Foundations of Genetic Algorithms 1991 (FOGA 1) book cover

Foundations of Genetic Algorithms 1991 (FOGA 1)

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; conditions for implicit parallelism; and analysis of multi-point crossover are also elaborated. This text likewise covers the genetic algorithms for real parameter optimization and isomorphisms of genetic algorithms.This publication is a good reference for students and researchers interested in genetic algorithms.

Hardbound, 341 Pages

Published: July 1991

Imprint: Morgan Kaufmann

ISBN: 978-1-55860-170-3

Contents


  • Introduction

    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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