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Evolutionary Algorithms

The Role of Mutation and Recombination

  • Book
  • © 2000

Overview

Authors:
  1. William M. Spears

    1. AI Center — Code 5514, Naval Research Laboratory, USA

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  • A thorough analysis of recombination and mutation in evolutionary algorithms
  • New theoretical and modeling tools for studying evolutionary algorithms
  • A new empirical tool for comparing search and optimization algorithms and a new theoretical tool for studying complex systems in general
  • Includes supplementary material: sn.pub/extras

Part of the book series: Natural Computing Series (NCS)

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Table of contents (15 chapters)

  1. Front Matter

    Pages I-XIV
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  2. Setting the Stage

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • William M. Spears
      Pages 3-18

    3. Background

      • William M. Spears
      Pages 19-35

  3. Static Theoretical Analyses

    1. Front Matter

      Pages 37-37
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    2. A Survival Schema Theory for Recombination

      • William M. Spears
      Pages 39-58

    3. A Construction Schema Theory for Recombination

      • William M. Spears
      Pages 59-75

    4. Survival and Construction Schema Theory for Recombination

      • William M. Spears
      Pages 77-82

    5. A Survival Schema Theory for Mutation

      • William M. Spears
      Pages 83-90

    6. A Construction Schema Theory for Mutation

      • William M. Spears
      Pages 91-100

    7. Schema Theory: Mutation versus Recombination

      • William M. Spears
      Pages 101-115

    8. Other Static Characterizations of Mutation and Recombination

      • William M. Spears
      Pages 117-126

  4. Dynamic Theoretical Analyses

    1. Front Matter

      Pages 127-127
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    2. Dynamic Analyses of Mutation and Recombination

      • William M. Spears
      Pages 129-146

    3. A Dynamic Model of Selection and Mutation

      • William M. Spears
      Pages 147-153

    4. A Dynamic Model of Selection, Recombination, and Mutation

      • William M. Spears
      Pages 155-168

    5. An Aggregation Algorithm for Markov Chains

      • William M. Spears
      Pages 169-190

  5. Empirical Analyses

    1. Front Matter

      Pages 191-191
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    2. Empirical Validation

      • William M. Spears
      Pages 193-201

  6. Summary

    1. Front Matter

      Pages 203-203
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Keywords

About this book

Despite decades of work in evolutionary algorithms, there remains a lot of uncertainty as to when it is beneficial or detrimental to use recombination or mutation. This book provides a characterization of the roles that recombination and mutation play in evolutionary algorithms. It integrates prior theoretical work and introduces new theoretical techniques for studying evolutionary algorithms. An aggregation algorithm for Markov chains is introduced which is useful for studying not only evolutionary algorithms specifically, but also complex systems in general. Practical consequences of the theory are explored and a novel method for comparing search and optimization algorithms is introduced. A focus on discrete rather than real-valued representations allows the book to bridge multiple communities, including evolutionary biologists and population geneticists.

Authors and Affiliations

  • AI Center — Code 5514, Naval Research Laboratory, USA

    William M. Spears

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