performance of the evolution population can be improved through genetic operations such as reproduction, crossover and mutation until the optimum goal of the minimum zone solution is obtained. The quality of the solution is better and the efficiency of computation is higher than other methods.

  7. Quantum Genetic Algorithms for Computer Scientists

    Directory of Open Access Journals (Sweden)

    Rafael Lahoz-Beltra

    2016-10-01

    Full Text Available 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 Genetic Algorithms” (QGAs. In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs “avoiding” the possible difficulties of quantum-mechanical phenomena.

  8. Surface/Surface Intersection Using Simulated Annealing Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The genetic algorithm and marching method are integrated into a novel algorithm to solve the surface intersection problem. By combining genetic algorithm with local searching method the efficiency of evolution is greatly improved. By fully utilizing the global searching ability and instinct attribute for parallel computation of genetic algorithm and the local rapid convergency of marching method, the algorithm can compute the intersection robustly and generate correct topology of intersection curves. The details of the new algorithm are discussed here.

  9. OPC recipe optimization using genetic algorithm

    Science.gov (United States)

    Asthana, Abhishek; Wilkinson, Bill; Power, Dave

    2016-03-01

    Optimization of OPC recipes is not trivial due to multiple parameters that need tuning and their correlation. Usually, no standard methodologies exist for choosing the initial recipe settings, and in the keyword development phase, parameters are chosen either based on previous learning, vendor recommendations, or to resolve specific problems on particular special constructs. Such approaches fail to holistically quantify the effects of parameters on other or possible new designs, and to an extent are based on the keyword developer's intuition. In addition, when a quick fix is needed for a new design, numerous customization statements are added to the recipe, which make it more complex. The present work demonstrates the application of Genetic Algorithm (GA) technique for optimizing OPC recipes. GA is a search technique that mimics Darwinian natural selection and has applications in various science and engineering disciplines. In this case, GA search heuristic is applied to two problems: (a) an overall OPC recipe optimization with respect to selected parameters and, (b) application of GA to improve printing and via coverage at line end geometries. As will be demonstrated, the optimized recipe significantly reduced the number of ORC violations for case (a). For case (b) line end for various features showed significant printing and filling improvement.

  10. Using Genetic Algorithms on Manufacturing Facilities Layout Problems

    Institute of Scientific and Technical Information of China (English)

    王克胜; EspenGunnarsen; 袁庆丰

    2004-01-01

    Traditionally, the objective of a manufacturing facility layout problem is to minimize the material handling cost of the manufacturing systems. Because of the combination of the facility layout problems, the genetic algorithms (GA) technique is the most promising approach for solving practical layout problems. Much of the previous work has been done for identical problems, where all departments are equal in area. In this paper, non-identical problems are dealt with. A new coding approach - the Comer Attachmer Structure (CAS) is introduced.

  11. Genetic algorithms for the vehicle routing problem

    Science.gov (United States)

    Volna, Eva

    2016-06-01

    The Vehicle Routing Problem (VRP) is one of the most challenging combinatorial optimization tasks. This problem consists in designing the optimal set of routes for fleet of vehicles in order to serve a given set of customers. Evolutionary algorithms are general iterative algorithms for combinatorial optimization. These algorithms have been found to be very effective and robust in solving numerous problems from a wide range of application domains. This problem is known to be NP-hard; hence many heuristic procedures for its solution have been suggested. For such problems it is often desirable to obtain approximate solutions, so they can be found fast enough and are sufficiently accurate for the purpose. In this paper we have performed an experimental study that indicates the suitable use of genetic algorithms for the vehicle routing problem.

  12. A Novel Training Algorithm of Genetic Neural Networks and Its Application to Classification

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorithm for neural networks based on genetic algorithm and BP algorithm is developed. The difference between the new training algorithm and BP algorithm in the ability of nonlinear approaching is expressed through an example, and the application foreground is illustrated by an example.

  13. Multi Population Hybrid Genetic Algorithms for University Course Timetabling Problem

    Directory of Open Access Journals (Sweden)

    Leila Jadidi

    2012-06-01

    Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with it at the beginning of each. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or near optimal feasible solutions instead of exact solutions. Genetic algorithms, because of multidirectional search property of them, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP are proposed: FGARI, FGASA and FGATS. In proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainly and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP.

  14. Multi Population Hybrid Genetic Algorithms for University Course Timetabling

    Directory of Open Access Journals (Sweden)

    Mehrnaz Shirani LIRI

    2012-08-01

    Full Text Available University course timetabling is one of the important and time consuming issues that each University is involved with at the beginning of each university year. This problem is in class of NP-hard problem and is very difficult to solve by classic algorithms. Therefore optimization techniques are used to solve them and produce optimal or almost optimal feasible solutions instead of exact solutions. Genetic algorithms, because of their multidirectional search property, are considered as an efficient approach for solving this type of problems. In this paper three new hybrid genetic algorithms for solving the university course timetabling problem (UCTP are proposed: FGARI, FGASA and FGATS. In the proposed algorithms, fuzzy logic is used to measure violation of soft constraints in fitness function to deal with inherent uncertainty and vagueness involved in real life data. Also, randomized iterative local search, simulated annealing and tabu search are applied, respectively, to improve exploitive search ability and prevent genetic algorithm to be trapped in local optimum. The experimental results indicate that the proposed algorithms are able to produce promising results for the UCTP

  15. Niche Genetic Algorithm with Accurate Optimization Performance

    Institute of Scientific and Technical Information of China (English)

    LIU Jian-hua; YAN De-kun

    2005-01-01

    Based on crowding mechanism, a novel niche genetic algorithm was proposed which can record evolutionary direction dynamically during evolution. After evolution, the solutions's precision can be greatly improved by means of the local searching along the recorded direction. Simulation shows that this algorithm can not only keep population diversity but also find accurate solutions. Although using this method has to take more time compared with the standard GA, it is really worth applying to some cases that have to meet a demand for high solution precision.

  16. Algorithmic approach to quantum physics

    CERN Document Server

    Ozhigov, Y

    2004-01-01

    Algorithmic approach is based on the assumption that any quantum evolution of many particle system can be simulated on a classical computer with the polynomial time and memory cost. Algorithms play the central role here but not the analysis, and a simulation gives a "film" which visualizes many particle quantum dynamics and is demonstrated to a user of the model. Restrictions following from the algorithm theory are considered on a level of fundamental physical laws. Born rule for the calculation of quantum probability as well as the decoherence is derived from the existence of a nonzero minimal value of amplitude module - a grain of amplitude. The limitation on the classical computational resources gives the unified description of quantum dynamics that is not divided to the unitary dynamics and measurements and does not depend on the existence of observer. It is proposed the description of states based on the nesting of particles in each other that permits to account the effects of all levels in the same mode...

  17. Genetic Algorithm Design of a 3D Printed Heat Sink

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Tong [ORNL; Ozpineci, Burak [ORNL; Ayers, Curtis William [ORNL

    2016-01-01

    In this paper, a genetic algorithm- (GA-) based approach is discussed for designing heat sinks based on total heat generation and dissipation for a pre-specified size andshape. This approach combines random iteration processesand genetic algorithms with finite element analysis (FEA) to design the optimized heat sink. With an approach that prefers survival of the fittest , a more powerful heat sink can bedesigned which can cool power electronics more efficiently. Some of the resulting designs can only be 3D printed due totheir complexity. In addition to describing the methodology, this paper also includes comparisons of different cases to evaluate the performance of the newly designed heat sinkcompared to commercially available heat sinks.

  18. Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization

    OpenAIRE

    Boumediene ALLAOUA; Laoufi, Abdellah; Brahim GASBAOUI; Nasri, Abdelfatah; Abdessalam ABDERRAHMANI

    2008-01-01

    In this paper, an intelligent controller of the DC (Direct current) Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became ve...

  19. TIP: protein backtranslation aided by genetic algorithms.

    Science.gov (United States)

    Moreira, Andrés; Maass, Alejandro

    2004-09-01

    Several applications require the backtranslation of a protein sequence into a nucleic acid sequence. The degeneracy of the genetic code makes this process ambiguous; moreover, not every translation is equally viable. The usual answer is to mimic the codon usage of the target species; however, this does not capture all the relevant features of the 'genomic styles' from different taxa. The program TIP ' Traducción Inversa de Proteínas') applies genetic algorithms to improve the backtranslation, by minimizing the difference of some coding statistics with respect to their average value in the target. http://www.cmm.uchile.cl/genoma/tip/

  20. Parameter Optimization of Linear Quadratic Controller Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Jimin; SHANG Chaoxuan; ZOU Minghu

    2007-01-01

    The selection of weighting matrix in design of the linear quadratic optimal controller is an important topic in the control theory. In this paper, an approach based on genetic algorithm is presented for selecting the weighting matrix for the optimal controller. Genetic algorithm is adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this algorithm, the fitness function is used to evaluate individuals and reproductive success varies with fitness. In the design of the linear quadratic optimal controller, the fitness function has relation to the anticipated step response of the system. Not only can the controller designed by this approach meet the demand of the performance indexes of linear quadratic controller, but also satisfy the anticipated step response of close-loop system. The method possesses a higher calculating efficiency and provides technical support for the optimal controller in engineering application. The simulation of a three-order single-input single-output (SISO) system has demonstrated the feasibility and validity of the approach.

  1. Fuzzy Inspired Hybrid Genetic Approach to Optimize Travelling Salesman Problem

    Directory of Open Access Journals (Sweden)

    Bindu

    2012-06-01

    Full Text Available One of the category of algorithm Problems are basically exponential problems. These problems are basically exponential problems and take time to find the solution. In the present work we are optimising one of the common NP complete problem called Travelling Salesman Problem. In our work we have defined a genetic approach by combining fuzzy approach along with genetics. In this work we have implemented the modified DPX crossover to improve genetic approach. The work is implemented in MATLAB environment and obtained results shows the define approach has optimized the existing genetic algorithm results

  2. Predicting mining activity with parallel genetic algorithms

    Science.gov (United States)

    Talaie, S.; Leigh, R.; Louis, S.J.; Raines, G.L.; Beyer, H.G.; O'Reilly, U.M.; Banzhaf, Arnold D.; Blum, W.; Bonabeau, C.; Cantu-Paz, E.W.; ,; ,

    2005-01-01

    We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa statistic to measure correlation between ground truth data and data predicted by the model. Within the genetic algorithm, we introduce a new evaluation function sensitive to spatial correctness and we explore the idea of evolving different rule parameters for different subregions of the land. We reduce the time required to run a simulation from 6 hours to 10 minutes by parallelizing the code and employing a 10-node cluster. Our empirical results suggest that using the spatially sensitive evaluation function does indeed improve the performance of the model and our preliminary results also show that evolving different rule parameters for different regions tends to improve overall model performance. Copyright 2005 ACM.

  3. Fashion sketch design by interactive genetic algorithms

    Science.gov (United States)

    Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.

    2012-11-01

    Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.

  4. Genetic algorithms and aquifer parameter identification

    Institute of Scientific and Technical Information of China (English)

    LI Jing-sheng(李竞生); YAO Lei-hua(姚磊华); LI Yang(李杨)

    2003-01-01

    In order to identify aquifer parameter,authors develops an improved combinatorial method called best chromosome clone plus younger generation chromosome prepotency genetic algorithm (BCC-YGCP-GA), based on a decimal system simple genetic algorithm (SGA). The paper takes unsteady state flows in a two-dimensional, inhomogeneous, confined aquifer for a ideal model, and utilizes SGA and BCC-YGCP-GA coupled to finite element method for identifying aquifer hydraulic conductivity K1,K2,K3 and storage S1,S2,S3, respectively. It is shown from the result that GSA does not reach convergence with 100 generations, whereas convergence rate of BCC-YGCD-GA is very fast. Objective function value calculated by BCC-YGCD-GA is 0.001 29 with 100 generations, and hydraulic conductivity and storage of three zones are almost equal to the "true" values of ideal model.

  5. Identification of Hammerstein Model Based on Quantum Genetic Algorithm

    OpenAIRE

    Zhang Hai Li

    2013-01-01

    Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA).The problems of nonlinear system identification are cast as function optimization overprameter space,and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorith...

  6. Automated Guide Vehicles Dynamic Scheduling Based on Annealing Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Zou Gan

    2013-05-01

    Full Text Available Dispatching automated guided vehicles (AGVs is the common approach for AGVs scheduling in practice, the information about load arrivals in advance was not used to optimize the performance of the automated guided vehicles system (AGVsS. According to the characteristics of the AGVsS, the mathematical model of AGVs scheduling was established. A heuristic algorithm called Annealing Genetic Algorithm (AGA was presented to deal with the AGVs scheduling problem,and applied the algorithm dynamically by using it repeatedly under a combined rolling optimization strategy. the performance of the proposed approach for AGVs scheduling was compared with the dispatching rules by simulation. Results showed that the approach performs significantly better than the dispatching rules and proved that it is really effective for AGVsS.

  7. Designing quantum gates using the genetic algorithm

    Science.gov (United States)

    Kumar, Karthikeyan S.; Paraoanu, G. S.

    2012-12-01

    We demonstrate the usage of Genetic Algorithm (GA) to tailor the radio frequency pulses for producing unitary transformations in qubit systems. We find that the initial population converges to the optimal solution after 10 generations, for a one segment pulse corresponding to single qubit Hadamard gate. For a two qubit CNOT gate, we see the population convergence for a two segment pulse after 150 generations. This demonstrates that the method is suitable for designing quantum gates.

  8. Genetic algorithms in computer aided inductor design

    OpenAIRE

    Jean Fivaz; Willem A. Cronjé

    2004-01-01

    The goal of this investigation is to determine the advantages of using genetic algorithms in computer-aided design as applied to inductors.  These advantages are exploited in design problems with a number of specifications and constraints, as encountered in power electronics during practical inductor design. The design tool should be able to select components, such as cores and wires, from databases of available components, and evaluate these choices based on the components’ characteristic d...

  9. Assembly line balancing using genetic algorithms

    OpenAIRE

    Tanyer, Muzaffer

    1997-01-01

    Ankara : Department of Industrial Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1997. Thesis (Master's) -- Bilkent University, 1997. Includes bibliographical references leaves 69-73 For the last few decades, the genetic algorithms (GAs) have been used as a kind of heuristic in many areas of manufacturing. Facility layout, scheduling, process planning, and assembly line balancing are some of the areas where GAs are already popular. GAs are more efficie...

  10. Genetic algorithms for multicriteria shape optimization of induction furnace

    Science.gov (United States)

    Kůs, Pavel; Mach, František; Karban, Pavel; Doležel, Ivo

    2012-09-01

    In this contribution we deal with a multi-criteria shape optimization of an induction furnace. We want to find shape parameters of the furnace in such a way, that two different criteria are optimized. Since they cannot be optimized simultaneously, instead of one optimum we find set of partially optimal designs, so called Pareto front. We compare two different approaches to the optimization, one using nonlinear conjugate gradient method and second using variation of genetic algorithm. As can be seen from the numerical results, genetic algorithm seems to be the right choice for this problem. Solution of direct problem (coupled problem consisting of magnetic and heat field) is done using our own code Agros2D. It uses finite elements of higher order leading to fast and accurate solution of relatively complicated coupled problem. It also provides advanced scripting support, allowing us to prepare parametric model of the furnace and simply incorporate various types of optimization algorithms.

  11. Quantum-Inspired Genetic Algorithm or Quantum Genetic Algorithm: Which Is It?

    Science.gov (United States)

    Jones, Erika

    2015-04-01

    Our everyday work focuses on genetic algorithms (GAs) related to quantum computing where we call ``related'' algorithms those falling into one of two classes: (1) GAs run on classical computers but making use of quantum mechanical (QM) constructs and (2) GAs run on quantum hardware. Though convention has yet to be set with respect to usage of the accepted terms quantum-inspired genetic algorithm (QIGA) and quantum genetic algorithm (QGA), we find the two terms highly suitable respectively as labels for the aforementioned classes. With these specific definitions in mind, the difference between the QIGA and QGA is greater than might first be appreciated, particularly by those coming from a perspective emphasizing GA use as a general computational tool irrespective of QM aspects (1) suggested by QIGAs and (2) inherent in QGAs. We offer a theoretical standpoint highlighting key differences-both obvious, and more significantly, subtle-to be considered in general design of a QIGA versus that of a QGA.

  12. Emergence of Algorithmic Languages in Genetic Systems

    CERN Document Server

    Angeles, O; Waelbroeck, H

    1997-01-01

    In genetic systems there is a non-trivial interface between the sequence of symbols which constitutes the chromosome, or ``genotype'', and the products which this sequence encodes --- the ``phenotype''. This interface can be thought of as a ``computer''. In this case the chromosome is viewed as an algorithm and the phenotype as the result of the computation. In general only a small fraction of all possible sequences of symbols makes any sense for a given computer. The difficulty of finding meaningful algorithms by random mutation is known as the brittleness problem. In this paper we show that mutation and crossover favour the emergence of an algorithmic language which facilitates the production of meaningful sequences following random mutations of the genotype. We base our conclusions on an analysis of the population dynamics of a variant of Kitano's neurogenetic model wherein the chromosome encodes the rules for cellular division and the phenotype is a 16-cell organism interpreted as a connectivity matrix fo...

  13. Optimization of unit commitment based on genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    蔡兴国; 初壮

    2002-01-01

    How to solve unit commitment and load dispatch of power system by genetic algorithms is discussed in this paper. A combination encoding scheme of binary encoding and floating number encoding and corresponding genetic operators are developed. Meanwhile a contract mapping genetic algorithm is used to enhance traditional GA' s convergence. The result of a practical example shows that this algorithm is effective.

  14. Explicit filtering of building blocks for genetic algorithms

    NARCIS (Netherlands)

    Kemenade, C.H.M. van

    1996-01-01

    Genetic algorithms are often applied to building block problems. We have developed a simple filtering algorithm that can locate building blocks within a bit-string, and does not make assumptions regarding the linkage of the bits. A comparison between the filtering algorithm and genetic algorithms re

  15. Using genetic algorithm feature selection in neural classification systems for image pattern recognition

    Directory of Open Access Journals (Sweden)

    Margarita R. Gamarra A.

    2012-09-01

    Full Text Available Pattern recognition performance depends on variations during extraction, selection and classification stages. This paper presents an approach to feature selection by using genetic algorithms with regard to digital image recognition and quality control. Error rate and kappa coefficient were used for evaluating the genetic algorithm approach Neural networks were used for classification, involving the features selected by the genetic algorithms. The neural network approach was compared to a K-nearest neighbor classifier. The proposed approach performed better than the other methods.

  16. Application layer multicast routing solution based on genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Peng CHENG; Qiufeng WU; Qionghai DAI

    2009-01-01

    Application layer multicast routing is a multi-objective optimization problem.Three routing con-straints,tree's cost,tree's balance and network layer load distribution are analyzed in this paper.The three fitness functions are used to evaluate a multicast tree on the three indexes respectively and one general fitness function is generated.A novel approach based on genetic algorithms is proposed.Numerical simulations show that,compared with geometrical routing rules,the proposed algorithm improve all three indexes,especially on cost and network layer load distribution indexes.

  17. NOVEL QUANTUM-INSPIRED GENETIC ALGORITHM BASED ON IMMUNITY

    Institute of Scientific and Technical Information of China (English)

    Li Ying; Zhao Rongchun; Zhang Yanning; Jiao Licheng

    2005-01-01

    A novel algorithm, the Immune Quantum-inspired Genetic Algorithm (IQGA), is proposed by introducing immune concepts and methods into Quantum-inspired Genetic Algorithm (QGA). With the condition of preserving QGA's advantages, IQGA utilizes the characteristics and knowledge in the pending problems for restraining the repeated and ineffective operations during evolution, so as to improve the algorithm efficiency. The experimental results of the knapsack problem show that the performance of IQGA is superior to the Conventional Genetic Algorithm (CGA), the Immune Genetic Algorithm (IGA) and QGA.

  18. Identification of Hammerstein Model Based on Quantum Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Zhang Hai Li

    2013-07-01

    Full Text Available Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA.The problems of nonlinear system identification are cast as function optimization overprameter space,and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorithm, its salient features of the algorithm parameters, small population size, and the use of Quantum gate update populations, greatly improving the recognition in the optimization of speed and accuracy. Simulation results show the effectiveness of the proposed method.

  19. Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

    Directory of Open Access Journals (Sweden)

    Huaixiao Wang

    2014-01-01

    Full Text Available To verify the availability of the improved quantum genetic algorithm in solving the scheduling engineering personnel problem, the following work has been carried out: the characteristics of the scheduling engineering personnel problem are analyzed, the quantum encoding method is proposed, and an improved quantum genetic algorithm is applied to address the issue. Taking the low efficiency and the bad performance of the conventional quantum genetic algorithm into account, a universal improved quantum genetic algorithm is introduced to solve the scheduling engineering personnel problem. Finally, the examples are applied to verify the effectiveness and superiority of the improved quantum genetic algorithm and the rationality of the encoding method.

  20. Fuzzy Flexible Resource Constrained Project Scheduling Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    查鸿; 张连营

    2014-01-01

    Both fuzzy temporal constraint and flexible resource constraint are considered in project scheduling. In order to obtain an optimal schedule, we propose a genetic algorithm integrated with concepts on fuzzy set theory as well as specialized coding and decoding mechanism. An example demonstrates that the proposed approach can assist the project managers to obtain the optimal schedule effectively and make the correct decision on skill training before a project begins.

  1. THE APPLICATION OF GENETIC ALGORITHM IN NON-LINEAR INVERSION OF ROCK MECHANICS PARAMETERS

    Institute of Scientific and Technical Information of China (English)

    赵晓东

    1998-01-01

    The non-linear inversion of rock mechanics parameters based on genetic algorithm ispresented. The principle and step of genetic algorithm is also given. A brief discussion of thismethod and an application example is presented at the end of this paper. From the satisfied re-sult, quick, convenient and practical new approach is developed to solve this kind of problems.

  2. Adaptive Genetic Algorithm for Sensor Coarse Signal Processing

    Directory of Open Access Journals (Sweden)

    Xuan Huang

    2014-03-01

    Full Text Available As with the development of computer technology and informatization, network technique, sensor technique and communication technology become three necessary components of information industry. As the core technique of sensor application, signal processing mainly determines the sensor performances. For this reason, study on signal processing mode is very important to sensors and the application of sensor network. In this paper, we introduce a new sensor coarse signal processing mode based on adaptive genetic algorithm. This algorithm selects crossover, mutation probability adaptively and compensates multiple operators commutatively to optimize the search process, so that we can obtain the global optimum solution. Based on the proposed algorithm, using auto-correlative characteristic parameter extraction method, it achieves smaller test error in sensor coarse signal processing mode of processing interference signal. We evaluate the proposed approach on a set of data. The experimental results show that, the proposed approach is able to improve the performance in different experimental setting

  3. An improved localization algorithm based on genetic algorithm in wireless sensor networks.

    Science.gov (United States)

    Peng, Bo; Li, Lei

    2015-04-01

    Wireless sensor network (WSN) are widely used in many applications. A WSN is a wireless decentralized structure network comprised of nodes, which autonomously set up a network. The node localization that is to be aware of position of the node in the network is an essential part of many sensor network operations and applications. The existing localization algorithms can be classified into two categories: range-based and range-free. The range-based localization algorithm has requirements on hardware, thus is expensive to be implemented in practice. The range-free localization algorithm reduces the hardware cost. Because of the hardware limitations of WSN devices, solutions in range-free localization are being pursued as a cost-effective alternative to more expensive range-based approaches. However, these techniques usually have higher localization error compared to the range-based algorithms. DV-Hop is a typical range-free localization algorithm utilizing hop-distance estimation. In this paper, we propose an improved DV-Hop algorithm based on genetic algorithm. Simulation results show that our proposed algorithm improves the localization accuracy compared with previous algorithms.

  4. Tuning Schema Matching Systems using Parallel Genetic Algorithms on GPU

    Directory of Open Access Journals (Sweden)

    Yuting Feng

    2010-11-01

    Full Text Available Most recent schema matching systems combine multiple components, each of which employs a particular matching technique with several knobs. The multi-component nature has brought a tuning problem, that is to determine which components to execute and how to adjust the knobs (e.g., thresholds, weights, etc. of these components for domain users. In this paper, we present an approach to automatically tune schema matching systems using genetic algorithms. We match a given schema S against generated matching scenarios, for which the ground truth matches are known, and find a configuration that effectively improves the performance of matching S against real schemas. To search the huge space of configuration candidates efficiently, we adopt genetic algorithms (GAs during the tuning process. To promote the performance of our approach, we implement parallel genetic algorithms on graphic processing units (GPUs based on NVIDIA’s Compute Unified Device Architecture (CUDA. Experiments over four real-world domains with two main matching systems demonstrate that our approach provides more qualified matches over different domains.

  5. Saving Resources with Plagues in Genetic Algorithms

    Energy Technology Data Exchange (ETDEWEB)

    de Vega, F F; Cantu-Paz, E; Lopez, J I; Manzano, T

    2004-06-15

    The population size of genetic algorithms (GAs) affects the quality of the solutions and the time required to find them. While progress has been made in estimating the population sizes required to reach a desired solution quality for certain problems, in practice the sizing of populations is still usually performed by trial and error. These trials might lead to find a population that is large enough to reach a satisfactory solution, but there may still be opportunities to optimize the computational cost by reducing the size of the population. This paper presents a technique called plague that periodically removes a number of individuals from the population as the GA executes. Recently, the usefulness of the plague has been demonstrated for genetic programming. The objective of this paper is to extend the study of plagues to genetic algorithms. We experiment with deceptive trap functions, a tunable difficult problem for GAs, and the experiments show that plagues can save computational time while maintaining solution quality and reliability.

  6. Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

    Science.gov (United States)

    Burchett, Bradley T.

    2003-01-01

    The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

  7. Using Genetic Algorithms in Secured Business Intelligence Mobile Applications

    OpenAIRE

    Silvia TRIF

    2011-01-01

    The paper aims to assess the use of genetic algorithms for training neural networks used in secured Business Intelligence Mobile Applications. A comparison is made between classic back-propagation method and a genetic algorithm based training. The design of these algorithms is presented. A comparative study is realized for determining the better way of training neural networks, from the point of view of time and memory usage. The results show that genetic algorithms based training offer bette...

  8. Grouping genetic algorithms advances and applications

    CERN Document Server

    Mutingi, Michael

    2017-01-01

    This book presents advances and innovations in grouping genetic algorithms, enriched with new and unique heuristic optimization techniques. These algorithms are specially designed for solving industrial grouping problems where system entities are to be partitioned or clustered into efficient groups according to a set of guiding decision criteria. Examples of such problems are: vehicle routing problems, team formation problems, timetabling problems, assembly line balancing, group maintenance planning, modular design, and task assignment. A wide range of industrial grouping problems, drawn from diverse fields such as logistics, supply chain management, project management, manufacturing systems, engineering design and healthcare, are presented. Typical complex industrial grouping problems, with multiple decision criteria and constraints, are clearly described using illustrative diagrams and formulations. The problems are mapped into a common group structure that can conveniently be used as an input scheme to spe...

  9. Implementation of Genetic Algorithm in Predicting Diabetes

    Directory of Open Access Journals (Sweden)

    S.Sapna

    2012-01-01

    Full Text Available Data Mining aims at discovering knowledge out of data and presenting it in a form that is easily compressible to humans. Data Mining represents a process developed to examine large amounts of data routinely collected. The term also refers to a collection of tools used to perform the process. One of the useful applications in the field of medicine is the incurable chronic disease diabetes. Data Mining algorithm is used for testing the accuracy in predicting diabetic status. Fuzzy Systems are been used for solving a wide range of problems in different application domain Genetic Algorithm for designing. Fuzzy systems allows in introducing the learning and adaptation capabilities. Neural Networks are efficiently used for learning membership functions. Diabetes occurs throughout the world, but Type 2 is more common in the most developed countries. The greater increase in prevalence is however expected in Asia and Africa where most patients will likely be found by 2030.

  10. Genetic Algorithm for Hierarchical Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sajid Hussain

    2007-09-01

    Full Text Available Large scale wireless sensor networks (WSNs can be used for various pervasive and ubiquitous applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, we investigate intelligent techniques for cluster formation and management. A genetic algorithm (GA is used to create energy efficient clusters for data dissemination in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique can extend the network lifetime for different network deployment environments.

  11. Genetic algorithms in computer aided inductor design

    Directory of Open Access Journals (Sweden)

    Jean Fivaz

    2004-09-01

    Full Text Available The goal of this investigation is to determine the advantages of using genetic algorithms in computer-aided design as applied to inductors.  These advantages are exploited in design problems with a number of specifications and constraints, as encountered in power electronics during practical inductor design. The design tool should be able to select components, such as cores and wires, from databases of available components, and evaluate these choices based on the components’ characteristic data read from a database of manufacturers’ data-sheets.  The proposed design must always be practically realizable, as close to the desired specifications as possible and within any specified constraints.

  12. A Partial Backlogging Inventory Model for Deteriorating Item under Fuzzy Inflation and Discounting over Random Planning Horizon: A Fuzzy Genetic Algorithm Approach

    Directory of Open Access Journals (Sweden)

    Dipak Kumar Jana

    2013-01-01

    Full Text Available An inventory model for deteriorating item is considered in a random planning horizon under inflation and time value money. The model is described in two different environments: random and fuzzy random. The proposed model allows stock-dependent consumption rate and shortages with partial backlogging. In the fuzzy stochastic model, possibility chance constraints are used for defuzzification of imprecise expected total profit. Finally, genetic algorithm (GA and fuzzy simulation-based genetic algorithm (FSGA are used to make decisions for the above inventory models. The models are illustrated with some numerical data. Sensitivity analysis on expected profit function is also presented. Scope and Purpose. The traditional inventory model considers the ideal case in which depletion of inventory is caused by a constant demand rate. However, to keep sales higher, the inventory level would need to remain high. Of course, this would also result in higher holding or procurement cost. Also, in many real situations, during a longer-shortage period some of the customers may refuse the management. For instance, for fashionable commodities and high-tech products with short product life cycle, the willingness for a customer to wait for backlogging is diminishing with the length of the waiting time. Most of the classical inventory models did not take into account the effects of inflation and time value of money. But in the past, the economic situation of most of the countries has changed to such an extent due to large-scale inflation and consequent sharp decline in the purchasing power of money. So, it has not been possible to ignore the effects of inflation and time value of money any more. The purpose of this paper is to maximize the expected profit in the random planning horizon.

  13. Pragmatic approaches to genetic screening.

    NARCIS (Netherlands)

    Mallia, P.; Have, H.A.M.J. ten

    2005-01-01

    Pragmatic approaches to genetic testing are discussed and appraised. Whilst there are various schools of pragmatism, the Deweyan approach seems to be the most appreciated in bioethics as it allows a historical approach indebted to Hegel. This in turn allows the pragmatist to specify and balance prin

  14. 基于遗传算法的多光谱影像非监督训练分类系统%Unsupervised Training Approaches Using Genetic Algorithms for Multispectral Image Classification Systems

    Institute of Scientific and Technical Information of China (English)

    HUNG Chih-Cheng; XIANG Mei; Minh Pham; KUO Bor-Chen; Tommy L. Coleman

    2007-01-01

    This paper conveys the application of genetic algorithms(GA) which are used to improve unsupervised training and thereby increase the classification accuracy of remotely sensed data. The genetic competitive learning algorithm(GA-CL), an integrated approach of the GA and simple competitive learning(CL) algorithm, was developed for unsupervised training. Genetic algorithms are used to improve the training results for the algorithm. GA is used to prevent falling in the local minima during the process of cluster prototypes learning. The evaluation of the algorithm uses the Jeffries-Matusita(J-M) distance, a measure of statistical separability of pairs of trained clusters. Experiments on Landsat Thema-tic Mapper(TM) data show that the GA improves the simple competitive learning algorithm. Comparisons with other unsupervised training algorithms, the K-means, GA-K-means, and the simple competitive learning algorithm are provided.%本文将遗传算法(GA)应用于非监督训练,提高了遥感数据的分类精度.遗传竞争学习算法(GA-CL)综合了遗传算法和简单的竞争学习算法,可用于改进非监督训练的结果.遗传算法在典型样本聚类的过程中可以避免得到局部最优值.Jeffries-Matusita(J-M)距离法是通过统计测量两个训练类别之间的分离度,可用于评价这种算法.将此算法应用于TM数据的结果显示,遗传算法改进了简单的竞争学习算法,与其他非监督训练算法相比,其提供了K-均值,GA-K-均值和简单的竞争学习算法.

  15. A Novel Shrinking Search Space (SSS) Genetic Algorithm based approach to Security Constrained Optimal Power Flow%基于启发搜索空间遗传算法的安全控制最优潮流方法

    Institute of Scientific and Technical Information of China (English)

    Mithun M Bhasskar; Mohan Benerji; Sydulu M

    2011-01-01

    This paper proposes a new superior genetic algorithm with faster convergence applied to the Security constrained Optimal Power Flow problem. A novel Shrinking Search Space (SSS) technique is applied, which reduces the computational burden and affluence the search space every iteration is put forward. The proposed algorithm is compared with conventional techniques like Simple Genetic Algorithm (SGA) , Adaptive Genetic Algorithm (AGA) , Particle Swarm Technique (PSO) and Differential Evolution (DE) to prove its robustness. Case studies with and without (N - 1) contingency of a line outage are analyzed by proposed algorithm on a test bed of standard IEEE 30 bus system and it's found the proposed technique offers a promising prospect for optimization problems.

  16. The Integration of Cooperation Model and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    In the photogrammetry,some researchers have applied genetic algorithms in aerial image texture classification and reducing hyper-spectrum remote sensing data.Genetic algorithm can rapidly find the solutions which are close to the optimal solution.But it is not easy to find the optimal solution.In order to solve the problem,a cooperative evolution idea integrating genetic algorithm and ant colony algorithm is presented in this paper.On the basis of the advantages of ant colony algorithm,this paper proposes the method integrating genetic algorithms and ant colony algorithm to overcome the drawback of genetic algorithms.Moreover,the paper takes designing texture classification masks of aerial images as an example to illustrate the integration theory and procedures.

  17. Assigning Task by Parallel Genetic Algorithm Based on PVM

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Genetic algorithm has been proposed to solve the problem of taskassignment. Ho wever, it has some drawbacks, e.g., it often takes a long time to find an optima l solution, and the success rate is low. To overcome these problems, a new coars e-grained parallel genetic algorithm with the scheme of central migration is pr e sented, which exploits isolated sub-populations. The new approach has been impl e mented in the PVM environment and has been evaluated on a workstation network fo r solving the task assignment problem. The results show that it not only signifi cantly improves the result quality but also increases the speed for getting best solution.

  18. Genetic Algorithm Based Hybrid Fuzzy System for Assessing Morningness

    Directory of Open Access Journals (Sweden)

    Animesh Biswas

    2014-01-01

    Full Text Available This paper describes a real life case example on the assessment process of morningness of individuals using genetic algorithm based hybrid fuzzy system. It is observed that physical and mental performance of human beings in different time slots of a day are majorly influenced by morningness orientation of those individuals. To measure the morningness of people various self-reported questionnaires were developed by different researchers in the past. Among them reduced version of Morningness-Eveningness Questionnaire is mostly accepted. Almost all of the linguistic terms used in questionnaires are fuzzily defined. So, assessing them in crisp environments with their responses does not seem to be justifiable. Fuzzy approach based research works for assessing morningness of people are very few in the literature. In this paper, genetic algorithm is used to tune the parameters of a Mamdani fuzzy inference model to minimize error with their predicted outputs for assessing morningness of people.

  19. Genetic algorithm for network cost minimization using threshold based discounting

    Directory of Open Access Journals (Sweden)

    Hrvoje Podnar

    2003-01-01

    Full Text Available We present a genetic algorithm for heuristically solving a cost minimization problem applied to communication networks with threshold based discounting. The network model assumes that every two nodes can communicate and offers incentives to combine flow from different sources. Namely, there is a prescribed threshold on every link, and if the total flow on a link is greater than the threshold, the cost of this flow is discounted by a factor α. A heuristic algorithm based on genetic strategy is developed and applied to a benchmark set of problems. The results are compared with former branch and bound results using the CPLEX® solver. For larger data instances we were able to obtain improved solutions using less CPU time, confirming the effectiveness of our heuristic approach.

  20. Locomotive assignment problem with train precedence using genetic algorithm

    Science.gov (United States)

    Noori, Siamak; Ghannadpour, Seyed Farid

    2012-07-01

    This paper aims to study the locomotive assignment problem which is very important for railway companies, in view of high cost of operating locomotives. This problem is to determine the minimum cost assignment of homogeneous locomotives located in some central depots to a set of pre-scheduled trains in order to provide sufficient power to pull the trains from their origins to their destinations. These trains have different degrees of priority for servicing, and the high class of trains should be serviced earlier than others. This problem is modeled using vehicle routing and scheduling problem where trains representing the customers are supposed to be serviced in pre-specified hard/soft fuzzy time windows. A two-phase approach is used which, in the first phase, the multi-depot locomotive assignment is converted to a set of single depot problems, and after that, each single depot problem is solved heuristically by a hybrid genetic algorithm. In the genetic algorithm, various heuristics and efficient operators are used in the evolutionary search. The suggested algorithm is applied to solve the medium sized numerical example to check capabilities of the model and algorithm. Moreover, some of the results are compared with those solutions produced by branch-and-bound technique to determine validity and quality of the model. Results show that suggested approach is rather effective in respect of quality and time.

  1. Feature selection for optimized skin tumor recognition using genetic algorithms.

    Science.gov (United States)

    Handels, H; Ross, T; Kreusch, J; Wolff, H H; Pöppl, S J

    1999-07-01

    In this paper, a new approach to computer supported diagnosis of skin tumors in dermatology is presented. High resolution skin surface profiles are analyzed to recognize malignant melanomas and nevocytic nevi (moles), automatically. In the first step, several types of features are extracted by 2D image analysis methods characterizing the structure of skin surface profiles: texture features based on cooccurrence matrices, Fourier features and fractal features. Then, feature selection algorithms are applied to determine suitable feature subsets for the recognition process. Feature selection is described as an optimization problem and several approaches including heuristic strategies, greedy and genetic algorithms are compared. As quality measure for feature subsets, the classification rate of the nearest neighbor classifier computed with the leaving-one-out method is used. Genetic algorithms show the best results. Finally, neural networks with error back-propagation as learning paradigm are trained using the selected feature sets. Different network topologies, learning parameters and pruning algorithms are investigated to optimize the classification performance of the neural classifiers. With the optimized recognition system a classification performance of 97.7% is achieved.

  2. Optimal Genetic View Selection Algorithm for Data Warehouse

    Institute of Scientific and Technical Information of China (English)

    Wang Ziqiang; Feng Boqin

    2005-01-01

    To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.

  3. Modeling of genetic algorithms with a finite population

    NARCIS (Netherlands)

    Kemenade, C.H.M. van

    1997-01-01

    Cross-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 algorithms, all in

  4. Explicit filtering of building blocks for genetic algorithms

    NARCIS (Netherlands)

    C.H.M. van Kemenade

    1996-01-01

    textabstractGenetic algorithms are often applied to building block problems. We have developed a simple filtering algorithm that can locate building blocks within a bit-string, and does not make assumptions regarding the linkage of the bits. A comparison between the filtering algorithm and genetic

  5. Efficient Dual Domain Decoding of Linear Block Codes Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Ahmed Azouaoui

    2012-01-01

    Full Text Available A computationally efficient algorithm for decoding block codes is developed using a genetic algorithm (GA. The proposed algorithm uses the dual code in contrast to the existing genetic decoders in the literature that use the code itself. Hence, this new approach reduces the complexity of decoding the codes of high rates. We simulated our algorithm in various transmission channels. The performance of this algorithm is investigated and compared with competitor decoding algorithms including Maini and Shakeel ones. The results show that the proposed algorithm gives large gains over the Chase-2 decoding algorithm and reach the performance of the OSD-3 for some quadratic residue (QR codes. Further, we define a new crossover operator that exploits the domain specific information and compare it with uniform and two point crossover. The complexity of this algorithm is also discussed and compared to other algorithms.

  6. Simulation and Optimization for Thermally Coupled Distillation Using Artificial Neural Network and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    王延敏; 姚平经

    2003-01-01

    In this paper, a new approach using artificial neural network and genetic algorithm for the optimization of the thermally coupled distillation is presented. Mathematical model can be constructed with artificial neural network based on the simulation results with ASPEN PLUS. Modified genetic algorithm was used to optimize the model. With the proposed model and optimization arithmetic, mathematical model can be calculated, decision variables and target value can be reached automatically and quickly. A practical example is used to demonstrate the algorithm.

  7. Query Optimization Using Genetic Algorithms in the Vector Space Model

    CERN Document Server

    Mashagba, Eman Al; Nassar, Mohammad Othman

    2011-01-01

    In information retrieval research; Genetic Algorithms (GA) can be used to find global solutions in many difficult problems. This study used different similarity measures (Dice, Inner Product) in the VSM, for each similarity measure we compared ten different GA approaches based on different fitness functions, different mutations and different crossover strategies to find the best strategy and fitness function that can be used when the data collection is the Arabic language. Our results shows that the GA approach which uses one-point crossover operator, point mutation and Inner Product similarity as a fitness function is the best IR system in VSM.

  8. Edge Crossing Minimization Algorithm for Hierarchical Graphs Based on Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    We present an edge crossing minimization algorithm forhierarchical gr aphs based on genetic algorithms, and comparing it with some heuristic algorithm s. The proposed algorithm is more efficient and has the following advantages: th e frame of the algorithms is unified, the method is simple, and its implementati on and revision are easy.

  9. A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making.

    Science.gov (United States)

    van der Lee, J H; Svrcek, W Y; Young, B R

    2008-01-01

    Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.

  10. An improved genetic algorithm with dynamic topology

    Science.gov (United States)

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

    2016-12-01

    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. Project supported by the National Natural Science Foundation for Young Scientists of China (Grant No. 61401011), the National Key Technologies R & D Program of China (Grant No. 2015BAG15B01), and the National Natural Science Foundation of China (Grant No. U1533119).

  11. Order Reduction of Linear Interval Systems Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Dr. Rajendra Prasad

    2010-10-01

    Full Text Available This paper presents an algorithm for order reduction of higher order linear interval system into stable lower order linear interval system by means of Genetic algorithm. In this algorithm the numerator and denominator polynomials are determined by minimizing the Integral square error (ISE using genetic algorithm (GA. The algorithm is simple, rugged and computer oriented. It is shown that the algorithm has several advantages, e.g. the reduced order models retain the steady-state value and stability of the original system. A numerical example illustrates the proposed algorithm.

  12. Using genetic algorithms to select and create features for pattern classification. Technical report

    Energy Technology Data Exchange (ETDEWEB)

    Chang, E.I.; Lippmann, R.P.

    1991-03-11

    Genetic algorithms were used to select and create features and to select reference exemplar patterns for machine vision and speech pattern classification tasks. On a 15-feature machine-vision inspection task, it was found that genetic algorithms performed no better than conventional approaches to feature selection but required much more computation. For a speech recognition task, genetic algorithms required no more computation time than traditional approaches but reduced the number of features required by a factor of five (from 153 to 33 features). On a difficult artificial machine-vision task, genetic algorithms were able to create new features (polynomial functions of the original features) that reduced classification error rates from 10 to almost 0 percent. Neural net and nearest-neighbor classifiers were unable to provide such low error rates using only the original features. Genetic algorithms were also used to reduce the number of reference exemplar patterns and to select the value of k for a k-nearest-neighbor classifier. On a .338 training pattern vowel recognition problem with 10 classes, genetic algorithms simultaneously reduced the number of stored exemplars from 338 to 63 and selected k without significantly decreasing classification accuracy. In all applications, genetic algorithms were easy to apply and found good solutions in many fewer trials than would be required by an exhaustive search. Run times were long but not unreasonable. These results suggest that genetic algorithms may soon be practical for pattern classification problems as faster serial and parallel computers are developed.

  13. 基于遗传算法的绿色建筑优化设计%Green Building Design Optimization Based on Genetic Algorithm Approach

    Institute of Scientific and Technical Information of China (English)

    李涛; 林尧林; 杨薇

    2016-01-01

    Many architects feel that building energy standards are complex and inconvenient in designing green buildings . Considering the advantages of using genetic-algorithm in multi-objective optimization, the genetic algorithm is combined with building energy simulation software for green building design optimization. A typical residential building in Wuhan is selected for study. A number of parameters, including building orientation , window-to-wall ratio , compositions of external wall and roof structure , heating temperature set points, cooling temperature set points, shading factor and glazing type, are selected to reduce the building energy consumption and minimize the construction cost. The outcomes from the optimization are compared with current green building design guideline, and new values for a number of parameters are suggested to be utilized for design reference.%建筑师在实际运用建筑节能设计标准过程中感觉标准较复杂、不方便。将遗传算法在多目标优化设计中的优势与建筑能耗仿真软件结合起来,建立一套绿色建筑优化设计方法或模型。以武汉地区住宅建筑为例,在最低能耗与最低成本的目标控制下,同时,优化设计建筑朝向、窗墙比、外墙构造、屋面构造、采暖温度设定点、空调温度设定点、遮阳系数、窗户玻璃类型的组合因素。将优化结果与现有绿色节能建筑设计规范进行对比,给出了绿色建筑设计中部分参数的新的参考值。

  14. Solving the Traveling Salesman Problem Using New Operators in Genetic Algorithms

    National Research Council Canada - National Science Library

    Naef T. Al Rahedi; Jalal Atoum

    2009-01-01

    ... near-optimum solutions for the TSP. Approach: A proposed genetic algorithm, that employed these new methods of representation and crossover operator, had been implemented using DELPHI programming language on a personal computer...

  15. An improved Genetic Algorithm of Bi-level Coding for Flexible Job Shop Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Ye Li

    2014-07-01

    Full Text Available The current study presents an improved genetic algorithm(GA for the flexible job shop scheduling problem (FJSP. The coding is divided into working sequence level and machine level and two effective crossover operators and mutation operators are designed for the generation and reduce the disruptive effects of genetic operators. The algorithm is tested on instances of 10 working sequences and 10 machines. Computational results show that the proposed GA was successfully and efficiently applied to the FJSP. The results were compared with other approaches, such as traditional GA and GA with neural network. Compared to traditional genetic algorithm, the proposed approach yields significant improvement in solution quality.

  16. Manufacturing Supply Chain Optimization Problem with Time Windows Based on Improved Orthogonal Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHANG Xinhua

    2006-01-01

    Aim to the manufacturing supply chain optimization problem with time windows, presents an improved orthogonal genetic algorithm to solve it. At first, we decompose this problem into two sub-problems (distribution and routing) plus an interface mechanism to allow the two algorithms to collaborate in a master-slave fashion, with the distribution algorithm driving the routing algorithm. At second, we describe the proposed improved orthogonal genetic algorithm for solving giving problem detailedly. Finally, the examples suggest that this proposed approach is feasible, correct and valid.

  17. Training product unit neural networks with genetic algorithms

    Science.gov (United States)

    Janson, D. J.; Frenzel, J. F.; Thelen, D. C.

    1991-01-01

    The training of product neural networks using genetic algorithms is discussed. Two unusual neural network techniques are combined; product units are employed instead of the traditional summing units and genetic algorithms train the network rather than backpropagation. As an example, a neural netork is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima affect the performance of a genetic algorithm, and one method of overcoming this is presented.

  18. Improved Quantum Genetic Algorithm in Application of Scheduling Engineering Personnel

    OpenAIRE

    Huaixiao Wang; Ling Li; Jianyong Liu; Yong Wang; Chengqun Fu

    2014-01-01

    To verify the availability of the improved quantum genetic algorithm in solving the scheduling engineering personnel problem, the following work has been carried out: the characteristics of the scheduling engineering personnel problem are analyzed, the quantum encoding method is proposed, and an improved quantum genetic algorithm is applied to address the issue. Taking the low efficiency and the bad performance of the conventional quantum genetic algorithm into account, a universal improved q...

  19. A Grafted Genetic Algorithm for the Job-Shop Scheduling Problem

    Institute of Scientific and Technical Information of China (English)

    LI Xiang-jun; WANG Shu-zhen; XU Guo-hua

    2004-01-01

    The standard genetic algorithm has limitations of a low convergence rate and premature convergence in solving the job-shop scheduling problem.To overcome these limitations,this paper presents a new improved hybrid genetic algorithm on the basis of the idea of graft in botany.Through the introduction of a grafted population and crossover probability matrix,this algorithm accelerates the convergence rate greatly and also increases the ability to fight premature convergence.Finally,the approach is tested on a set of standard instances taken from the literature and compared with other approaches.The computation results validate the effectiveness of the proposed algorithm.

  20. Genetic algorithm and particle swarm optimization combined with Powell method

    Science.gov (United States)

    Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui

    2013-10-01

    In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.

  1. Solving Hitchcock's transportation problem by a genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    CHEN Hai-feng; CHO Joong Rae; LEE Jeong.Tae

    2004-01-01

    Genetic algorithms (GAs) employ the evolutionary process of Darwin's nature selection theory to find the solutions of optimization problems. In this paper, an implementation of genetic algorithm is put forward to solve a classical transportation problem, namely the Hitchcock's Transportation Problem (HTP), and the GA is improved to search for all optimal solutions and identify them automatically. The algorithm is coded with C++ and validated by numerical examples. The computational results show that the algorithm is efficient for solving the Hitchcock's transportation problem.

  2. A Hybrid Architecture Approach for Quantum Algorithms

    Directory of Open Access Journals (Sweden)

    Mohammad R.S. Aghaei

    2009-01-01

    Full Text Available Problem statement: In this study, a general plan of hybrid architecture for quantum algorithms is proposed. Approach: Analysis of the quantum algorithms shows that these algorithms were hybrid with two parts. First, the relationship of classical and quantum parts of the hybrid algorithms was extracted. Then a general plan of hybrid structure was designed. Results: This plan was illustrated the hybrid architecture and the relationship of classical and quantum parts of the algorithms. This general plan was used to increase implementation performance of quantum algorithms. Conclusion/Recommendations: Moreover, simulation results of quantum algorithms on the hybrid architecture proved that quantum algorithms can be implemented on the general plan as well.

  3. Using Genetic Algorithms for Building Metrics of Collaborative Systems

    Directory of Open Access Journals (Sweden)

    Cristian CIUREA

    2011-01-01

    Full Text Available he paper objective is to reveal the importance of genetic algorithms in building robust metrics of collaborative systems. The main types of collaborative systems in economy are presented and some characteristics of genetic algorithms are described. A genetic algorithm was implemented in order to determine the local maximum and minimum points of the relative complexity function associated to a collaborative banking system. The intelligent collaborative systems based on genetic algorithms, representing the new generation of collaborative systems, are analyzed and the implementation of auto-adaptive interfaces in a banking application is described.

  4. A Parallel Genetic Simulated Annealing Hybrid Algorithm for Task Scheduling

    Institute of Scientific and Technical Information of China (English)

    SHU Wanneng; ZHENG Shijue

    2006-01-01

    In this paper combined with the advantages of genetic algorithm and simulated annealing, brings forward a parallel genetic simulated annealing hybrid algorithm (PGSAHA) and applied to solve task scheduling problem in grid computing .It first generates a new group of individuals through genetic operation such as reproduction, crossover, mutation, etc, and than simulated anneals independently all the generated individuals respectively.When the temperature in the process of cooling no longer falls, the result is the optimal solution on the whole.From the analysis and experiment result, it is concluded that this algorithm is superior to genetic algorithm and simulated annealing.

  5. Optimization-Based Image Segmentation by Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    H. Laurent

    2008-05-01

    Full Text Available Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.

  6. Optimization-Based Image Segmentation by Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Rosenberger C

    2008-01-01

    Full Text Available Abstract Many works in the literature focus on the definition of evaluation metrics and criteria that enable to quantify the performance of an image processing algorithm. These evaluation criteria can be used to define new image processing algorithms by optimizing them. In this paper, we propose a general scheme to segment images by a genetic algorithm. The developed method uses an evaluation criterion which quantifies the quality of an image segmentation result. The proposed segmentation method can integrate a local ground truth when it is available in order to set the desired level of precision of the final result. A genetic algorithm is then used in order to determine the best combination of information extracted by the selected criterion. Then, we show that this approach can either be applied for gray-levels or multicomponents images in a supervised context or in an unsupervised one. Last, we show the efficiency of the proposed method through some experimental results on several gray-levels and multicomponents images.

  7. High performance genetic algorithm for VLSI circuit partitioning

    Science.gov (United States)

    Dinu, Simona

    2016-12-01

    Partitioning is one of the biggest challenges in computer-aided design for VLSI circuits (very large-scale integrated circuits). This work address the min-cut balanced circuit partitioning problem- dividing the graph that models the circuit into almost equal sized k sub-graphs while minimizing the number of edges cut i.e. minimizing the number of edges connecting the sub-graphs. The problem may be formulated as a combinatorial optimization problem. Experimental studies in the literature have shown the problem to be NP-hard and thus it is important to design an efficient heuristic algorithm to solve it. The approach proposed in this study is a parallel implementation of a genetic algorithm, namely an island model. The information exchange between the evolving subpopulations is modeled using a fuzzy controller, which determines an optimal balance between exploration and exploitation of the solution space. The results of simulations show that the proposed algorithm outperforms the standard sequential genetic algorithm both in terms of solution quality and convergence speed. As a direction for future study, this research can be further extended to incorporate local search operators which should include problem-specific knowledge. In addition, the adaptive configuration of mutation and crossover rates is another guidance for future research.

  8. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

    CERN Document Server

    Ünal, Muhammet; Topuz, Vedat; Erdal, Hasan

    2013-01-01

    Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to  process system control.

  9. Performance Analysis of Estimation of Distribution Algorithm and Genetic Algorithm in Zone Routing Protocol

    CERN Document Server

    Rahman, Mst Farhana; Ripon, Kazi Shah Nawaz; Suvo, Md Iqbal Hossain

    2010-01-01

    In this paper, Estimation of Distribution Algorithm (EDA) is used for Zone Routing Protocol (ZRP) in Mobile Ad-hoc Network (MANET) instead of Genetic Algorithm (GA). It is an evolutionary approach, and used when the network size grows and the search space increases. When the destination is outside the zone, EDA is applied to find the route with minimum cost and time. The implementation of proposed method is compared with Genetic ZRP, i.e., GZRP and the result demonstrates better performance for the proposed method. Since the method provides a set of paths to the destination, it results in load balance to the network. As both EDA and GA use random search method to reach the optimal point, the searching cost reduced significantly, especially when the number of data is large.

  10. A Comparison between Memetic algorithm and Genetic algorithm for the cryptanalysis of Simplified Data Encryption Standard algorithm

    CERN Document Server

    Garg, Poonam

    2010-01-01

    Genetic algorithms are a population-based Meta heuristics. They have been successfully applied to many optimization problems. However, premature convergence is an inherent characteristic of such classical genetic algorithms that makes them incapable of searching numerous solutions of the problem domain. A memetic algorithm is an extension of the traditional genetic algorithm. It uses a local search technique to reduce the likelihood of the premature convergence. The cryptanalysis of simplified data encryption standard can be formulated as NP-Hard combinatorial problem. In this paper, a comparison between memetic algorithm and genetic algorithm were made in order to investigate the performance for the cryptanalysis on simplified data encryption standard problems(SDES). The methods were tested and various experimental results show that memetic algorithm performs better than the genetic algorithms for such type of NP-Hard combinatorial problem. This paper represents our first effort toward efficient memetic algo...

  11. Structure, electronic properties and vibrational spectra of (MgF2)n clusters through a combination of genetic algorithm and DFT-based approach

    Science.gov (United States)

    Ganguly Neogi, Soumya; Chaudhury, Pinaki

    2015-12-01

    In this article, we look at the option of using a stochastic optimisation technique, namely genetic algorithm (GA) in association with density functional theory (DFT) to find out the global minimum structures of (MgF2)n clusters with the range of n being between 2 and 10. To confirm whether the structures are indeed the acceptable ones, we go on to evaluate several properties like IR spectroscopic modes, vertical excitation energy, cluster formation energy, vertical ionisation potential and the HOMO-LUMO gap. We stress on the fact that an initial estimation of structure using GA, on two empirical potentials (with and without inclusion of polarisation), leads to a very quick convergence to structures which are quite close to the structures obtained from quantum chemical calculations done from the outset, such as using a DFT calculation. The general structural trend of these systems to form three-dimensional networks is also clear from our study. The lowest energy isomers of these clusters show preference for four-membered Mg2F2 and six-membered Mg3F3 rings. In the IR spectra of (MgF2)n clusters, a blueshift of the Mg-F symmetric stretch and a redshift of asymmetric Mg-F stretching as n increases are obtained.

  12. A genetic algorithm selection perturbative hyper-heuristic for solving ...

    African Journals Online (AJOL)

    http://dx.doi.org/10.5784/31-1-158. 39 ... in solving other combinatorial optimisation problems, this paper investigates the use of a genetic ...... [16] Goldberg D, 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-.

  13. Anisotropic selection in cellular genetic algorithms

    CERN Document Server

    Simoncini, David; Collard, Philippe; Clergue, Manuel

    2008-01-01

    In this paper we introduce a new selection scheme in cellular genetic algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows accurate control of the selective pressure. First we compare this new scheme with the classical rectangular grid shapes solution according to the selective pressure: we can obtain the same takeover time with the two techniques although the spreading of the best individual is different. We then give experimental results that show to what extent AS promotes the emergence of niches that support low coupling and high cohesion. Finally, using a cGA with anisotropic selection on a Quadratic Assignment Problem we show the existence of an anisotropic optimal value for which the best average performance is observed. Further work will focus on the selective pressure self-adjustment ability provided by this new selection scheme.

  14. ATM cash management using genetic algorithm

    Directory of Open Access Journals (Sweden)

    Ahmadreza Ghodrati

    2013-07-01

    Full Text Available Automatic teller machine (ATM is one of the most popular banking facilities to do daily financial transactions. People use ATM services to pay bills, transfer funds and withdraw cash. Therefore, we can treat ATM as a tradition inventory problem and use simulation technique to analysis the amount of cash required on different occasions such as regular days, holidays, etc. The proposed model of this paper uses genetic algorithm to determine the replenishment cash strategy for each ATM. The survey uses all transactions accomplished during the fiscal years of 2011-2012 on one of Iranian banks named Ayande. The study categorizes various ATM based on the average daily transactions into three groups of low, medium and high levels. The preliminary results of our survey indicate that it is possible to do setup different strategies to manage cash in various banks, optimally.

  15. A Genetic Algorithm-Based Feature Selection

    Directory of Open Access Journals (Sweden)

    Babatunde Oluleye

    2014-07-01

    Full Text Available This article details the exploration and application of Genetic Algorithm (GA for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100 features were extracted from set of images found in the Flavia dataset (a publicly available dataset. The extracted features are Zernike Moments (ZM, Fourier Descriptors (FD, Lengendre Moments (LM, Hu 7 Moments (Hu7M, Texture Properties (TP and Geometrical Properties (GP. The main contributions of this article are (1 detailed documentation of the GA Toolbox in MATLAB and (2 the development of a GA-based feature selector using a novel fitness function (kNN-based classification error which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy

  16. Optimized dynamical decoupling via genetic algorithms

    Science.gov (United States)

    Quiroz, Gregory; Lidar, Daniel A.

    2013-11-01

    We utilize genetic algorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.

  17. Optimized Dynamical Decoupling via Genetic Algorithms

    CERN Document Server

    Quiroz, Gregory

    2013-01-01

    We utilize genetic algorithms to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse-intervals and perform the optimization with respect to pulse type and order. In this manner we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.

  18. Warehouse Optimization Model Based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Guofeng Qin

    2013-01-01

    Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.

  19. DNA Technique, cryptography, bit exchange, Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Meenakshi Moza

    2016-07-01

    Full Text Available Internet reliability and performance is based mostly on the underlying routing protocols. The current traffic load has to be taken into account for computation of paths in routing protocols. Addressing the selection of path, from a known source to destination is the basic aim of this paper. Making use of multipoint crossover and mutation is done for optimum and when required alternate path determination. Network scenario which consists of nodes that are fixed and limited to the known size of topology, comprises the population size. This paper proposes a simple method of calculating the shortest path for a network using Genetic Algorithm (GA, which is capable of giving an efficient, dynamic and consistent solution in spite of, what topology, changes in link and node happen and volume of the network. GA is used in this paper for optimization of routing. It helps us in enhancing the performance of the routers.

  20. Optimisation of assembly scheduling in VCIM systems using genetic algorithm

    Science.gov (United States)

    Dao, Son Duy; Abhary, Kazem; Marian, Romeo

    2017-01-01

    Assembly plays an important role in any production system as it constitutes a significant portion of the lead time and cost of a product. Virtual computer-integrated manufacturing (VCIM) system is a modern production system being conceptually developed to extend the application of traditional computer-integrated manufacturing (CIM) system to global level. Assembly scheduling in VCIM systems is quite different from one in traditional production systems because of the difference in the working principles of the two systems. In this article, the assembly scheduling problem in VCIM systems is modeled and then an integrated approach based on genetic algorithm (GA) is proposed to search for a global optimised solution to the problem. Because of dynamic nature of the scheduling problem, a novel GA with unique chromosome representation and modified genetic operations is developed herein. Robustness of the proposed approach is verified by a numerical example.

  1. GAMPMS: Genetic algorithm managed peptide mutant screening.

    Science.gov (United States)

    Long, Thomas; McDougal, Owen M; Andersen, Tim

    2015-06-30

    The prominence of endogenous peptide ligands targeted to receptors makes peptides with the desired binding activity good molecular scaffolds for drug development. Minor modifications to a peptide's primary sequence can significantly alter its binding properties with a receptor, and screening collections of peptide mutants is a useful technique for probing the receptor-ligand binding domain. Unfortunately, the combinatorial growth of such collections can limit the number of mutations which can be explored using structure-based molecular docking techniques. Genetic algorithm managed peptide mutant screening (GAMPMS) uses a genetic algorithm to conduct a heuristic search of the peptide's mutation space for peptides with optimal binding activity, significantly reducing the computational requirements of the virtual screening. The GAMPMS procedure was implemented and used to explore the binding domain of the nicotinic acetylcholine receptor (nAChR) α3β2-isoform with a library of 64,000 α-conotoxin (α-CTx) MII peptide mutants. To assess GAMPMS's performance, it was compared with a virtual screening procedure that used AutoDock to predict the binding affinity of each of the α-CTx MII peptide mutants with the α3β2-nAChR. The GAMPMS implementation performed AutoDock simulations for as few as 1140 of the 64,000 α-CTx MII peptide mutants and could consistently identify a set of 10 peptides with an aggregated binding energy that was at least 98% of the aggregated binding energy of the 10 top peptides from the exhaustive AutoDock screening.

  2. Feature Selection for Image Retrieval based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Preeti Kushwaha

    2016-12-01

    Full Text Available This paper describes the development and implementation of feature selection for content based image retrieval. We are working on CBIR system with new efficient technique. In this system, we use multi feature extraction such as colour, texture and shape. The three techniques are used for feature extraction such as colour moment, gray level co- occurrence matrix and edge histogram descriptor. To reduce curse of dimensionality and find best optimal features from feature set using feature selection based on genetic algorithm. These features are divided into similar image classes using clustering for fast retrieval and improve the execution time. Clustering technique is done by k-means algorithm. The experimental result shows feature selection using GA reduces the time for retrieval and also increases the retrieval precision, thus it gives better and faster results as compared to normal image retrieval system. The result also shows precision and recall of proposed approach compared to previous approach for each image class. The CBIR system is more efficient and better performs using feature selection based on Genetic Algorithm.

  3. A Survey Paper on Deduplication by Using Genetic Algorithm Alongwith Hash-Based Algorithm

    Directory of Open Access Journals (Sweden)

    Miss. J. R. Waykole

    2014-01-01

    Full Text Available In today‟s world, by increasing the volume of information available in digital libraries, most of the system may be affected by the existence of replicas in their warehouses. This is due to the fact that, clean and replica-free warehouse not only allow the retrieval of information which is of higher quality but also lead to more concise data and reduces computational time and resources to process this data. Here, we propose a genetic programming approach along with hash-based similarity i.e, with MD5 and SHA-1 algorithm. This approach removes the replicas data and finds the optimization solution to deduplication of records.

  4. Dynamic airspace configuration by genetic algorithm

    Directory of Open Access Journals (Sweden)

    Marina Sergeeva

    2017-06-01

    Full Text Available With the continuous air traffic growth and limits of resources, there is a need for reducing the congestion of the airspace systems. Nowadays, several projects are launched, aimed at modernizing the global air transportation system and air traffic management. In recent years, special interest has been paid to the solution of the dynamic airspace configuration problem. Airspace sector configurations need to be dynamically adjusted to provide maximum efficiency and flexibility in response to changing weather and traffic conditions. The main objective of this work is to automatically adapt the airspace configurations according to the evolution of traffic. In order to reach this objective, the airspace is considered to be divided into predefined 3D airspace blocks which have to be grouped or ungrouped depending on the traffic situation. The airspace structure is represented as a graph and each airspace configuration is created using a graph partitioning technique. We optimize airspace configurations using a genetic algorithm. The developed algorithm generates a sequence of sector configurations for one day of operation with the minimized controller workload. The overall methodology is implemented and successfully tested with air traffic data taken for one day and for several different airspace control areas of Europe.

  5. Actuator Placement Via Genetic Algorithm for Aircraft Morphing

    Science.gov (United States)

    Crossley, William A.; Cook, Andrea M.

    2001-01-01

    This research continued work that began under the support of NASA Grant NAG1-2119. The focus of this effort was to continue investigations of Genetic Algorithm (GA) approaches that could be used to solve an actuator placement problem by treating this as a discrete optimization problem. In these efforts, the actuators are assumed to be "smart" devices that change the aerodynamic shape of an aircraft wing to alter the flow past the wing, and, as a result, provide aerodynamic moments that could provide flight control. The earlier work investigated issued for the problem statement, developed the appropriate actuator modeling, recognized the importance of symmetry for this problem, modified the aerodynamic analysis routine for more efficient use with the genetic algorithm, and began a problem size study to measure the impact of increasing problem complexity. The research discussed in this final summary further investigated the problem statement to provide a "combined moment" problem statement to simultaneously address roll, pitch and yaw. Investigations of problem size using this new problem statement provided insight into performance of the GA as the number of possible actuator locations increased. Where previous investigations utilized a simple wing model to develop the GA approach for actuator placement, this research culminated with application of the GA approach to a high-altitude unmanned aerial vehicle concept to demonstrate that the approach is valid for an aircraft configuration.

  6. A hybrid genetic algorithm to optimize simple distillation column sequences

    Institute of Scientific and Technical Information of China (English)

    GAN YongSheng; Andreas Linninger

    2004-01-01

    Based on the principles of Genetic Algorithms (GAs), a hybrid genetic algorithm used to optimize simple distillation column sequences was established. A new data structure, a novel arithmetic crossover operator and a dynamic mutation operator were proposed. Together with the feasibility test of distillation columns, they are capable to obtain the optimum simple column sequence at one time without the limitation of the number of mixture components, ideal or non-ideal mixtures and sloppy or sharp splits. Compared with conventional algorithms, this hybrid genetic algorithm avoids solving complicated nonlinear equations and demands less derivative information and computation time. Result comparison between this genetic algorithm and Underwood method and Doherty method shows that this hybrid genetic algorithm is reliable.

  7. Double Exchange Genetic Algorithm for the Synthesis of Linear Array

    Directory of Open Access Journals (Sweden)

    Zhang Jian-Hua

    2012-01-01

    Full Text Available The development of Synthesis of Linear array put forward higher request for complex optimization solutions. This article improves the basic genetic algorithm according to the traditional genetic algorithm easily prematuring convergence and later evolution slow convergence shortcoming. And then, adopt double exchange operator in reproductive strategies and implement dynamic mutation rate in variation operations. Combined characteristics of guarantee to the population diversity based on fitness shared niche while iteration times exponential diminishing, this article creats niche double exchangegenetic algorithm, and applies in pattern synthesis of homogeneous linear array, and simulates multi-objective complex array problem. The result turns out much better in effectively preventing premature and improving the searching efficiency of genetic algorithm than original genetic algorithm and immune genetic algorithm, what will achieve the broad prospect in the antenna array comprehensive field.

  8. OPTIMIZATION BASED ON LMPROVED REAL—CODED GENETIC ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    ShiYu; YuShenglin

    2002-01-01

    An improved real-coded genetic algorithm is pro-posed for global optimization of functionsl.The new algo-rithm is based om the judgement of the searching perfor-mance of basic real-coded genetic algorithm.The opera-tions of basic real-coded genetic algorithm are briefly dis-cussed and selected.A kind of chaos sequence is described in detail and added in the new algorithm ad a disturbance factor.The strategy of field partition is also used to im-prove the strcture of the new algorithm.Numerical ex-periment shows that the mew genetic algorithm can find the global optimum of complex funtions with satistaiting precision.

  9. An Improved Genetic Algorithm with Quasi-Gradient Crossover

    Institute of Scientific and Technical Information of China (English)

    Xiao-Ling Zhang; Li Du; Guang-Wei Zhang; Qiang Miao; Zhong-Lai Wang

    2008-01-01

    The convergence of genetic algorithm is mainly determined by its core operation crossover operation. When the objective function is a multiple hump function, traditional genetic algorithms are easily trapped into local optimum, which is called premature conver gence. In this paper, we propose a new genetic algorithm with improved arithmetic crossover operation based on gradient method. This crossover operation can generate offspring along quasi-gradient direction which is the Steepest descent direction of the value of objective function. The selection operator is also simplified, every individual in the population is given an opportunity to get evolution to avoid complicated selection algorithm. The adaptive mutation operator and the elitist strategy are also applied in this algorithm. The case 4 indicates this algorithm can faster converge to the global optimum and is more stable than the conventional genetic algorithms.

  10. 基于改进GA的分段堆场计划调度方法研究%Block stockyard scheduling approach based on an improved genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    张志英; 计峰; 曾建智

    2015-01-01

    针对船舶分段堆场在调度过程中周转效率低、调度滞后以及调度成本高等问题,以进出场分段在堆场中的调度为研究对象,考虑船舶分段堆场调度过程中的扰动因素,采用基于事件触发式的重调度方法,结合分段质量和移动距离建立数学模型,以移动分段所需的成本为优化目标,提出利用改进遗传算法来选择分段在堆场中停放位置的较优方案,并构建启发式规则来确定分段最优进、出场路径。利用某船厂实际数据对模型进行验证,表明该方法可得到较优的堆场作业计划,实现堆场资源的高效利用。%This paper presented a mathematical model defined as the assignment of the inbound and outbound blocks in the stockyard with the purpose of minimizing the moving cost based on the block scheduling problems such as low turnover, lagged scheduling, high cost, etc. An event⁃triggered rescheduling method was formulated to solve dis⁃turbance factors appeared in the process of block stockyard scheduling. Taking the blocks' mass and moving dis⁃tance into consideration, an improved genetic algorithm is put forward to select the optimal parking locations for the inbound blocks. A heuristic rule is embedded in the algorithm to select the optimal inbound and outbound paths for the blocks. Application data are obtained from a shipyard to validate the model, and the result showed that using the proposed method, an optimal schedule of stockyard can be obtained, and the resources of stockyard can be uti⁃lized with high efficiency.

  11. Nonlinear Inversion of Potential-Field Data Using an Improved Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    Feng Gangding; Chen Chao

    2004-01-01

    The genetic algorithm is useful for solving an inversion of complex nonlinear geophysical equations. The multi-point search of the genetic algorithm makes it easier to find a globally optimal solution and avoid falling into a local extremum. The search efficiency of the genetic algorithm is a key to producing successful solutions in a huge multi-parameter model space. The encoding mechanism of the genetic algorithm affects the searching processes in the evolution. Not all genetic operations perform perfectly in a search under either a binary or decimal encoding system. As such, a standard genetic algorithm (SGA) is sometimes unable to resolve an optimization problem such as a simple geophysical inversion. With the binary encoding system the operation of the crossover may produce more new individuals. The decimal encoding system, on the other hand, makes the mutation generate more new genes. This paper discusses approaches of exploiting the search potentials of genetic operations with different encoding systems and presents a hybrid-encoding mechanism for the genetic algorithm. This is referred to as the hybrid-encoding genetic algorithm (HEGA). The method is based on the routine in which the mutation operation is executed in decimal code and other operations in binary code. HEGA guarantees the birth of better genes by mutation processing with a high probability, so that it is beneficial for resolving the inversions of complicated problems. Synthetic and real-world examples demonstrate the advantages of using HEGA in the inversion of potential-field data.

  12. Using Genetic Algorithms for Navigation Planning in Dynamic Environments

    Directory of Open Access Journals (Sweden)

    Ferhat Uçan

    2012-01-01

    Full Text Available Navigation planning can be considered as a combination of searching and executing the most convenient flight path from an initial waypoint to a destination waypoint. Generally the aim is to follow the flight path, which provides minimum fuel consumption for the air vehicle. For dynamic environments, constraints change dynamically during flight. This is a special case of dynamic path planning. As the main concern of this paper is flight planning, the conditions and objectives that are most probable to be used in navigation problem are considered. In this paper, the genetic algorithm solution of the dynamic flight planning problem is explained. The evolutionary dynamic navigation planning algorithm is developed for compensating the existing deficiencies of the other approaches. The existing fully dynamic algorithms process unit changes to topology one modification at a time, but when there are several such operations occurring in the environment simultaneously, the algorithms are quite inefficient. The proposed algorithm may respond to the concurrent constraint updates in a shorter time for dynamic environment. The most secure navigation of the air vehicle is planned and executed so that the fuel consumption is minimum.

  13. Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms

    Science.gov (United States)

    Jaffe, Jacob D.; Feeney, Caitlin M.; Patel, Jinal; Lu, Xiaodong; Mani, D. R.

    2016-11-01

    Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques.

  14. Multidisciplinary Optimization of Airborne Radome Using Genetic Algorithm

    Science.gov (United States)

    Tang, Xinggang; Zhang, Weihong; Zhu, Jihong

    A multidisciplinary optimization scheme of airborne radome is proposed. The optimization procedure takes into account the structural and the electromagnetic responses simultaneously. The structural analysis is performed with the finite element method using Patran/Nastran, while the electromagnetic analysis is carried out using the Plane Wave Spectrum and Surface Integration technique. The genetic algorithm is employed for the multidisciplinary optimization process. The thicknesses of multilayer radome wall are optimized to maximize the overall transmission coefficient of the antenna-radome system under the constraint of the structural failure criteria. The proposed scheme and the optimization approach are successfully assessed with an illustrative numerical example.

  15. Genetic Algorithm based Decentralized PI Type Controller: Load Frequency Control

    Science.gov (United States)

    Dwivedi, Atul; Ray, Goshaidas; Sharma, Arun Kumar

    2016-12-01

    This work presents a design of decentralized PI type Linear Quadratic (LQ) controller based on genetic algorithm (GA). The proposed design technique allows considerable flexibility in defining the control objectives and it does not consider any knowledge of the system matrices and moreover it avoids the solution of algebraic Riccati equation. To illustrate the results of this work, a load-frequency control problem is considered. Simulation results reveal that the proposed scheme based on GA is an alternative and attractive approach to solve load-frequency control problem from both performance and design point of views.

  16. Synthesis design of artificial magnetic metamaterials using a genetic algorithm.

    Science.gov (United States)

    Chen, P Y; Chen, C H; Wang, H; Tsai, J H; Ni, W X

    2008-08-18

    In this article, we present a genetic algorithm (GA) as one branch of artificial intelligence (AI) for the optimization-design of the artificial magnetic metamaterial whose structure is automatically generated by computer through the filling element methodology. A representative design example, metamaterials with permeability of negative unity, is investigated and the optimized structures found by the GA are presented. It is also demonstrated that our approach is effective for the synthesis of functional magnetic and electric metamaterials with optimal structures. This GA-based optimization-design technique shows great versatility and applicability in the design of functional metamaterials.

  17. Evolutionary algorithm based configuration interaction approach

    CERN Document Server

    Chakraborty, Rahul

    2016-01-01

    A stochastic configuration interaction method based on evolutionary algorithm is designed as an affordable approximation to full configuration interaction (FCI). The algorithm comprises of initiation, propagation and termination steps, where the propagation step is performed with cloning, mutation and cross-over, taking inspiration from genetic algorithm. We have tested its accuracy in 1D Hubbard problem and a molecular system (symmetric bond breaking of water molecule). We have tested two different fitness functions based on energy of the determinants and the CI coefficients of determinants. We find that the absolute value of CI coefficients is a more suitable fitness function when combined with a fixed selection scheme.

  18. Parameter optimization for a high-order band-pass continuous-time sigma-delta modulator MEMS gyroscope using a genetic algorithm approach

    Science.gov (United States)

    Chen, Fang; Chang, Honglong; Yuan, Weizheng; Wilcock, Reuben; Kraft, Michael

    2012-10-01

    This paper describes a novel multiobjective parameter optimization method based on a genetic algorithm (GA) for the design of a sixth-order continuous-time, force feedback band-pass sigma-delta modulator (BP-ΣΔM) interface for the sense mode of a MEMS gyroscope. The design procedure starts by deriving a parameterized Simulink model of the BP-ΣΔM gyroscope interface. The system parameters are then optimized by the GA. Consequently, the optimized design is tested for robustness by a Monte Carlo analysis to find a solution that is both optimal and robust. System level simulations result in a signal-to-noise ratio (SNR) larger than 90 dB in a bandwidth of 64 Hz with a 200° s-1 angular rate input signal; the noise floor is about -100 dBV Hz-1/2. The simulations are compared to measured data from a hardware implementation. For zero input rotation with the gyroscope operating at atmospheric pressure, the spectrum of the output bitstream shows an obvious band-pass noise shaping and a deep notch at the gyroscope resonant frequency. The noise floor of measured power spectral density (PSD) of the output bitstream agrees well with simulation of the optimized system level model. The bias stability, rate sensitivity and nonlinearity of the gyroscope controlled by an optimized BP-ΣΔM closed-loop interface are 34.15° h-1, 22.3 mV °-1 s-1, 98 ppm, respectively. This compares to a simple open-loop interface for which the corresponding values are 89° h-1, 14.3 mV °-1 s-1, 7600 ppm, and a nonoptimized BP-ΣΔM closed-loop interface with corresponding values of 60° h-1, 17 mV °-1 s-1, 200 ppm.

  19. Accurate estimation of seismic source parameters of induced seismicity by a combined approach of generalized inversion and genetic algorithm: Application to The Geysers geothermal area, California

    Science.gov (United States)

    Picozzi, M.; Oth, A.; Parolai, S.; Bindi, D.; De Landro, G.; Amoroso, O.

    2017-05-01

    The accurate determination of stress drop, seismic efficiency, and how source parameters scale with earthquake size is an important issue for seismic hazard assessment of induced seismicity. We propose an improved nonparametric, data-driven strategy suitable for monitoring induced seismicity, which combines the generalized inversion technique together with genetic algorithms. In the first step of the analysis the generalized inversion technique allows for an effective correction of waveforms for attenuation and site contributions. Then, the retrieved source spectra are inverted by a nonlinear sensitivity-driven inversion scheme that allows accurate estimation of source parameters. We therefore investigate the earthquake source characteristics of 633 induced earthquakes (Mw 2-3.8) recorded at The Geysers geothermal field (California) by a dense seismic network (i.e., 32 stations, more than 17.000 velocity records). We find a nonself-similar behavior, empirical source spectra that require an ωγ source model with γ > 2 to be well fit and small radiation efficiency ηSW. All these findings suggest different dynamic rupture processes for smaller and larger earthquakes and that the proportion of high-frequency energy radiation and the amount of energy required to overcome the friction or for the creation of new fractures surface changes with earthquake size. Furthermore, we observe also two distinct families of events with peculiar source parameters that in one case suggests the reactivation of deep structures linked to the regional tectonics, while in the other supports the idea of an important role of steeply dipping faults in the fluid pressure diffusion.

  20. An Efficient Soft Decoder of Block Codes Based on Compact Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Ahmed Azouaoui

    2012-09-01

    Full Text Available Soft-decision decoding is an NP-hard problem with great interest to developers of communication systems. We present an efficient soft-decision decoder of linear block codes based on compact genetic algorithm (cGA and compare its performances with various other decoding algorithms including Shakeel algorithm. The proposed algorithm uses the dual code in contrast to Shakeel algorithm which uses the code itself. Hence, this new approach reduces the decoding complexity of high rates codes. The complexity and an optimized version of this new algorithm are also presented and discussed.

  1. Niching genetic algorithms for optimization in electromagnetics - I. Fundamentals

    OpenAIRE

    Sareni, Bruno; Krähenbühl, Laurent; Nicolas, Alain

    1998-01-01

    Niching methods extend genetic algorithms and permit the investigation of multiple optimal solutions in the search space. In this paper, we review and discuss various strategies of niching for optimization in electromagnetics. Traditional mathematical problems and an electromagnetic benchmark are solved using niching genetic algorithms to show their interest in real world optimization.

  2. Application of a Genetic Algorithm to Nearest Neighbour Classification

    NARCIS (Netherlands)

    Simkin, S.; Verwaart, D.; Vrolijk, H.C.J.

    2005-01-01

    This paper describes the application of a genetic algorithm to nearest-neighbour based imputation of sample data into a census data dataset. The genetic algorithm optimises the selection and weights of variables used for measuring distance. The results show that the measure of fit can be improved by

  3. Semiclassical genetic algorithm with quantum crossover and mutation operations

    CERN Document Server

    SaiToh, Akira; Nakahara, Mikio

    2012-01-01

    In order for finding a good individual for a given fitness function in the context of evolutionary computing, we introduce a novel semiclassical quantum genetic algorithm. It has both of quantum crossover and quantum mutation procedures unlike conventional quantum genetic algorithms. A complexity analysis shows a certain improvement over its classical counterpart.

  4. FEATURE SELECTION USING GENETIC ALGORITHMS FOR HANDWRITTEN CHARACTER RECOGNITION

    NARCIS (Netherlands)

    Kim, G.; Kim, S.

    2004-01-01

    A feature selection method using genetic algorithms which are suitable means for selecting appropriate set of features from ones with huge dimension is proposed. SGA (Simple Genetic Algorithm) and its modified methods are applied to improve the recognition speed as well as the recognition accuracy.

  5. On the runtime analysis of the Simple Genetic Algorithm

    DEFF Research Database (Denmark)

    Oliveto, Pietro S.; Witt, Carsten

    2014-01-01

    For many years it has been a challenge to analyze the time complexity of Genetic Algorithms (GAs) using stochastic selection together with crossover and mutation. This paper presents a rigorous runtime analysis of the well-known Simple Genetic Algorithm (SGA) for OneMax. It is proved that the SGA...

  6. A Test of Genetic Algorithms in Relevance Feedback.

    Science.gov (United States)

    Lopez-Pujalte, Cristina; Guerrero Bote, Vicente P.; Moya Anegon, Felix de

    2002-01-01

    Discussion of information retrieval, query optimization techniques, and relevance feedback focuses on genetic algorithms, which are derived from artificial intelligence techniques. Describes an evaluation of different genetic algorithms using a residual collection method and compares results with the Ide dec-hi method (Salton and Buckley, 1990…

  7. On the Analysis of the Simple Genetic Algorithm

    DEFF Research Database (Denmark)

    Oliveto, Pietro S.; Witt, Carsten

    2012-01-01

    For many years it has been a challenge to analyze the time complexity of Genetic Algorithms (GAs) using stochastic selection together with crossover and mutation. This paper presents a rigorous runtime analysis of the well-known Simple Genetic Algorithm (SGA) for OneMax. It is proved that the SGA...

  8. 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

  9. Genetic algorithms principles and perspectives : a guide to GA theory

    CERN Document Server

    Reeves, Colin R; Reeves, Colin R

    2002-01-01

    Genetic Algorithms (GAs) have become a highly effective tool for solving hard optimization problems. This text provides a survey of some important theoretical contributions, many of which have been proposed and developed in the Foundations of Genetic Algorithms series of workshops.

  10. 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.

  11. Topology control based on quantum genetic algorithm in sensor networks

    Institute of Scientific and Technical Information of China (English)

    SUN Lijuan; GUO Jian; LU Kai; WANG Ruchuan

    2007-01-01

    Nowadays,two trends appear in the application of sensor networks in which both multi-service and quality of service (QoS)are supported.In terms of the goal of low energy consumption and high connectivity,the control on topology is crucial.The algorithm of topology control based on quantum genetic algorithm in sensor networks is proposed.An advantage of the quantum genetic algorithm over the conventional genetic algorithm is demonstrated in simulation experiments.The goals of high connectivity and low consumption of energy are reached.

  12. Global annealing genetic algorithm and its convergence analysis

    Institute of Scientific and Technical Information of China (English)

    张讲社; 徐宗本; 梁怡

    1997-01-01

    A new selection mechanism termed global annealing selection (GAnS) is proposed for the genetic algorithm. It is proved that the GAnS genetic algorithm converges to the global optimums if and only if the parents are allowed to compete for reproduction, and that the variance of population’s fitness can be used as a natural stopping criterion. Numerical simulations show that the new algorithm has stronger ability to escape from local maximum and converges more rapidly than canonical genetic algorithm.

  13. Using Genetic Algorithms in Secured Business Intelligence Mobile Applications

    Directory of Open Access Journals (Sweden)

    Silvia TRIF

    2011-01-01

    Full Text Available The paper aims to assess the use of genetic algorithms for training neural networks used in secured Business Intelligence Mobile Applications. A comparison is made between classic back-propagation method and a genetic algorithm based training. The design of these algorithms is presented. A comparative study is realized for determining the better way of training neural networks, from the point of view of time and memory usage. The results show that genetic algorithms based training offer better performance and memory usage than back-propagation and they are fit to be implemented on mobile devices.

  14. Application of genetic algorithms to tuning fuzzy control systems

    Science.gov (United States)

    Espy, Todd; Vombrack, Endre; Aldridge, Jack

    1993-01-01

    Real number genetic algorithms (GA) were applied for tuning fuzzy membership functions of three controller applications. The first application is our 'Fuzzy Pong' demonstration, a controller that controls a very responsive system. The performance of the automatically tuned membership functions exceeded that of manually tuned membership functions both when the algorithm started with randomly generated functions and with the best manually-tuned functions. The second GA tunes input membership functions to achieve a specified control surface. The third application is a practical one, a motor controller for a printed circuit manufacturing system. The GA alters the positions and overlaps of the membership functions to accomplish the tuning. The applications, the real number GA approach, the fitness function and population parameters, and the performance improvements achieved are discussed. Directions for further research in tuning input and output membership functions and in tuning fuzzy rules are described.

  15. Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    L. M. Galantucci

    2004-06-01

    Full Text Available The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of the optimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies.

  16. Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    L.M. Galantucci

    2008-11-01

    Full Text Available The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of the optimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies.

  17. Multilayer Traffic Network Optimized by Multiobjective Genetic Clustering Algorithm

    Science.gov (United States)

    Wen, Feng; Gen, Mitsuo; Yu, Xinjie

    This paper introduces a multilayer traffic network model and traffic network clustering method for solving the route selection problem (RSP) in car navigation system (CNS). The purpose of the proposed method is to reduce the computation time of route selection substantially with acceptable loss of accuracy by preprocessing the large size traffic network into new network form. The proposed approach further preprocesses the traffic network than the traditional hierarchical network method by clustering method. The traffic network clustering considers two criteria. We specify a genetic clustering algorithm for traffic network clustering and use NSGA-II for calculating the multiple objective Pareto optimal set. The proposed method can overcome the size limitations when solving route selection in CNS. Solutions provided by the proposed algorithm are compared with the optimal solutions to analyze and quantify the loss of accuracy.

  18. Solving Hub Network Problem Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Mursyid Hasan Basri

    2012-01-01

    non-linearity, so there is no guarantee to find the optimal solution. Moreover, it has generated a great number of variables. Therefore, a heuristic method is required to find near optimal solution with reasonable computation time. For this reason, a genetic algorithm (GA-based procedure is proposed. The proposed procedure then is applied to the same problem as discussed in the basic model. The results indicated that there is significant improvement on hub locations. Flows are successfully consolidated to several big ports as expected. With regards to spoke allocations, however, spokes are not fairly allocated.Keywords: Hub and Spoke Model; Marine Transportation; Genetic Algorithm

  19. Solving Hub Network Problem Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Mursyid Hasan Basri

    2012-01-01

    non-linearity, so there is no guarantee to find the optimal solution. Moreover, it has generated a great number of variables. Therefore, a heuristic method is required to find near optimal solution with reasonable computation time. For this reason, a genetic algorithm (GA-based procedure is proposed. The proposed procedure then is applied to the same problem as discussed in the basic model. The results indicated that there is significant improvement on hub locations. Flows are successfully consolidated to several big ports as expected. With regards to spoke allocations, however, spokes are not fairly allocated.Keywords: Hub and Spoke Model; Marine Transportation; Genetic Algorithm

  20. Library design using genetic algorithms for catalyst discovery and optimization

    Science.gov (United States)

    Clerc, Frederic; Lengliz, Mourad; Farrusseng, David; Mirodatos, Claude; Pereira, Sílvia R. M.; Rakotomalala, Ricco

    2005-06-01

    This study reports a detailed investigation of catalyst library design by genetic algorithm (GA). A methodology for assessing GA configurations is described. Operators, which promote the optimization speed while being robust to noise and outliers, are revealed through statistical studies. The genetic algorithms were implemented in GA platform software called OptiCat, which enables the construction of custom-made workflows using a tool box of operators. Two separate studies were carried out (i) on a virtual benchmark and (ii) on real surface response which is derived from HT screening. Additionally, we report a methodology to model a complex surface response by binning the search space in small zones that are then independently modeled by linear regression. In contrast to artificial neural networks, this approach allows one to obtain an explicit model in an analogical form that can be further used in Excel or entered in OptiCat to perform simulations. While speeding the implementation of a hybrid algorithm combining a GA with a knowledge-based extraction engine is described, while speeding up the optimization process by means of virtual prescreening the hybrid GA enables one to open the "black-box" by providing knowledge as a set of association rules.

  1. Intelligent Controller Design for DC Motor Speed Control based on Fuzzy Logic-Genetic Algorithms Optimization

    Directory of Open Access Journals (Sweden)

    Boumediene ALLAOUA

    2008-12-01

    Full Text Available In this paper, an intelligent controller of the DC (Direct current Motor drive is designed using fuzzy logic-genetic algorithms optimization. First, a controller is designed according to fuzzy rules such that the systems are fundamentally robust. To obtain the globally optimal values, parameters of the fuzzy controller are improved by genetic algorithms optimization model. Computer MATLAB work space demonstrate that the fuzzy controller associated to the genetic algorithms approach became very strong, gives a very good results and possesses good robustness.

  2. Self-calibration of a noisy multiple-sensor system with genetic algorithms

    Science.gov (United States)

    Brooks, Richard R.; Iyengar, S. Sitharama; Chen, Jianhua

    1996-01-01

    This paper explores an image processing application of optimization techniques which entails interpreting noisy sensor data. The application is a generalization of image correlation; we attempt to find the optimal gruence which matches two overlapping gray-scale images corrupted with noise. Both taboo search and genetic algorithms are used to find the parameters which match the two images. A genetic algorithm approach using an elitist reproduction scheme is found to provide significantly superior results. The presentation includes a graphic presentation of the paths taken by tabu search and genetic algorithms when trying to find the best possible match between two corrupted images.

  3. GENETIC ALGORITHM BASED PARAMETER TUNING OF PID CONTROLLER FOR COMPOSITION CONTROL SYSTEM

    Directory of Open Access Journals (Sweden)

    Bhawna Tandon

    2011-08-01

    Full Text Available A Composition control system is discussed in this paper in which the PID controller is tuned using Genetic Algorithm & Ziegler-Nichols Tuning Criteria. Tuning methods for PID controllers are very importantfor the process industries. Traditional methods such as Ziegler-Nichols method often do not provide adequate tuning. Genetic Algorithm (GA as an intelligent approach has also been widely used to tune the parameters of PID. Genetic algorithms are used to create an objective function that can evaluate the optimum PID gains based on the controlled systems overall error.

  4. Optimisation of Resource Scheduling in VCIM Systems Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Son Duy Dao

    2012-11-01

    Full Text Available The concept of Virtual Computer-Integrated Manufacturing (VCIM has been proposed for one and a half decade with purpose of overcoming the limitation of traditional Computer-Integrated Manufacturing (CIM as it only works within an enterprise. VCIM system is a promising solution for enterprises to survive in the globally competitive market because it can exploit effectively locally as well as globally distributed resources. In this paper, a Genetic Algorithm (GA based approach for optimising resource scheduling in the VCIM system is proposed. Firstly, based on the latest concept of VCIM system, a class of resource scheduling problems in the system is modelled by using agent-based approach. Secondly, GA with new strategies of handling constraint, chromosome encoding, crossover and mutation is developed to search for optimal solution for the problem. Finally, a case study is given to demonstrate the robustness of the proposed approach.

  5. Scheduling Diet for Diabetes Mellitus Patients using Genetic Algorithm

    Science.gov (United States)

    Syahputra, M. F.; Felicia, V.; Rahmat, R. F.; Budiarto, R.

    2017-01-01

    Diabetes Melitus (DM) is one of metabolic diseases which affects on productivity and lowers the human resources quality. This disease can be controlled by maintaining and regulating balanced and healthy lifestyle especially for daily diet. However, nowadays, there is no system able to help DM patient to get any information of proper diet. Therefore, an approach is required to provide scheduling diet every day in a week with appropriate nutrition for DM patients to help them regulate their daily diet for healing this disease. In this research, we calculate the number of caloric needs using Harris-Benedict equation and propose genetic algorithm for scheduling diet for DM patient. The results show that the greater the number of individuals, the greater the more the possibility of changes in fitness score approaches the best fitness score. Moreover, the greater the created generation, the more the opportunites to obtain best individual with fitness score approaching 0 or equal to 0.

  6. Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits

    Directory of Open Access Journals (Sweden)

    Leandro eWatanabe

    2014-11-01

    Full Text Available This paper describes a hierarchical stochastic simulation algorithm which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method.

  7. DIGITAL SPECKLE CORRELATION METHOD IMPROVED BY GENETIC ALGORITHM

    Institute of Scientific and Technical Information of China (English)

    MaShaopeng; JillGuanchang

    2003-01-01

    The digital speckle correlation method is an important optical metrology for surface displacement and strain measurement. With this technique, the whole field deformation information can be obtained by tracking the geometric points on the speckle images based on a correlation-matching search technique. However, general search techniques suffer from great computational complexity in the processing of speckle images with large deformation and the large random errors in the processing of images of bad quality. In this paper, an advanced approach based on genetic algorithms (GA) for correlation-matching search is developed. Benefiting from the abilities of global optimum and parallelism searching of GA, this new approach can complete the correlation-matching search with less computational consumption and at high accuracy. Two experimental results from the simulated speckle images have proved the efficiency of the new approach.

  8. Efficient Genetic Algorithm sets for optimizing constrained building design problem

    National Research Council Canada - National Science Library

    Wright, Jonathan; Alajmi, Ali

    2016-01-01

    .... This requires trying large possible solutions which need heuristic optimization algorithms. A comparison between several heuristic optimization algorithms showed that Genetic Algorithm (GA) is robust on getting the optimum(s) simulation ( Wetter and Wright, 2004; Brownlee et al., 2011; Bichiou and Krarti, 2011; Sahu et al., 2012 ) while the building simulat...

  9. Seasonal Time Series Analysis Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Pattern discovery from the seasonal time-series is of importance. Traditionally, most of the algorithms of pattern discovery in time series are similar. A novel mode of time series is proposed which integrates the Genetic Algorithm (GA) for the actual problem. The experiments on the electric power yield sequence models show that this algorithm is practicable and effective.

  10. New Iris Localization Method Based on Chaos Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    Jia Dongli; Muhammad Khurram Khan; Zhang Jiashu

    2005-01-01

    This paper present a new method based on Chaos Genetic Algorithm (CGA) to localize the human iris in a given image. First, the iris image is preprocessed to estimate the range of the iris localization, and then CGA is used to extract the boundary of the iris. Simulation results show that the proposed algorithms is efficient and robust, and can achieve sub pixel precision. Because Genetic Algorithms (GAs) can search in a large space, the algorithm does not need accurate estimation of iris center for subsequent localization, and hence can lower the requirement for original iris image processing. On this point, the present localization algirithm is superior to Daugmans algorithm.

  11. Higher-Order Quantum-Inspired Genetic Algorithms

    OpenAIRE

    Nowotniak, Robert; Kucharski, Jacek

    2014-01-01

    This paper presents a theory and an empirical evaluation of Higher-Order Quantum-Inspired Genetic Algorithms. Fundamental notions of the theory have been introduced, and a novel Order-2 Quantum-Inspired Genetic Algorithm (QIGA2) has been presented. Contrary to all QIGA algorithms which represent quantum genes as independent qubits, in higher-order QIGAs quantum registers are used to represent genes strings which allows modelling of genes relations using quantum phenomena. Performance comparis...

  12. Advanced optimization of permanent magnet wigglers using a genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Hajima, Ryoichi [Univ. of Tokyo (Japan)

    1995-12-31

    In permanent magnet wigglers, magnetic imperfection of each magnet piece causes field error. This field error can be reduced or compensated by sorting magnet pieces in proper order. We showed a genetic algorithm has good property for this sorting scheme. In this paper, this optimization scheme is applied to the case of permanent magnets which have errors in the direction of field. The result shows the genetic algorithm is superior to other algorithms.

  13. A Hybrid Algorithm for Satellite Data Transmission Schedule Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    LI Yun-feng; WU Xiao-yue

    2008-01-01

    A hybrid scheduling algorithm based on genetic algorithm is proposed in this paper for reconnaissance satellite data transmission. At first, based on description of satellite data transmission request, satellite data transmission task modal and satellite data transmission scheduling problem model are established. Secondly, the conflicts in scheduling are discussed. According to the meaning of possible conflict, the method to divide possible conflict task set is given. Thirdly, a hybrid algorithm which consists of genetic algorithm and heuristic information is presented. The heuristic information comes from two concepts, conflict degree and conflict number. Finally, an example shows the algorithm's feasibility and performance better than other traditional algorithms.

  14. Genetic algorithm-based wide-band deterministic maximum likelihood direction finding algorithm

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    The wide-band direction finding is one of hit and difficult task in array signal processing. This paper generalizes narrow-band deterministic maximum likelihood direction finding algorithm to the wideband case, and so constructions an object function, then utilizes genetic algorithm for nonlinear global optimization. Direction of arrival is estimated without preprocessing of array data and so the algorithm eliminates the effect of pre-estimate on the final estimation. The algorithm is applied on uniform linear array and extensive simulation results prove the efficacy of the algorithm. In the process of simulation, we obtain the relation between estimation error and parameters of genetic algorithm.

  15. High-Speed General Purpose Genetic Algorithm Processor.

    Science.gov (United States)

    Hoseini Alinodehi, Seyed Pourya; Moshfe, Sajjad; Saber Zaeimian, Masoumeh; Khoei, Abdollah; Hadidi, Khairollah

    2016-07-01

    In this paper, an ultrafast steady-state genetic algorithm processor (GAP) is presented. Due to the heavy computational load of genetic algorithms (GAs), they usually take a long time to find optimum solutions. Hardware implementation is a significant approach to overcome the problem by speeding up the GAs procedure. Hence, we designed a digital CMOS implementation of GA in [Formula: see text] process. The proposed processor is not bounded to a specific application. Indeed, it is a general-purpose processor, which is capable of performing optimization in any possible application. Utilizing speed-boosting techniques, such as pipeline scheme, parallel coarse-grained processing, parallel fitness computation, parallel selection of parents, dual-population scheme, and support for pipelined fitness computation, the proposed processor significantly reduces the processing time. Furthermore, by relying on a built-in discard operator the proposed hardware may be used in constrained problems that are very common in control applications. In the proposed design, a large search space is achievable through the bit string length extension of individuals in the genetic population by connecting the 32-bit GAPs. In addition, the proposed processor supports parallel processing, in which the GAs procedure can be run on several connected processors simultaneously.

  16. A hybrid Genetic and Simulated Annealing Algorithm for Chordal Ring implementation in large-scale networks

    DEFF Research Database (Denmark)

    Riaz, M. Tahir; Gutierrez Lopez, Jose Manuel; Pedersen, Jens Myrup

    2011-01-01

    The paper presents a hybrid Genetic and Simulated Annealing algorithm for implementing Chordal Ring structure in optical backbone network. In recent years, topologies based on regular graph structures gained a lot of interest due to their good communication properties for physical topology...... of the networks. There have been many use of evolutionary algorithms to solve the problems which are in combinatory complexity nature, and extremely hard to solve by exact approaches. Both Genetic and Simulated annealing algorithms are similar in using controlled stochastic method to search the solution....... The paper combines the algorithms in order to analyze the impact of implementation performance....

  17. Genetic Algorithms for Satellite Scheduling Problems

    Directory of Open Access Journals (Sweden)

    Fatos Xhafa

    2012-01-01

    Full Text Available Recently there has been a growing interest in mission operations scheduling problem. The problem, in a variety of formulations, arises in management of satellite/space missions requiring efficient allocation of user requests to make possible the communication between operations teams and spacecraft systems. Not only large space agencies, such as ESA (European Space Agency and NASA, but also smaller research institutions and universities can establish nowadays their satellite mission, and thus need intelligent systems to automate the allocation of ground station services to space missions. In this paper, we present some relevant formulations of the satellite scheduling viewed as a family of problems and identify various forms of optimization objectives. The main complexities, due highly constrained nature, windows accessibility and visibility, multi-objectives and conflicting objectives are examined. Then, we discuss the resolution of the problem through different heuristic methods. In particular, we focus on the version of ground station scheduling, for which we present computational results obtained with Genetic Algorithms using the STK simulation toolkit.

  18. Lunar Habitat Optimization Using Genetic Algorithms

    Science.gov (United States)

    SanScoucie, M. P.; Hull, P. V.; Tinker, M. L.; Dozier, G. V.

    2007-01-01

    Long-duration surface missions to the Moon and Mars will require bases to accommodate habitats for the astronauts. Transporting the materials and equipment required to build the necessary habitats is costly and difficult. The materials chosen for the habitat walls play a direct role in protection against each of the mentioned hazards. Choosing the best materials, their configuration, and the amount required is extremely difficult due to the immense size of the design region. Clearly, an optimization method is warranted for habitat wall design. Standard optimization techniques are not suitable for problems with such large search spaces; therefore, a habitat wall design tool utilizing genetic algorithms (GAs) has been developed. GAs use a "survival of the fittest" philosophy where the most fit individuals are more likely to survive and reproduce. This habitat design optimization tool is a multiobjective formulation of up-mass, heat loss, structural analysis, meteoroid impact protection, and radiation protection. This Technical Publication presents the research and development of this tool as well as a technique for finding the optimal GA search parameters.

  19. Closed Loop System Identification with Genetic Algorithms

    Science.gov (United States)

    Whorton, Mark S.

    2004-01-01

    High performance control design for a flexible space structure is challenging since high fidelity plant models are di.cult to obtain a priori. Uncertainty in the control design models typically require a very robust, low performance control design which must be tuned on-orbit to achieve the required performance. Closed loop system identi.cation is often required to obtain a multivariable open loop plant model based on closed-loop response data. In order to provide an accurate initial plant model to guarantee convergence for standard local optimization methods, this paper presents a global parameter optimization method using genetic algorithms. A minimal representation of the state space dynamics is employed to mitigate the non-uniqueness and over-parameterization of general state space realizations. This control-relevant system identi.cation procedure stresses the joint nature of the system identi.cation and control design problem by seeking to obtain a model that minimizes the di.erence between the predicted and actual closed-loop performance.

  20. Robot path planning using a genetic algorithm

    Science.gov (United States)

    Cleghorn, Timothy F.; Baffes, Paul T.; Wang, Liu

    1988-01-01

    Robot path planning can refer either to a mobile vehicle such as a Mars Rover, or to an end effector on an arm moving through a cluttered workspace. In both instances there may exist many solutions, some of which are better than others, either in terms of distance traversed, energy expended, or joint angle or reach capabilities. A path planning program has been developed based upon a genetic algorithm. This program assumes global knowledge of the terrain or workspace, and provides a family of good paths between the initial and final points. Initially, a set of valid random paths are constructed. Successive generations of valid paths are obtained using one of several possible reproduction strategies similar to those found in biological communities. A fitness function is defined to describe the goodness of the path, in this case including length, slope, and obstacle avoidance considerations. It was found that with some reproduction strategies, the average value of the fitness function improved for successive generations, and that by saving the best paths of each generation, one could quite rapidly obtain a collection of good candidate solutions.

  1. Multiobjective Genetic Algorithm applied to dengue control.

    Science.gov (United States)

    Florentino, Helenice O; Cantane, Daniela R; Santos, Fernando L P; Bannwart, Bettina F

    2014-12-01

    Dengue fever is an infectious disease caused by a virus of the Flaviridae family and transmitted to the person by a mosquito of the genus Aedes aegypti. This disease has been a global public health problem because a single mosquito can infect up to 300 people and between 50 and 100 million people are infected annually on all continents. Thus, dengue fever is currently a subject of research, whether in the search for vaccines and treatments for the disease or efficient and economical forms of mosquito control. The current study aims to study techniques of multiobjective optimization to assist in solving problems involving the control of the mosquito that transmits dengue fever. The population dynamics of the mosquito is studied in order to understand the epidemic phenomenon and suggest strategies of multiobjective programming for mosquito control. A Multiobjective Genetic Algorithm (MGA_DENGUE) is proposed to solve the optimization model treated here and we discuss the computational results obtained from the application of this technique.

  2. Application of Genetic Algorithm in the Layout of Fixture Components

    Institute of Scientific and Technical Information of China (English)

    焦黎; 孙厚芳

    2003-01-01

    Automation in the layout of fixture components is important to achieve efficiency and flexibility in computer aided fixture design. Based on basic genetic algorithm and particulars of different fixture components, a method of layout space division is presented. Such techniques as suitable crossover rate, mutation rate and selection arithmetic element are adopted in the genetic operation. The results show that genetic algorithm can effectively be applied in the automatic layout of fixture components.

  3. Key Frames Extraction Based on the Improved Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    ZHOU Dong-sheng; JIANG Wei; YI Peng-fei; LIURui

    2014-01-01

    In order toovercomethe poor local search ability of genetic algorithm, resulting in the basic genetic algorithm is time-consuming, and low search abilityin the late evolutionary, we use thegray coding instead ofbinary codingatthebeginning of the coding;we use multi-point crossoverto replace the originalsingle-point crossoveroperation.Finally, theexperimentshows that the improved genetic algorithmnot only has a strong search capability, but also thestability has been effectively improved.

  4. Coordinating Exploration and Exploitation To Construct Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    江瑞; 罗予频; 胡东成; 司徒国业

    2002-01-01

    A new genetic algorithm is proposed based on the careful coordination of the exploration in the solution space of the given problem and the exploitation of the information from the previous search. In the new algorithm architecture, the population in each generation consists of three sub-populations: a preserved part, a reproduced part, and a randomized part. Two parameters are incorporated into the algorithm to efficiently control the percentage of each sub-population to achieve good balance between the exploration and exploitation processes during the optimization. By modeling the algorithm as a homogeneous finite Markov chain, the new genetic algorithm is shown to converge towards the global optimum of the problem at hand. Experiments were designed to test the algorithm using the Rastrigin function, the Griewangk function, and the Schaffer function. Data analyses using the average success ratio, the average objective calculating number, the average first passage time to solution, and the standard deviation of the first passage time were compared with those of the canonical genetic algorithm, the elitist genetic algorithm, and the steady genetic algorithm. The results show strong evidence that our algorithm is superior in performance in terms of economy, robustness and efficiency.

  5. Modelling Agro-Met Station Observations Using Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Prashant Kumar

    2014-01-01

    Full Text Available The present work discusses the development of a nonlinear data-fitting technique based on genetic algorithm (GA for the prediction of routine weather parameters using observations from Agro-Met Stations (AMS. The algorithm produces the equations that best describe the temporal evolutions of daily minimum and maximum near-surface (at 2.5-meter height air temperature and relative humidity and daily averaged wind speed (at 10-meter height at selected AMS locations. These enable the forecasts of these weather parameters, which could have possible use in crop forecast models. The forecast equations developed in the present study use only the past observations of the above-mentioned parameters. This approach, unlike other prediction methods, provides explicit analytical forecast equation for each parameter. The predictions up to 3 days in advance have been validated using independent datasets, unknown to the training algorithm, with impressive results. The power of the algorithm has also been demonstrated by its superiority over persistence forecast used as a benchmark.

  6. A Parallel Genetic Algorithm for Automated Electronic Circuit Design

    Science.gov (United States)

    Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris

    2000-01-01

    Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency

  7. A genetic algorithm for solving supply chain network design model

    Science.gov (United States)

    Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

    2013-09-01

    Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

  8. A test sheet generating algorithm based on intelligent genetic algorithm and hierarchical planning

    Science.gov (United States)

    Gu, Peipei; Niu, Zhendong; Chen, Xuting; Chen, Wei

    2013-03-01

    In recent years, computer-based testing has become an effective method to evaluate students' overall learning progress so that appropriate guiding strategies can be recommended. Research has been done to develop intelligent test assembling systems which can automatically generate test sheets based on given parameters of test items. A good multisubject test sheet depends on not only the quality of the test items but also the construction of the sheet. Effective and efficient construction of test sheets according to multiple subjects and criteria is a challenging problem. In this paper, a multi-subject test sheet generation problem is formulated and a test sheet generating approach based on intelligent genetic algorithm and hierarchical planning (GAHP) is proposed to tackle this problem. The proposed approach utilizes hierarchical planning to simplify the multi-subject testing problem and adopts genetic algorithm to process the layered criteria, enabling the construction of good test sheets according to multiple test item requirements. Experiments are conducted and the results show that the proposed approach is capable of effectively generating multi-subject test sheets that meet specified requirements and achieve good performance.

  9. Integrating GIS and genetic algorithms for automating land partitioning

    Science.gov (United States)

    Demetriou, Demetris; See, Linda; Stillwell, John

    2014-08-01

    Land consolidation is considered to be the most effective land management planning approach for controlling land fragmentation and hence improving agricultural efficiency. Land partitioning is a basic process of land consolidation that involves the subdivision of land into smaller sub-spaces subject to a number of constraints. This paper explains the development of a module called LandParcelS (Land Parcelling System) that integrates geographical information systems and a genetic algorithm to automate the land partitioning process by designing and optimising land parcels in terms of their shape, size and value. This new module has been applied to two land blocks that are part of a larger case study area in Cyprus. Partitioning is carried out by guiding a Thiessen polygon process within ArcGIS and it is treated as a multiobjective problem. The results suggest that a step forward has been made in solving this complex spatial problem, although further research is needed to improve the algorithm. The contribution of this research extends land partitioning and space partitioning in general, since these approaches may have relevance to other spatial processes that involve single or multi-objective problems that could be solved in the future by spatial evolutionary algorithms.

  10. Visibility conflict resolution for multiple antennae and multi-satellites via genetic algorithm

    Science.gov (United States)

    Lee, Junghyun; Hyun, Chung; Ahn, Hyosung; Wang, Semyung; Choi, Sujin; Jung, Okchul; Chung, Daewon; Ko, Kwanghee

    Satellite mission control systems typically are operated by scheduling missions to the visibility between ground stations and satellites. The communication for the mission is achieved by interacting with satellite visibility and ground station support. Specifically, the satellite forms a cone-type visibility passing over a ground station, and the antennas of ground stations support the satellite. When two or more satellites pass by at the same time or consecutively, the satellites may generate a visibility conflict. As the number of satellites increases, solving visibility conflict becomes important issue. In this study, we propose a visibility conflict resolution algorithm of multi-satellites by using a genetic algorithm (GA). The problem is converted to scheduling optimization modeling. The visibility of satellites and the supports of antennas are considered as tasks and resources individually. The visibility of satellites is allocated to the total support time of antennas as much as possible for users to obtain the maximum benefit. We focus on a genetic algorithm approach because the problem is complex and not defined explicitly. The genetic algorithm can be applied to such a complex model since it only needs an objective function and can approach a global optimum. However, the mathematical proof of global optimality for the genetic algorithm is very challenging. Therefore, we apply a greedy algorithm and show that our genetic approach is reasonable by comparing with the performance of greedy algorithm application.

  11. Mobile robot dynamic path planning based on improved genetic algorithm

    Science.gov (United States)

    Wang, Yong; Zhou, Heng; Wang, Ying

    2017-08-01

    In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

  12. A Parallel Genetic Algorithm Based on Spark for Pairwise Test Suite Generation

    Institute of Scientific and Technical Information of China (English)

    Rong-Zhi Qi; Zhi-Jian Wang; Shui-Yan Li

    2016-01-01

    Pairwise testing is an effective test generation technique that requires all pairs of parameter values to be covered by at least one test case. It has been proven that generating minimum test suite is an NP-complete problem. Genetic algorithms have been used for pairwise test suite generation by researchers. However, it is always a time-consuming process, which leads to significant limitations and obstacles for practical use of genetic algorithms towards large-scale test problems. Parallelism will be an effective way to not only enhance the computation performance but also improve the quality of the solutions. In this paper, we use Spark, a fast and general parallel computing platform, to parallelize the genetic algorithm to tackle the problem. We propose a two-phase parallelization algorithm including fitness evaluation parallelization and genetic operation parallelization. Experimental results show that our algorithm outperforms the sequential genetic algorithm and competes with other approaches in both test suite size and computational performance. As a result, our algorithm is a promising improvement of the genetic algorithm for pairwise test suite generation.

  13. Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

    Science.gov (United States)

    Yang, Shengxiang

    2008-01-01

    In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

  14. A Software Pattern of the Genetic Algorithm -a Study on Reusable Object Model of Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    The Genetic Algorithm (GA) has been a pop research field, butthere is little concern on GA in view of Software Engineering and this result in a serie s of problems. In this paper, we extract a GA's software pattern, draw a model d iagram of the reusable objects, analyze the advantages and disadvantages of the pattern, and give a sample code at the end. We are then able to improve the reus ability and expansibility of GA. The results make it easier to program a new GA code by using some existing successful operators, thereby reducing the difficult ies and workload of programming a GA's code, and facilitate the GA application.

  15. Maximizing lifetime of wireless sensor networks using genetic approach

    DEFF Research Database (Denmark)

    Wagh, Sanjeev; Prasad, Ramjee

    2014-01-01

    the cluster head intelligently using auction data of node i.e. its local battery power, topology strength and external battery support. The network lifetime is the centre focus of the research paper which explores intelligently selection of cluster head using auction based approach. The multi......-objective parameters are considered to solve the problem using genetic algorithm of evolutionary approach....

  16. MODIFIED GENETIC ALGORITHM APPLIED TO SOLVE PRODUCT FAMILY OPTIMIZATION PROBLEM

    Institute of Scientific and Technical Information of China (English)

    CHEN Chunbao; WANG Liya

    2007-01-01

    The product family design problem solved by evolutionary algorithms is discussed. A successfiil product family design method should achieve an optimal tradeoff among a set of competing objectives, which involves maximizing conunonality across the family of products and optimizing the performances of each product in the family. A 2-level chromosome structured genetic algorithm (2LCGA) is proposed to solve this dass of problems and its performance is analyzed in comparing its results with those obtained with other methods. By interpreting the chromosome as a 2-level linear structure, the variable commonality genetic algorithm (GA) is constructed to vary the amount of platform commonality and automatically searches across varying levels of commonality for the platform while trying to resolve the tradeoff between commonality and individual product performance within the product family during optimization process. By incorporating a commonality assessing index to the problem formulation, the 2LCGA optimize the product platform and its corresponding family of products in a single stage, which can yield improvements in the overall performance of the product family compared with two-stage approaches (the first stage involves determining the best settings for the platform variables and values of unique variables are found for each product in the second stage). The scope of the algorithm is also expanded by introducing a classification mechanism to allow multiple platforms to be considered during product family optimization, offering opportunities for superior overall design by more efficacious tradeoffs between commonality and performance. The effectiveness of 2LCGA is demonstrated through the design of a family of universal electric motors and comparison against previous results.

  17. GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE

    Directory of Open Access Journals (Sweden)

    Ashish Jain

    2012-07-01

    Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.

  18. Restart-Based Genetic Algorithm for the Quadratic Assignment Problem

    Science.gov (United States)

    Misevicius, Alfonsas

    The power of genetic algorithms (GAs) has been demonstrated for various domains of the computer science, including combinatorial optimization. In this paper, we propose a new conceptual modification of the genetic algorithm entitled a "restart-based genetic algorithm" (RGA). An effective implementation of RGA for a well-known combinatorial optimization problem, the quadratic assignment problem (QAP), is discussed. The results obtained from the computational experiments on the QAP instances from the publicly available library QAPLIB show excellent performance of RGA. This is especially true for the real-life like QAPs.

  19. Aerodynamic Optimum Design of Transonic Turbine Cascades Using Genetic Algorithms

    Institute of Scientific and Technical Information of China (English)

    1997-01-01

    This paper presents an aerodynamic optimum design method for transonic turbine cascades based on the Genetic Algorithms coupled to the inviscid flow Euler Solver and the boundary-layer calculation.The Genetic Algorithms control the evolution of a population of cascades towards an optimum design.The fitness value of each string is evaluated using the flow solver.The design procedure has been developed and the behavior of the genetic algorithms has been tested.The objective functions of the design examples are the minimum mean-square deviation between the aimed pressure and computed pressure and the minimum amount of user expertise.

  20. Support Vector Machine Optimized by Improved Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xiang Chang Sheng

    2013-07-01

    Full Text Available Parameters of support vector machines (SVM which is optimized by standard genetic algorithm is easy to trap into the local minimum, in order to get the optimal parameters of support vector machine, this paper proposed a parameters optimization method for support vector machines based on improved genetic algorithm, the simulation experiment is carried out on 5 benchmark datasets. The simulation show that the proposed method not only can assure the classification precision, but also can reduce training time markedly compared with standard genetic algorithm.

  1. Chaotic genetic algorithm for gene selection and classification problems.

    Science.gov (United States)

    Chuang, Li-Yeh; Yang, Cheng-San; Li, Jung-Chike; Yang, Cheng-Hong

    2009-10-01

    Pattern recognition techniques suffer from a well-known curse, the dimensionality problem. The microarray data classification problem is a classical complex pattern recognition problem. Selecting relevant genes from microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, multiclass categories being involved, and the usually small sample size. The goal of feature (gene) selection is to select those subsets of differentially expressed genes that are potentially relevant for distinguishing the sample classes. In this paper, information gain and chaotic genetic algorithm are proposed for the selection of relevant genes, and a K-nearest neighbor with the leave-one-out crossvalidation method serves as a classifier. The chaotic genetic algorithm is modified by using the chaotic mutation operator to increase the population diversity. The enhanced population diversity expands the GA's search ability. The proposed approach is tested on 10 microarray data sets from the literature. The experimental results show that the proposed method not only effectively reduced the number of gene expression levels, but also achieved lower classification error rates than other methods.

  2. Partial AUC maximization for essential gene prediction using genetic algorithms.

    Science.gov (United States)

    Hwang, Kyu-Baek; Ha, Beom-Yong; Ju, Sanghun; Kim, Sangsoo

    2013-01-01

    Identifying genes indispensable for an organism's life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, protein-protein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature's relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.

  3. Hybrid genetic algorithm in the Hopfield network for maximum 2-satisfiability problem

    Science.gov (United States)

    Kasihmuddin, Mohd Shareduwan Mohd; Sathasivam, Saratha; Mansor, Mohd. Asyraf

    2017-08-01

    Heuristic method was designed for finding optimal solution more quickly compared to classical methods which are too complex to comprehend. In this study, a hybrid approach that utilizes Hopfield network and genetic algorithm in doing maximum 2-Satisfiability problem (MAX-2SAT) was proposed. Hopfield neural network was used to minimize logical inconsistency in interpretations of logic clauses or program. Genetic algorithm (GA) has pioneered the implementation of methods that exploit the idea of combination and reproduce a better solution. The simulation incorporated with and without genetic algorithm will be examined by using Microsoft Visual 2013 C++ Express software. The performance of both searching techniques in doing MAX-2SAT was evaluate based on global minima ratio, ratio of satisfied clause and computation time. The result obtained form the computer simulation demonstrates the effectiveness and acceleration features of genetic algorithm in doing MAX-2SAT in Hopfield network.

  4. Mining Interesting Positive and Negative Association Rule Based on Improved Genetic Algorithm (MIPNAR_GA

    Directory of Open Access Journals (Sweden)

    Nikky Suryawanshi Rai

    2014-01-01

    Full Text Available Association Rule mining is very efficient technique for finding strong relation between correlated data. The correlation of data gives meaning full extraction process. For the mining of positive and negative rules, a variety of algorithms are used such as Apriori algorithm and tree based algorithm. A number of algorithms are wonder performance but produce large number of negative association rule and also suffered from multi-scan problem. The idea of this paper is to eliminate these problems and reduce large number of negative rules. Hence we proposed an improved approach to mine interesting positive and negative rules based on genetic and MLMS algorithm. In this method we used a multi-level multiple support of data table as 0 and 1. The divided process reduces the scanning time of database. The proposed algorithm is a combination of MLMS and genetic algorithm. This paper proposed a new algorithm (MIPNAR_GA for mining interesting positive and negative rule from frequent and infrequent pattern sets. The algorithm is accomplished in to three phases: a.Extract frequent and infrequent pattern sets by using apriori method b.Efficiently generate positive and negative rule. c.Prune redundant rule by applying interesting measures. The process of rule optimization is performed by genetic algorithm and for evaluation of algorithm conducted the real world dataset such as heart disease data and some standard data used from UCI machine learning repository.

  5. Global Convergence Analysis of Non-Crossover Genetic Algorithm and Its Application to Optimization

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Selection, crossover, and mutation are three main operators of the canonical genetic algorithm (CGA). This paper presents a new approach to the genetic algorithm. This new approach applies only to mutation and selection operators. The paper proves that the search process of the non-crossover genetic algorithm (NCGA) is an ergodic homogeneous Markov chain. The proof of its convergence to global optimum is presented. Some nonlinear multi-modal optimization problems are applied to test the efficacy of the NCGA. NP-hard traveling salesman problem (TSP) is cited here as the benchmark problem to test the efficiency of the algorithm. The simulation result shows that NCGA achieves much faster convergence speed than CGA in terms of CPU time. The convergence speed per epoch of NCGA is also faster than that of CGA.

  6. Scheduling of Automated Guided Vehicle and Flexible Jobshop using Jumping Genes Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    P. Paul Pandian

    2012-01-01

    Full Text Available Problem statement: Now a day’s many researchers try Genetic algorithm based optimization to find near optimal solution for flexible job shop. It is a global search. In Our study in the GA, some changes are made to search locally and globally by adding jumping genes operation. A typical flexible job shop model is considered for this research study. For that layout, five different example problems are formulated for purpose of evaluation. The material flow time for different shop types, processing times of products, waiting times of products, sequences of products are created and given in tabular form. Approach: The one of best evolutionary approach i.e., genetic algorithm with jumping genes operation is applied in this study, to optimize AGV flow time and the performance measures of Flexible Job shop manufacturing system. The non dominated sorting approach is used. Genetic algorithm with jumping genes operator is used to evaluate the method. Results: The AGV flow sequence is found out. Using this flow sequence make span, flow time of products with AGV, completion of the products is minimized. The position of the shop types are calculated for all products. The effectiveness of the proposed method is proved by comparing with Hamed Fazlollahtabar method. Conclusion: It is found that jumping genes genetic algorithm delivered good solutions as like as other evolutionary algorithms. Jumping genes genetic algorithm may applied to Multi objective optimization techniques in future.

  7. PM Synchronous Motor Dynamic Modeling with Genetic Algorithm ...

    African Journals Online (AJOL)

    Adel

    intelligence like neural network, genetic algorithm, etc (El Shahat and El Shewy, ..... maximum power factor has the most powerful effect on all various machine .... Artificial Intelligence, Renewable Energy, Power System, Control Systems, PV ...

  8. Cystic Lung Diseases: Algorithmic Approach.

    Science.gov (United States)

    Raoof, Suhail; Bondalapati, Praveen; Vydyula, Ravikanth; Ryu, Jay H; Gupta, Nishant; Raoof, Sabiha; Galvin, Jeff; Rosen, Mark J; Lynch, David; Travis, William; Mehta, Sanjeev; Lazzaro, Richard; Naidich, David

    2016-10-01

    Cysts are commonly seen on CT scans of the lungs, and diagnosis can be challenging. Clinical and radiographic features combined with a multidisciplinary approach may help differentiate among various disease entities, allowing correct diagnosis. It is important to distinguish cysts from cavities because they each have distinct etiologies and associated clinical disorders. Conditions such as emphysema, and cystic bronchiectasis may also mimic cystic disease. A simplified classification of cysts is proposed. Cysts can occur in greater profusion in the subpleural areas, when they typically represent paraseptal emphysema, bullae, or honeycombing. Cysts that are present in the lung parenchyma but away from subpleural areas may be present without any other abnormalities on high-resolution CT scans. These are further categorized into solitary or multifocal/diffuse cysts. Solitary cysts may be incidentally discovered and may be an age related phenomenon or may be a remnant of prior trauma or infection. Multifocal/diffuse cysts can occur with lymphoid interstitial pneumonia, Birt-Hogg-Dubé syndrome, tracheobronchial papillomatosis, or primary and metastatic cancers. Multifocal/diffuse cysts may be associated with nodules (lymphoid interstitial pneumonia, light-chain deposition disease, amyloidosis, and Langerhans cell histiocytosis) or with ground-glass opacities (Pneumocystis jirovecii pneumonia and desquamative interstitial pneumonia). Using the results of the high-resolution CT scans as a starting point, and incorporating the patient's clinical history, physical examination, and laboratory findings, is likely to narrow the differential diagnosis of cystic lesions considerably. Copyright © 2016 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

  9. A genetic similarity algorithm for searching the Gene Ontology terms and annotating anonymous protein sequences.

    Science.gov (United States)

    Othman, Razib M; Deris, Safaai; Illias, Rosli M

    2008-02-01

    A genetic similarity algorithm is introduced in this study to find a group of semantically similar Gene Ontology terms. The genetic similarity algorithm combines semantic similarity measure algorithm with parallel genetic algorithm. The semantic similarity measure algorithm is used to compute the similitude strength between the Gene Ontology terms. Then, the parallel genetic algorithm is employed to perform batch retrieval and to accelerate the search in large search space of the Gene Ontology graph. The genetic similarity algorithm is implemented in the Gene Ontology browser named basic UTMGO to overcome the weaknesses of the existing Gene Ontology browsers which use a conventional approach based on keyword matching. To show the applicability of the basic UTMGO, we extend its structure to develop a Gene Ontology -based protein sequence annotation tool named extended UTMGO. The objective of developing the extended UTMGO is to provide a simple and practical tool that is capable of producing better results and requires a reasonable amount of running time with low computing cost specifically for offline usage. The computational results and comparison with other related tools are presented to show the effectiveness of the proposed algorithm and tools.

  10. Multiscale modeling for classification of SAR imagery using hybrid EM algorithm and genetic algorithm

    Institute of Scientific and Technical Information of China (English)

    Xianbin Wen; Hua Zhang; Jianguang Zhang; Xu Jiao; Lei Wang

    2009-01-01

    A novel method that hybridizes genetic algorithm (GA) and expectation maximization (EM) algorithm for the classification of syn-thetic aperture radar (SAR) imagery is proposed by the finite Gaussian mixtures model (GMM) and multiscale autoregressive (MAR)model. This algorithm is capable of improving the global optimality and consistency of the classification performance. The experiments on the SAR images show that the proposed algorithm outperforms the standard EM method significantly in classification accuracy.

  11. Genetic-Algorithm Tool For Search And Optimization

    Science.gov (United States)

    Wang, Lui; Bayer, Steven

    1995-01-01

    SPLICER computer program used to solve search and optimization problems. Genetic algorithms adaptive search procedures (i.e., problem-solving methods) based loosely on processes of natural selection and Darwinian "survival of fittest." Algorithms apply genetically inspired operators to populations of potential solutions in iterative fashion, creating new populations while searching for optimal or nearly optimal solution to problem at hand. Written in Think C.

  12. Building Blocks Propagation in Quantum-Inspired Genetic Algorithm

    OpenAIRE

    Nowotniak, Robert; Kucharski, Jacek

    2010-01-01

    This paper presents an analysis of building blocks propagation in Quantum-Inspired Genetic Algorithm, which belongs to a new class of metaheuristics drawing their inspiration from both biological evolution and unitary evolution of quantum systems. The expected number of quantum chromosomes matching a schema has been analyzed and a random variable corresponding to this issue has been introduced. The results have been compared with Simple Genetic Algorithm. Also, it has been presented how selec...

  13. SNMP Based Network Optimization Technique Using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    M. Mohamed Surputheen

    2012-03-01

    Full Text Available Genetic Algorithms (GAs has innumerable applications through the optimization techniques and network optimization is one of them. SNMP (Simple Network Management Protocol is used as the basic network protocol for monitoring the network activities health of the systems. This paper deals with adding Intelligence to the various aspects of SNMP by adding optimization techniques derived out of genetic algorithms, which enhances the performance of SNMP processes like routing.

  14. A novel genetic algorithm for k-LCS

    Science.gov (United States)

    Zheng, Li; Yang, Guoyu; Zhang, Rui

    2017-08-01

    A new fitness function model is designed, which considers that the outstanding sequence must be a common subsequence and the longer the better. However, the sequence which is not a common subsequence should be eliminated. Then, a novel genetic algorithm is proposed and described in details. Finally, the experimental results show that the new fitness function associated with the novel genetic algorithm can find out better solution.

  15. Optimal Design of Materials for DJMP Based on Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    FENG Zhong-ren; WANG Xiong-jiang

    2004-01-01

    The genetic algorithm was used in optimal design of deep jet method pile. The cost of deep jetmethod pile in one unit area of foundation was taken as the objective function. All the restrains were listed followingthe corresponding specification. Suggestions were proposed and the modified. The real-coded Genetic Algorithm wasgiven to deal with the problems of excessive computational cost and premature convergence. Software system of opti-mal design of deep jet method pile was developed.

  16. Parallel Genetic Algorithm Based on the MPI Environment

    OpenAIRE

    2012-01-01

    Current genetic algorithm require both management of huge amounts of data and heavy computation, fulfilling these requirements calls for simple ways to implement parallel computing. In this paper, serial genetic algorithm was designed to parallel GA; this technology appears to be particularly well adapted to this task. Here we introduce two related mechanism: elite reserve strategy and MPI. The first can increase the possible to get the optimal solution of the population, while the message pa...

  17. An Approach of Attribute Reduction Based on Quantum Genetic Algorithm and Rough Set%一种基于量子遗传算法与粗糙集理论的属性约简法

    Institute of Scientific and Technical Information of China (English)

    冯林

    2011-01-01

    提出一种基于粗糙集与量子遗传算法理论的属性约简模型.首先,基于粗糙集理论,以条件属性集对决策属性近似分类质量为准则,构造出一种衡量最佳属性子集的适应度函数.以此为基础,结合量子计算原理中量子旋转门调整策略以及量子交叉方法对种群进行更新操作,构造了该模型的属性约简方法.仿真实验结果表明了本文方法的有效性.%A model of attribute reduction based on rough set and quantum genetic algorithm is proposed .First, through calculating the approximation classification quality of the conditional attribute set to the decision attribute based on rough set theory, a fitness function for evaluating the optimal attribute subset is constructed. Combining both quantum rotation gate adjustment strategy and quantum cross method in quantum computing theory to update the population, an attribute reduction algorithm is proposed for the model. Simulation results illustrate the efficiency of the proposed approach.

  18. Neural Network Control Optimization based on Improved Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Zhaoyin Zhang

    2013-08-01

    Full Text Available To clearly find the effect of factors in network classification, the classification process of PNN is analyzed in detail. The XOR problem is described by PNN and the elements in PNN are also studied. Through simulations and combined with genetic algorithm, a novel PNN supervised learning algorithm is proposed. This algorithm introduces the classification accuracy of training samples to the network parameter learning. It adopts genetic algorithm to train the PNN smoothing parameter and hidden centric vector. Then the effects of hidden neuron number, hidden centric vector and smoothing parameter in PNN are verified in the experiments. It is shown that this algorithm is superior to other PNN learning algorithms on classification effect.

  19. Use of Genetic Algorithms for Contrast and Entropy Optimization in ISAR Autofocusing

    Directory of Open Access Journals (Sweden)

    Martorella Marco

    2006-01-01

    Full Text Available Image contrast maximization and entropy minimization are two commonly used techniques for ISAR image autofocusing. When the signal phase history due to the target radial motion has to be approximated with high order polynomial models, classic optimization techniques fail when attempting to either maximize the image contrast or minimize the image entropy. In this paper a solution of this problem is proposed by using genetic algorithms. The performances of the new algorithms that make use of genetic algorithms overcome the problem with previous implementations based on deterministic approaches. Tests on real data of airplanes and ships confirm the insight.

  20. EVOLVING RETRIEVAL ALGORITHMS WITH A GENETIC PROGRAMMING SCHEME

    Energy Technology Data Exchange (ETDEWEB)

    J. THEILER; ET AL

    1999-06-01

    The retrieval of scene properties (surface temperature, material type, vegetation health, etc.) from remotely sensed data is the ultimate goal of many earth observing satellites. The algorithms that have been developed for these retrievals are informed by physical models of how the raw data were generated. This includes models of radiation as emitted and/or rejected by the scene, propagated through the atmosphere, collected by the optics, detected by the sensor, and digitized by the electronics. To some extent, the retrieval is the inverse of this ''forward'' modeling problem. But in contrast to this forward modeling, the practical task of making inferences about the original scene usually requires some ad hoc assumptions, good physical intuition, and a healthy dose of trial and error. The standard MTI data processing pipeline will employ algorithms developed with this traditional approach. But we will discuss some preliminary research on the use of a genetic programming scheme to ''evolve'' retrieval algorithms. Such a scheme cannot compete with the physical intuition of a remote sensing scientist, but it may be able to automate some of the trial and error. In this scenario, a training set is used, which consists of multispectral image data and the associated ''ground truth;'' that is, a registered map of the desired retrieval quantity. The genetic programming scheme attempts to combine a core set of image processing primitives to produce an IDL (Interactive Data Language) program which estimates this retrieval quantity from the raw data.

  1. Efficient synthesis of large-scale thinned arrays using a density-taper initialised genetic algorithm

    CSIR Research Space (South Africa)

    Du Plessis, WP

    2011-09-01

    Full Text Available The use of the density-taper approach to initialise a genetic algorithm is shown to give excellent results in the synthesis of thinned arrays. This approach is shown to give better SLL values more consistently than using random values and difference...

  2. Optimization of meander line antennas for RFID applications by using genetic algorithm

    Science.gov (United States)

    Bucuci, Stefania C.; Anchidin, Liliana; Dumitrascu, Ana; Danisor, Alin; Berescu, Serban; Tamas, Razvan D.

    2015-02-01

    In this paper, we propose an approach of optimization of meander line antennas by using genetic algorithm. Such antennas are used in RFID applications. As opposed to other approaches for meander antennas, we propose the use of only two optimization objectives, i.e. gain and size. As an example, we have optimized a single meander dipole antenna, resonating at 869 MHz.

  3. Improved time complexity analysis of the Simple Genetic Algorithm

    DEFF Research Database (Denmark)

    Oliveto, Pietro S.; Witt, Carsten

    2015-01-01

    A runtime analysis of the Simple Genetic Algorithm (SGA) for the OneMax problem has recently been presented proving that the algorithm with population size μ≤n1/8−ε requires exponential time with overwhelming probability. This paper presents an improved analysis which overcomes some limitations...

  4. OPTIMIZING LOCALIZATION ROUTE USING PARTICLE SWARM-A GENETIC APPROACH

    Directory of Open Access Journals (Sweden)

    L. Lakshmanan

    2014-01-01

    Full Text Available One of the most key problems in wireless sensor networks is finding optimal algorithms for sending packets from source node to destination node. Several algorithms exist in literature, since some are in vital role other may not. Since WSN focus on low power consumption during packet transmission and receiving, finally we adopt by merging swarm particle based algorithm with genetic approach. Initially we order the nodes based on their energy criterion and then focusing towards node path; this can be done using Proactive route algorithm for finding optimal path between Source-Destination (S-D nodes. Fast processing and pre traversal can be done using selective flooding approach and results are in genetic. We have improved our results with high accuracy and optimality in rendering routes.

  5. Reverse Genetic Approaches in Zebrafish

    Institute of Scientific and Technical Information of China (English)

    Peng Huang; Zuoyan Zhu; Shuo Lin; Bo Zhang

    2012-01-01

    Zebrafish (Danio rerio) is a well-established vertebrate animal model.A comprehensive collection of reverse genetics tools has been developed for studying gene function in this useful organism.Morpholino is the most widely used reagent to knock down target gene expression post-transcriptionally.For a long time,targeted genome modification has been heavily relied on large-scale traditional forward genetic screens,such as ENU (N-ethyl-N-nitrosourea) mutagenesis derived TILLING (Targeting Induced Local Lesions IN Genomes)strategy and pseudo-typed retrovirus mediated insertional mutagenesis.Recently,engineered endonucleases,including ZFNs (zinc finger nucleases) and TALENs (transcription activator-like effector nucleases),provide new and efficient strategies to directly generate sitespecific indel mutations by inducing double strand breaks in target genes.Here we summarize the major reverse genetic approaches for loss-of-function studies used and emerging in zebrafish,including strategies based on genome-wide mutagenesis and methods for sitespecific gene targeting.Future directions and expectations will also be discussed.

  6. An Intelligent Model for Pairs Trading Using Genetic Algorithms.

    Science.gov (United States)

    Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An

    2015-01-01

    Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.

  7. Gravitational Lens Modeling with Genetic Algorithms and Particle Swarm Optimizers

    CERN Document Server

    Rogers, Adam

    2011-01-01

    Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automa...

  8. Some Studies on Forming Optimization with Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Ganesh Marotrao KAKANDIKAR

    2012-07-01

    Full Text Available Forming is a compression-tension process involving wide spectrum of operations andflow conditions. The result of the process depends on the large number of parameters and theirinterdependence. The selection of various parameters is still based on trial and error methods. In thispaper the authors present a new approach to optimize the geometry parameters of circularcomponents, process parameters such as blank holder pressure and coefficient of friction etc. Theoptimization problem has been formulated with the objective of optimizing the maximum formingload required in Forming. Genetic algorithm is used as a tool for the optimization: to optimize thedrawing load and to optimize the process parameters. A finite element analysis simulation softwareFast Form Advanced is used for the validations of the results after optimization with prior results.

  9. An Intelligent Model for Pairs Trading Using Genetic Algorithms

    Science.gov (United States)

    Hsu, Chi-Jen; Chen, Chi-Chung; Li, Chen-An

    2015-01-01

    Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236

  10. Optimization of genomic selection training populations with a genetic algorithm

    Science.gov (United States)

    In this article, we derive a computationally efficient statistic to measure the reliability of estimates of genetic breeding values for a fixed set of genotypes based on a given training set of genotypes and phenotypes. We adopt a genetic algorithm scheme to find a training set of certain size from ...

  11. Multiple Query Evaluation Based on an Enhanced Genetic Algorithm.

    Science.gov (United States)

    Tamine, Lynda; Chrisment, Claude; Boughanem, Mohand

    2003-01-01

    Explains the use of genetic algorithms to combine results from multiple query evaluations to improve relevance in information retrieval. Discusses niching techniques, relevance feedback techniques, and evolution heuristics, and compares retrieval results obtained by both genetic multiple query evaluation and classical single query evaluation…

  12. Dynamic Uniform Scaling for Multiobjective Genetic Algorithms

    DEFF Research Database (Denmark)

    Pedersen, Gerulf; Goldberg, David E.

    2004-01-01

    Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front can......, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and proposed solutions will be implemented using the NSGA-II algorithm....

  13. The Applications of Genetic Algorithms in Medicine

    OpenAIRE

    Ali Ghaheri; Saeed Shoar; Mohammad Naderan; Sayed Shahabuddin Hoseini

    2015-01-01

    A great wealth of information is hidden amid medical research data that in some cases cannot be easily analyzed, if at all, using classical statistical methods. Inspired by nature, metaheuristic algorithms have been developed to offer optimal or near-optimal solutions to complex data analysis and decision-making tasks in a reasonable time. Due to their powerful features, metaheuristic algorithms have frequently been used in other fields of sciences. In medicine, however, the use of these algo...

  14. Dynamic Uniform Scaling for Multiobjective Genetic Algorithms

    DEFF Research Database (Denmark)

    Pedersen, Gerulf; Goldberg, D.E.

    2004-01-01

    Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front c......, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and proposed solutions will be implemented using the NSGA-II algorithm....

  15. A Genetic Algorithm for the Segmentation of Known Touching Objects

    Directory of Open Access Journals (Sweden)

    Edgar Scavino

    2009-01-01

    Full Text Available Problem statement: Segmentation is the first and fundamental step in the process of computer vision and object classification. However, complicate or similar colour pattern add complexity to the segmentation of touching objects. The objective of this study was to develop a robust technique for the automatic segmentation and classification of touching plastic bottles, whose features were previously stored in a database. Approach: Our technique was based on the possibility to separate the two objects by means of a segment of straight line, whose position was determined by a genetic approach. The initial population of the genetic algorithm was heuristically determined among a large set of cutting lines, while further generations were selected based on the likelihood of the two objects with the images stored in the database. Results: Extensive testing, which was performed on random couples out of a population of 50 bottles, showed that the correct segmentation could be achieved in success rates above 90% with only a limited number of both chromosomes and iterations, thus reducing the computing time. Conclusion: These findings proved the effectiveness of our method as far as touching plastic bottles are concerned. This technique, being absolutely general, can be extended to any situation in which the properties of single objects were previously stored in a database.

  16. Optimal Path Planning for Mobile Robot Using Tailored Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Dong Xiao Xian

    2013-07-01

    Full Text Available During routine inspecting, mobile robot may be requested to visit multiple locations to execute special tasks occasionally. This study aims at optimal path planning for multiple goals visiting task based on tailored genetic algorithm. The proposed algorithm will generate an optimal path that has the least idle time, which is proven to be more effective on evaluating a path in our previous work. In proposed algorithm, customized chromosome representing a path and genetic operators including repair and cut are developed and implemented. Afterwards, simulations are carried out to verify the effectiveness and applicability. Finally, analysis of simulation results is conducted and future work is addressed.

  17. Evolving aerodynamic airfoils for wind turbines through a genetic algorithm

    Science.gov (United States)

    Hernández, J. J.; Gómez, E.; Grageda, J. I.; Couder, C.; Solís, A.; Hanotel, C. L.; Ledesma, JI

    2017-01-01

    Nowadays, genetic algorithms stand out for airfoil optimisation, due to the virtues of mutation and crossing-over techniques. In this work we propose a genetic algorithm with arithmetic crossover rules. The optimisation criteria are taken to be the maximisation of both aerodynamic efficiency and lift coefficient, while minimising drag coefficient. Such algorithm shows greatly improvements in computational costs, as well as a high performance by obtaining optimised airfoils for Mexico City's specific wind conditions from generic wind turbines designed for higher Reynolds numbers, in few iterations.

  18. Parallel Genetic Algorithm Based on the MPI Environment

    Directory of Open Access Journals (Sweden)

    Wen-Juan Liu

    2012-11-01

    Full Text Available Current genetic algorithm require both management of huge amounts of data and heavy computation, fulfilling these requirements calls for simple ways to implement parallel computing. In this paper, serial genetic algorithm was designed to parallel GA; this technology appears to be particularly well adapted to this task. Here we introduce two related mechanism: elite reserve strategy and MPI. The first can increase the possible to get the optimal solution of the population, while the message passing interface MPI support is adding to form a new coarse-grain model of distributed parallel genetic algorithm. This new algorithm is tested by the classical and effective Knapsack problem, analysis shows that, the introduction of the parallel strategies can reduce the communication between different machines and the scheduling time of the heterogeneous system, thereby accelerate the traditional genetic algorithm search process, ultimately concluded that the parallel genetic algorithm is very promising and this framework could have a wide range of applications while maintaining good computational efficiency, scalability and ease of maintenance.

  19. Solving Classification Problems Using Genetic Programming Algorithms on GPUs

    Science.gov (United States)

    Cano, Alberto; Zafra, Amelia; Ventura, Sebastián

    Genetic Programming is very efficient in problem solving compared to other proposals but its performance is very slow when the size of the data increases. This paper proposes a model for multi-threaded Genetic Programming classification evaluation using a NVIDIA CUDA GPUs programming model to parallelize the evaluation phase and reduce computational time. Three different well-known Genetic Programming classification algorithms are evaluated using the parallel evaluation model proposed. Experimental results using UCI Machine Learning data sets compare the performance of the three classification algorithms in single and multithreaded Java, C and CUDA GPU code. Results show that our proposal is much more efficient.

  20. Coarse-Grained Parallel Genetic Algorithm to solve the Shortest Path Routing problem using Genetic operators

    Directory of Open Access Journals (Sweden)

    V.PURUSHOTHAM REDDY

    2011-02-01

    Full Text Available In computer networks the routing is based on shortest path routing algorithms. Based on its advantages, an alternative method is used known as Genetic Algorithm based routing algorithm, which is highly scalable and insensitive to variations in network topology. Here we propose a coarse-grained parallel genetic algorithm to solve the shortest path routing problem with the primary goal of computation time reduction along with the use of migration scheme. This algorithm is developed and implemented on an MPI cluster. The effects of migration and its performance is studied in this paper.

  1. Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

    Science.gov (United States)

    Chen, C.; Xia, J.; Liu, J.; Feng, G.

    2006-01-01

    Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant

  2. Novel hybrid genetic algorithm for progressive multiple sequence alignment.

    Science.gov (United States)

    Afridi, Muhammad Ishaq

    2013-01-01

    The family of evolutionary or genetic algorithms is used in various fields of bioinformatics. Genetic algorithms (GAs) can be used for simultaneous comparison of a large pool of DNA or protein sequences. This article explains how the GA is used in combination with other methods like the progressive multiple sequence alignment strategy to get an optimal multiple sequence alignment (MSA). Optimal MSA get much importance in the field of bioinformatics and some other related disciplines. Evolutionary algorithms evolve and improve their performance. In this optimisation, the initial pair-wise alignment is achieved through a progressive method and then a good objective function is used to select and align more alignments and profiles. Child and subpopulation initialisation is based upon changes in the probability of similarity or the distance matrix of the alignment population. In this genetic algorithm, optimisation of mutation, crossover and migration in the population of candidate solution reflect events of natural organic evolution.

  3. Optimization of a genetic algorithm for searching molecular conformer space

    Science.gov (United States)

    Brain, Zoe E.; Addicoat, Matthew A.

    2011-11-01

    We present two sets of tunings that are broadly applicable to conformer searches of isolated molecules using a genetic algorithm (GA). In order to find the most efficient tunings for the GA, a second GA - a meta-genetic algorithm - was used to tune the first genetic algorithm to reliably find the already known a priori correct answer with minimum computational resources. It is shown that these tunings are appropriate for a variety of molecules with different characteristics, and most importantly that the tunings are independent of the underlying model chemistry but that the tunings for rigid and relaxed surfaces differ slightly. It is shown that for the problem of molecular conformational search, the most efficient GA actually reduces to an evolutionary algorithm.

  4. Stochastic analysis and convergence velocity estimation of genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    郭观七; 喻寿益

    2003-01-01

    Formulizations of mutation and crossover operators independent of representation of solutions are proposed. A kind of precisely quantitative Markov chain of populations of standard genetic algorithms is modeled. It is proved that inadequate parameters of mutation and crossover probabilities degenerate standard genetic algorithm to a class of random search algorithms without selection bias toward any solution based on fitness. After introducing elitist reservation, the stochastic matrix of Markov chain of the best-so-far individual with the highest fitness is derived.The average convergence velocity of genetic algorithms is defined as the mathematical expectation of the mean absorbing time steps that the best-so-far individual transfers from any initial solution to the global optimum. Using the stochastic matrix of the best-so-far individual, a theoretic method and the computing process of estimating the average convergence velocity are proposed.

  5. Solving the MDBCS Problem Using the Metaheuric–Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Milena Bogdanovic

    2011-12-01

    Full Text Available The problems degree-limited graph of nodes considering the weight of the vertex or weight of the edges, with the aim to find the optimal weighted graph in terms of certain restrictions on the degree of the vertices in the subgraph. This class of combinatorial problems was extensively studied because of the implementation and application in network design, connection of networks and routing algorithms. It is likely that solution of MDBCS problem will find its place and application in these areas. The paper is given an ILP model to solve the problem MDBCS, as well as the genetic algorithm, which calculates a good enough solution for the input graph with a greater number of nodes. An important feature of the heuristic algorithms is that can approximate, but still good enough to solve the problems of exponential complexity. However, it should solve the problem heuristic algorithms may not lead to a satisfactory solution, and that for some of the problems, heuristic algorithms give relatively poor results. This is particularly true of problems for which no exact polynomial algorithm complexity. Also, heuristic algorithms are not the same, because some parts of heuristic algorithms differ depending on the situation and problems in which they are used. These parts are usually the objective function (transformation, and their definition significantly affects the efficiency of the algorithm. By mode of action, genetic algorithms are among the methods directed random search space solutions are looking for a global optimum.

  6. Composite multiobjective optimization beamforming based on genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Shi Jing; Meng Weixiao; Zhang Naitong; Wang Zheng

    2006-01-01

    All thc parameters of beamforming are usually optimized simultaneously in implementing the optimization of antenna array pattern with multiple objectives and parameters by genetic algorithms (GAs).Firstly, this paper analyzes the performance of fitness functions of previous algorithms. It shows that original algorithms make the fitness functions too complex leading to large amount of calculation, and also the selection of the weight of parameters very sensitive due to many parameters optimized simultaneously. This paper proposes a kind of algorithm of composite beamforming, which detaches the antenna array into two parts corresponding to optimization of different objective parameters respectively. New algorithm substitutes the previous complex fitness function with two simpler functions. Both theoretical analysis and simulation results show that this method simplifies the selection of weighting parameters and reduces the complexity of calculation. Furthermore, the algorithm has better performance in lowering side lobe and interferences in comparison with conventional algorithms of beamforming in the case of slightly widening the main lobe.

  7. Solving traveling salesman problems by genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    The gene section ordering on solving traveling salesman problems is analyzed by numerical experiments. Some improved crossover operations are presented. Several combinations of genetic operations are examined and the functions of these operations are analyzed. The essentiality of the ordering of the gene section and the significance of the evolutionary inversion operation are discussed. Some results and conclusions are obtained and given, which provide useful information for the implementation of the genetic operations for solving the traveling salesman problem.

  8. Forward and backward models for fault diagnosis based on parallel genetic algorithms

    Institute of Scientific and Technical Information of China (English)

    Yi LIU; Ying LI; Yi-jia CAO; Chuang-xin GUO

    2008-01-01

    In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.

  9. A Genetic Clustering Algorithm for Mean-Residual Vector Quantization

    Institute of Scientific and Technical Information of China (English)

    CHUShuchuan; JohnF.Roddick; CHENTsongyi

    2004-01-01

    Vector quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Meanresidual vector quantization (M/R VQ) has been shown to be efficient in the encoding time and it only needs a little storage. In this paper, genetic algorithms in combination with the Generalized lloyd algorithm (GLA) are applied to the codebook design of M/R VQ. The mean codebook and residual codebook are trained using GLA algorithm separately, then Genetic algorithms (GA) are used to evaluate and evolve the combined mean codebook and residual codebook. The parameters used in the proposed algorithm are designed based on experiments and they are robust to the proposed GA based clustering algorithm for M/R VQ. Experimental results demonstrate the proposed genetic clustering algorithm applied to M/R VQ may improve the peak signal to noise ratio of the recovered data vector compared with the GLA algorithm.

  10. An Agent Inspired Reconfigurable Computing Implementation of a Genetic Algorithm

    Science.gov (United States)

    Weir, John M.; Wells, B. Earl

    2003-01-01

    Many software systems have been successfully implemented using an agent paradigm which employs a number of independent entities that communicate with one another to achieve a common goal. The distributed nature of such a paradigm makes it an excellent candidate for use in high speed reconfigurable computing hardware environments such as those present in modem FPGA's. In this paper, a distributed genetic algorithm that can be applied to the agent based reconfigurable hardware model is introduced. The effectiveness of this new algorithm is evaluated by comparing the quality of the solutions found by the new algorithm with those found by traditional genetic algorithms. The performance of a reconfigurable hardware implementation of the new algorithm on an FPGA is compared to traditional single processor implementations.

  11. Weighted K-Nearest Neighbor Classification Algorithm Based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Xuesong Yan

    2013-10-01

    Full Text Available K-Nearest Neighbor (KNN is one of the most popular algorithms for data classification. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different datasets. The traditional KNN text classification algorithm has limitations: calculation complexity, the performance is solely dependent on the training set, and so on. To overcome these limitations, an improved version of KNN is proposed in this paper, we use genetic algorithm combined with weighted KNN to improve its classification performance. and the experiment results shown that our proposed algorithm outperforms the KNN with greater accuracy.

  12. Quantum Algorithms and the Genetic Code

    CERN Document Server

    Patel, A D

    2001-01-01

    Replication of DNA and synthesis of proteins are studied from the view-pointof quantum database search. Identification of a base-pairing with a quantumquery gives a natural (and first ever!) explanation of why living organismshave 4 nucleotide bases and 20 amino acids. It is amazing that these numbersarise as solutions to an optimisation problem. Components of the DNA structurewhich implement Grover's algorithm are identified, and a physical scenario ispresented for the execution of the quantum algorithm. It is proposed thatenzymes play a crucial role in maintaining quantum coherence of the process.Experimental tests that can verify this scenario are pointed out.

  13. An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

    Directory of Open Access Journals (Sweden)

    Yunqing Rao

    2013-01-01

    Full Text Available For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

  14. Optimization of heterogeneous Bin packing using adaptive genetic algorithm

    Science.gov (United States)

    Sridhar, R.; Chandrasekaran, M.; Sriramya, C.; Page, Tom

    2017-03-01

    This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach. The optimal and feasible packing of goods for transportation and distribution to various locations by satisfying the practical constraints are the key points in this project work. As the number of boxes for packing can not be predicted in advance and the boxes may not be of same category always. It also involves many practical constraints that are why the optimal packing makes much importance to the industries. This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering most of the practical constraints facing in logistic industries. This goal was achieved in this research by optimizing the empty volume inside the container using genetic approach. Feasible packing pattern was achieved by satisfying various practical constraints like box orientation, stack priority, container stability, weight constraint, overlapping constraint, shipment placement constraint. 3D bin packing problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and in-turn profit. Furthermore, Boxes to be packed may be of arbitrary sizes. The user input data are the number of bins, its size, shape, weight, and constraints if any along with standard container dimension. This user input were stored in the database and encoded to string (chromosomes) format which were normally acceptable by GA. GA operators were allowed to act over these encoded strings for finding the best solution.

  15. An Approach of Degree Constraint MST Algorithm

    Directory of Open Access Journals (Sweden)

    Sanjay Kumar Pal

    2013-08-01

    Full Text Available This paper is approaching a new technique of creating Minimal Spanning Trees based on degree constraints of a simple symmetric and connected graph G. Here we recommend a new algorithm based on the average degree sequence factor of the nodes in the graph. The time complexity of the problem is less than O(N log|E| compared to the other existing time complexity algorithms is O(|E| log|E|+C of Kruskal, which is optimum. The goal is to design an algorithm that is simple, graceful, resourceful, easy to understand, and applicable in various fields starting from constraint based network design, mobile computing to other field of science and engineering.

  16. A modified decision tree algorithm based on genetic algorithm for mobile user classification problem.

    Science.gov (United States)

    Liu, Dong-sheng; Fan, Shu-jiang

    2014-01-01

    In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity.

  17. An Evaluation of Potentials of Genetic Algorithm in Shortest Path Problem

    Science.gov (United States)

    Hassany Pazooky, S.; Rahmatollahi Namin, Sh; Soleymani, A.; Samadzadegan, F.

    2009-04-01

    One of the most typical issues considered in combinatorial systems in transportation networks, is the shortest path problem. In such networks, routing has a significant impact on the network's performance. Due to natural complexity in transportation networks and strong impact of routing in different fields of decision making, such as traffic management and vehicle routing problem (VRP), appropriate solutions to solve this problem are crucial to be determined. During last years, in order to solve the shortest path problem, different solutions are proposed. These techniques are divided into two categories of classic and evolutionary approaches. Two well-known classic algorithms are Dijkstra and A*. Dijkstra is known as a robust, but time consuming algorithm in finding the shortest path problem. A* is also another algorithm very similar to Dijkstra, less robust but with a higher performance. On the other hand, Genetic algorithms are introduced as most applicable evolutionary algorithms. Genetic Algorithm uses a parallel search method in several parts of the domain and is not trapped in local optimums. In this paper, the potentiality of Genetic algorithm for finding the shortest path is evaluated by making a comparison between this algorithm and classic algorithms (Dijkstra and A*). Evaluation of the potential of these techniques on a transportation network in an urban area shows that due to the problem of classic methods in their small search space, GA had a better performance in finding the shortest path.

  18. Design Optimization of Tilting-Pad Journal Bearing Using a Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Hamit Saruhan

    2004-01-01

    Full Text Available This article focuses on the use of genetic algorithms in developing an efficient optimum design method for tilting pad bearings. The approach optimizes based on minimum film thickness, power loss, maximum film temperature, and a global objective. Results for a five tilting-pad preloaded bearing are presented to provide a comparison with more traditional optimum design methods such as the gradient-based global criterion method, and also to provide insight into the potential of genetic algorithms in the design of rotor bearings. Genetic algorithms are efficient search techniques based on the idea of natural selection and genetics. These robust methods have gained recognition as general problem solving techniques in many applications.

  19. Genetic Algorithms, Neural Networks, and Time Effectiveness Algorithm Based Air Combat Intelligence Simulation System

    Institute of Scientific and Technical Information of China (English)

    曾宪钊; 成冀; 安欣; 方礼明

    2002-01-01

    This paper introduces a new Air Combat Intelligence Simulation System (ACISS) in a 32 versus 32 air combat, describes three methods: Genetic Algorithms (GA) in the multi-targeting decision and Evading Missile Rule Base learning, Neural Networks (NN) in the maneuvering decision, and Time Effectiveness Algorithm (TEA) in the adjudicating an air combat and the evaluating evading missile effectiveness.

  20. Application of Modified Genetic Algorithm to Optimal Design of Supporting Structure

    Institute of Scientific and Technical Information of China (English)

    ZHOU Rui-zhong; PAN Shi-wei

    2003-01-01

    The modified genetic algorithm was used for the optimal design of supporting structure in deep pits.Based on the common genetic algorithm, using niche technique and reserving the optimum individual the modified genetic algorithm was presented. By means of the practical engineering, the modified genetic algorithm not only has more expedient convergence, but also can enhance security and operation efficiency.