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© 2000

Evolutionary Algorithms

The Role of Mutation and Recombination

  • 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

Book

Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages I-XIV
  2. Setting the Stage

    1. Front Matter
      Pages 1-1
    2. William M. Spears
      Pages 3-18
    3. William M. Spears
      Pages 19-35
  3. Static Theoretical Analyses

    1. Front Matter
      Pages 37-37
    2. William M. Spears
      Pages 39-58
    3. William M. Spears
      Pages 59-75
    4. William M. Spears
      Pages 83-90
    5. William M. Spears
      Pages 91-100
    6. William M. Spears
      Pages 101-115
  4. Dynamic Theoretical Analyses

    1. Front Matter
      Pages 127-127
    2. William M. Spears
      Pages 129-146
    3. William M. Spears
      Pages 147-153
    4. William M. Spears
      Pages 169-190
  5. Empirical Analyses

    1. Front Matter
      Pages 191-191
    2. William M. Spears
      Pages 193-201
  6. Summary

    1. Front Matter
      Pages 203-203

About this book

Introduction

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.

Keywords

Graph Master Patient Index Racter algorithms evolution evolutionary algorithm mutation optimization uncertainty

Authors and affiliations

  1. 1.AI Center — Code 5514Naval Research LaboratoryUSA

Bibliographic information

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