This book addresses the need for a principled approach to understanding the foundations of genetic algorithms and classifer systems as a way of enhancing their further development and application. Each paper presents original research, and most are accessible to anyone with general training in computer science or mathematics.
This book addresses the need for a principled approach to understanding the foundations of genetic algorithms and classifer systems as a way of enhancing their further development and application. Each paper presents original research, and most are accessible to anyone with general training in computer science or mathematics.
Part 1: Genetic Algorithm Hardness The Nonuniform Walsh-Schema Transform Epistasis Variance: A Viewpoint on GA-Hardness Deceptiveness and Genetic Algorithm Dynamics Part 2: Selection and Convergence An Extension to the Theory of Convergence and a Proof of the Time Complexity of Genetic Algorithms A Comparative Analysis of Selection Schemes Used in Genetic Algorithms A Study of Reproduction in Generational and Steady State Genetic Algorithms Spurious Correlations and Premature Convergence in Genetic Algorithms Part 3: Classifier Systems Representing Attribute-Based Concepts in a Classifier System Quasimorphisms or Queasymorphisms? Modeling Finite Automaton Environments Variable Default Hierarchy Separation in a Classifier System Part 4: Coding and Representation A Hierarchical Approach to Learning the Boolean Multiplexer Function A Grammar-Based Genetic Algorithm Genetic Algorithms for Real Parameter Optimization Part 5: Framework Issues Fundamental Principles of Deception in Genetic Search Isomorphisms of Genetic Algorithms Conditions for Implicit Parallelism Part 6: Variation and Recombination The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination Genetic Operators for Sequencing Problems An Analysis of Multi-Point Crossover Evolution in Time and Space-The Parallel Genetic Algorithm Author Index Key Word Index
Part 1: Genetic Algorithm Hardness The Nonuniform Walsh-Schema Transform Epistasis Variance: A Viewpoint on GA-Hardness Deceptiveness and Genetic Algorithm Dynamics Part 2: Selection and Convergence An Extension to the Theory of Convergence and a Proof of the Time Complexity of Genetic Algorithms A Comparative Analysis of Selection Schemes Used in Genetic Algorithms A Study of Reproduction in Generational and Steady State Genetic Algorithms Spurious Correlations and Premature Convergence in Genetic Algorithms Part 3: Classifier Systems Representing Attribute-Based Concepts in a Classifier System Quasimorphisms or Queasymorphisms? Modeling Finite Automaton Environments Variable Default Hierarchy Separation in a Classifier System Part 4: Coding and Representation A Hierarchical Approach to Learning the Boolean Multiplexer Function A Grammar-Based Genetic Algorithm Genetic Algorithms for Real Parameter Optimization Part 5: Framework Issues Fundamental Principles of Deception in Genetic Search Isomorphisms of Genetic Algorithms Conditions for Implicit Parallelism Part 6: Variation and Recombination The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination Genetic Operators for Sequencing Problems An Analysis of Multi-Point Crossover Evolution in Time and Space-The Parallel Genetic Algorithm Author Index Key Word Index
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