Genetic Algorithms

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José Luis Verdegay - One of the best experts on this subject based on the ideXlab platform.

  • Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
    Artificial Intelligence Review, 1998
    Co-Authors: Francisco Herrera, M Lozano, José Luis Verdegay
    Abstract:

    Genetic Algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic Algorithms are based on the underlying Genetic process in biological organisms and on the naturalevolution principles of populations. These Algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation.Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these Algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded Genetic Algorithms. Different models of Genetic operators and some mechanisms available for studying the behaviour of this type of Genetic Algorithms are revised and compared.

Sam Kwong - One of the best experts on this subject based on the ideXlab platform.

  • Genetic Algorithms concepts and designs
    2001
    Co-Authors: K. S. Tang, Sam Kwong
    Abstract:

    1. Introduction, Background and Biological Inspiration.- 1.1 Biological Background.- 1.1.1 Coding of DNA.- 1.1.2 Flow of Genetic Information.- 1.1.3 Recombination.- 1.1.4 Mutation.- 1.2 Conventional Genetic Algorithm.- 1.3 Theory and Hypothesis.- 1.3.1 Schema Theory.- 1.3.2 Building Block Hypothesis.- 1.4 A Simple Example.- 2. Modifications to Genetic Algorithms.- 2.1 Chromosome Representation.- 2.2 Objective and Fitness Functions.- 2.2.1 Linear Scaling.- 2.2.2 Sigma Truncation.- 2.2.3 Power Law Scaling.- 2.2.4 Ranking.- 2.3 Selection Methods.- 2.4 Genetic Operations.- 2.4.1 Crossover.- 2.4.2 Mutation.- 2.4.3 Operational Rates Settings.- 2.4.4 Reordering.- 2.5 Replacement Scheme.- 2.6 A Game of Genetic Creatures.- 2.7 Chromosome Representation.- 2.8 Fitness Function.- 2.9 Genetic Operation.- 2.9.1 Selection Window for Functions and Parameters.- 2.10 Demo and Run.- 3. Intrinsic Characteristics.- 3.1 Parallel Genetic Algorithm.- 3.1.1 Global GA.- 3.1.2 Migration GA.- 3.1.3 Diffusion GA.- 3.2 Multiple Objective.- 3.3 Robustness.- 3.4 Multimodal.- 3.5 Constraints.- 3.5.1 Searching Domain.- 3.5.2 Repair Mechanism.- 3.5.3 Penalty Scheme.- 3.5.4 Specialized Genetic Operations.- 4. Hierarchical Genetic Algorithm.- 4.1 Biological Inspiration.- 4.1.1 Regulatory Sequences and Structural Genes.- 4.1.2 Active and Inactive Genes.- 4.2 Hierarchical Chromosome Formulation.- 4.3 Genetic Operations.- 4.4 Multiple Objective Approach.- 4.4.1 Iterative Approach.- 4.4.2 Group Technique.- 4.4.3 Multiple-Objective Ranking.- 5. Genetic Algorithms in Filtering.- 5.1 Digital IIR Filter Design.- 5.1.1 Chromosome Coding.- 5.1.2 The Lowest Filter Order Criterion.- 5.2 Time Delay Estimation.- 5.2.1 Problem Formulation.- 5.2.2 Genetic Approach.- 5.2.3 Results.- 5.3 Active Noise Control.- 5.3.1 Problem Formulation.- 5.3.2 Simple Genetic Algorithm.- 5.3.3 Multiobjective Genetic Algorithm Approach.- 5.3.4 Parallel Genetic Algorithm Approach.- 5.3.5 Hardware GA Processor.- 6. Genetic Algorithms in H-infinity Control.- 6.1 A Mixed Optimization Design Approach.- 6.1.1 Hierarchical Genetic Algorithm.- 6.1.2 Application I: The Distillation Column Design.- 6.1.3 Application II: Benchmark Problem.- 6.1.4 Design Comments.- 7. Hierarchical Genetic Algorithms in Computational Intelligence.- 7.1 Neural Networks.- 7.1.1 Introduction of Neural Network.- 7.1.2 HGA Trained Neural Network (HGANN).- 7.1.3 Simulation Results.- 7.1.4 Application of HGANN on Classification.- 7.2 Fuzzy Logic.- 7.2.1 Basic Formulation of Fuzzy Logic Controller.- 7.2.2 Hierarchical Structure.- 7.2.3 Application I: Water Pump System.- 7.2.4 Application II: Solar Plant.- 8. Genetic Algorithms in Speech Recognition Systems.- 8.1 Background of Speech Recognition Systems.- 8.2 Block Diagram of a Speech Recognition System.- 8.3 Dynamic Time Warping.- 8.4 Genetic Time Warping Algorithm (GTW).- 8.4.1 Encoding mechanism.- 8.4.2 Fitness function.- 8.4.3 Selection.- 8.4.4 Crossover.- 8.4.5 Mutation.- 8.4.6 Genetic Time Warping with Relaxed Slope Weighting Function (GTW-RSW).- 8.4.7 Hybrid Genetic Algorithm.- 8.4.8 Performance Evaluation.- 8.5 Hidden Markov Model using Genetic Algorithms.- 8.5.1 Hidden Markov Model.- 8.5.2 Training Discrete HMMs using Genetic Algorithms.- 8.5.3 Genetic Algorithm for Continuous HMM Training.- 8.6 A Multiprocessor System for Parallel Genetic Algorithms.- 8.6.1 Implementation.- 8.7 Global GA for Parallel GA-DTW and PGA-HMM.- 8.7.1 Experimental Results of Nonlinear Time-Normalization by the Parallel GA-DTW.- 8.8 Summary.- 9. Genetic Algorithms in Production Planning and Scheduling Problems.- 9.1 Background of Manufacturing Systems.- 9.2 ETPSP Scheme.- 9.2.1 ETPSP Model.- 9.2.2 Bottleneck Analysis.- 9.2.3 Selection of Key-Processes.- 9.3 Chromosome Configuration.- 9.3.1 Operational Parameters for GA Cycles.- 9.4 GA Application for ETPSP.- 9.4.1 Case 1: Two-product ETPSP.- 9.4.2 Case 2: Multi-product ETPSP.- 9.4.3 Case 3: MOGA Approach.- 9.5 Concluding Remarks.- 10. Genetic Algorithms in Communication Systems.- 10.1 Virtual Path Design in ATM.- 10.1.1 Problem Formulation.- 10.1.2 Average packet delay.- 10.1.3 Constraints.- 10.1.4 Combination Approach.- 10.1.5 Implementation.- 10.1.6 Results.- 10.2 Mesh Communication Network Design.- 10.2.1 Design of Mesh Communication Networks.- 10.2.2 Network Optimization using GA.- 10.2.3 Implementation.- 10.2.4 Results.- 10.3 Wireles Local Area Network Design.- 10.3.1 Problem Formulation.- 10.3.2 Multiobjective HGA Approach.- 10.3.3 Implementation.- 10.3.4 Results.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- Appendix E.- Appendix F.- References.

  • Genetic Algorithms: Concepts and applications
    IEEE Transactions on Industrial Electronics, 1996
    Co-Authors: K. F. Man, K. S. Tang, Sam Kwong
    Abstract:

    This paper introduces Genetic Algorithms (GA) as a complete entity, in which knowledge of this emerging technology can be integrated together to form the framework of a design tool for industrial engineers. An attempt has also been made to explain “why’’ and “when” GA should be used as an optimization tool.

Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.

  • Hierarchical distributed Genetic Algorithms
    International Journal of Intelligent Systems, 1999
    Co-Authors: Francisco Herrera, Manuel Lozano, Claudio Moraga
    Abstract:

    Genetic algorithm behavior is determined by the explorationrexploitation balance kept throughout the run. When this balance is disproportionate, the premature convergence problem will probably appear, causing a drop in the Genetic algorithm’s efficacy. One approach presented for dealing with this problem is the distributed Genetic algorithm model. Its basic idea is to keep, in parallel, several subpopulations that are processed by Genetic Algorithms, with each one being independent from the others. Furthermore, a migration operator produces a chromosome exchange between the subpopulations. Making distinctions between the subpopulations of a distributed Genetic algorithm by applying Genetic Algorithms with different configurations, we obtain the so-called heterogeneous distributed Genetic Algorithms. In this paper, we present a hierarchical model of distributed Genetic Algorithms in which a higher level distributed Genetic algorithm joins different simple distributed Genetic Algorithms. Furthermore, with the union of the hierarchical structure presented and the idea of the heterogeneous distributed Genetic Algorithms, we propose a type of heterogeneous hierarchical distributed Genetic Algorithms, the hierarchical gradual distributed Genetic Algorithms. Experimental results show that the proposals consistently outperform equivalent sequential Genetic Algorithms and simple distributed Genetic Algorithms. Q 1999 John Wiley & Sons, Inc.

  • Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
    Artificial Intelligence Review, 1998
    Co-Authors: Francisco Herrera, M Lozano, José Luis Verdegay
    Abstract:

    Genetic Algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic Algorithms are based on the underlying Genetic process in biological organisms and on the naturalevolution principles of populations. These Algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation.Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these Algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded Genetic Algorithms. Different models of Genetic operators and some mechanisms available for studying the behaviour of this type of Genetic Algorithms are revised and compared.

M Lozano - One of the best experts on this subject based on the ideXlab platform.

  • Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
    Artificial Intelligence Review, 1998
    Co-Authors: Francisco Herrera, M Lozano, José Luis Verdegay
    Abstract:

    Genetic Algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic Algorithms are based on the underlying Genetic process in biological organisms and on the naturalevolution principles of populations. These Algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation.Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these Algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded Genetic Algorithms. Different models of Genetic operators and some mechanisms available for studying the behaviour of this type of Genetic Algorithms are revised and compared.

K. S. Tang - One of the best experts on this subject based on the ideXlab platform.

  • Genetic Algorithms concepts and designs
    2001
    Co-Authors: K. S. Tang, Sam Kwong
    Abstract:

    1. Introduction, Background and Biological Inspiration.- 1.1 Biological Background.- 1.1.1 Coding of DNA.- 1.1.2 Flow of Genetic Information.- 1.1.3 Recombination.- 1.1.4 Mutation.- 1.2 Conventional Genetic Algorithm.- 1.3 Theory and Hypothesis.- 1.3.1 Schema Theory.- 1.3.2 Building Block Hypothesis.- 1.4 A Simple Example.- 2. Modifications to Genetic Algorithms.- 2.1 Chromosome Representation.- 2.2 Objective and Fitness Functions.- 2.2.1 Linear Scaling.- 2.2.2 Sigma Truncation.- 2.2.3 Power Law Scaling.- 2.2.4 Ranking.- 2.3 Selection Methods.- 2.4 Genetic Operations.- 2.4.1 Crossover.- 2.4.2 Mutation.- 2.4.3 Operational Rates Settings.- 2.4.4 Reordering.- 2.5 Replacement Scheme.- 2.6 A Game of Genetic Creatures.- 2.7 Chromosome Representation.- 2.8 Fitness Function.- 2.9 Genetic Operation.- 2.9.1 Selection Window for Functions and Parameters.- 2.10 Demo and Run.- 3. Intrinsic Characteristics.- 3.1 Parallel Genetic Algorithm.- 3.1.1 Global GA.- 3.1.2 Migration GA.- 3.1.3 Diffusion GA.- 3.2 Multiple Objective.- 3.3 Robustness.- 3.4 Multimodal.- 3.5 Constraints.- 3.5.1 Searching Domain.- 3.5.2 Repair Mechanism.- 3.5.3 Penalty Scheme.- 3.5.4 Specialized Genetic Operations.- 4. Hierarchical Genetic Algorithm.- 4.1 Biological Inspiration.- 4.1.1 Regulatory Sequences and Structural Genes.- 4.1.2 Active and Inactive Genes.- 4.2 Hierarchical Chromosome Formulation.- 4.3 Genetic Operations.- 4.4 Multiple Objective Approach.- 4.4.1 Iterative Approach.- 4.4.2 Group Technique.- 4.4.3 Multiple-Objective Ranking.- 5. Genetic Algorithms in Filtering.- 5.1 Digital IIR Filter Design.- 5.1.1 Chromosome Coding.- 5.1.2 The Lowest Filter Order Criterion.- 5.2 Time Delay Estimation.- 5.2.1 Problem Formulation.- 5.2.2 Genetic Approach.- 5.2.3 Results.- 5.3 Active Noise Control.- 5.3.1 Problem Formulation.- 5.3.2 Simple Genetic Algorithm.- 5.3.3 Multiobjective Genetic Algorithm Approach.- 5.3.4 Parallel Genetic Algorithm Approach.- 5.3.5 Hardware GA Processor.- 6. Genetic Algorithms in H-infinity Control.- 6.1 A Mixed Optimization Design Approach.- 6.1.1 Hierarchical Genetic Algorithm.- 6.1.2 Application I: The Distillation Column Design.- 6.1.3 Application II: Benchmark Problem.- 6.1.4 Design Comments.- 7. Hierarchical Genetic Algorithms in Computational Intelligence.- 7.1 Neural Networks.- 7.1.1 Introduction of Neural Network.- 7.1.2 HGA Trained Neural Network (HGANN).- 7.1.3 Simulation Results.- 7.1.4 Application of HGANN on Classification.- 7.2 Fuzzy Logic.- 7.2.1 Basic Formulation of Fuzzy Logic Controller.- 7.2.2 Hierarchical Structure.- 7.2.3 Application I: Water Pump System.- 7.2.4 Application II: Solar Plant.- 8. Genetic Algorithms in Speech Recognition Systems.- 8.1 Background of Speech Recognition Systems.- 8.2 Block Diagram of a Speech Recognition System.- 8.3 Dynamic Time Warping.- 8.4 Genetic Time Warping Algorithm (GTW).- 8.4.1 Encoding mechanism.- 8.4.2 Fitness function.- 8.4.3 Selection.- 8.4.4 Crossover.- 8.4.5 Mutation.- 8.4.6 Genetic Time Warping with Relaxed Slope Weighting Function (GTW-RSW).- 8.4.7 Hybrid Genetic Algorithm.- 8.4.8 Performance Evaluation.- 8.5 Hidden Markov Model using Genetic Algorithms.- 8.5.1 Hidden Markov Model.- 8.5.2 Training Discrete HMMs using Genetic Algorithms.- 8.5.3 Genetic Algorithm for Continuous HMM Training.- 8.6 A Multiprocessor System for Parallel Genetic Algorithms.- 8.6.1 Implementation.- 8.7 Global GA for Parallel GA-DTW and PGA-HMM.- 8.7.1 Experimental Results of Nonlinear Time-Normalization by the Parallel GA-DTW.- 8.8 Summary.- 9. Genetic Algorithms in Production Planning and Scheduling Problems.- 9.1 Background of Manufacturing Systems.- 9.2 ETPSP Scheme.- 9.2.1 ETPSP Model.- 9.2.2 Bottleneck Analysis.- 9.2.3 Selection of Key-Processes.- 9.3 Chromosome Configuration.- 9.3.1 Operational Parameters for GA Cycles.- 9.4 GA Application for ETPSP.- 9.4.1 Case 1: Two-product ETPSP.- 9.4.2 Case 2: Multi-product ETPSP.- 9.4.3 Case 3: MOGA Approach.- 9.5 Concluding Remarks.- 10. Genetic Algorithms in Communication Systems.- 10.1 Virtual Path Design in ATM.- 10.1.1 Problem Formulation.- 10.1.2 Average packet delay.- 10.1.3 Constraints.- 10.1.4 Combination Approach.- 10.1.5 Implementation.- 10.1.6 Results.- 10.2 Mesh Communication Network Design.- 10.2.1 Design of Mesh Communication Networks.- 10.2.2 Network Optimization using GA.- 10.2.3 Implementation.- 10.2.4 Results.- 10.3 Wireles Local Area Network Design.- 10.3.1 Problem Formulation.- 10.3.2 Multiobjective HGA Approach.- 10.3.3 Implementation.- 10.3.4 Results.- Appendix A.- Appendix B.- Appendix C.- Appendix D.- Appendix E.- Appendix F.- References.

  • Genetic Algorithms: Concepts and applications
    IEEE Transactions on Industrial Electronics, 1996
    Co-Authors: K. F. Man, K. S. Tang, Sam Kwong
    Abstract:

    This paper introduces Genetic Algorithms (GA) as a complete entity, in which knowledge of this emerging technology can be integrated together to form the framework of a design tool for industrial engineers. An attempt has also been made to explain “why’’ and “when” GA should be used as an optimization tool.

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