Self-Adaptive Heuristics for Evolutionary Computation

  • Oliver┬áKramer

Part of the Studies in Computational Intelligence book series (SCI, volume 147)

Table of contents

  1. Front Matter
  2. Introduction

    1. Oliver Kramer
      Pages 1-6
  3. Part I: Foundations of Evolutionary Computation

    1. Front Matter
      Pages 7-7
    2. Oliver Kramer
      Pages 9-27
    3. Oliver Kramer
      Pages 29-47
  4. Part II: Self-Adaptive Operators

    1. Front Matter
      Pages 49-49
    2. Oliver Kramer
      Pages 51-80
    3. Oliver Kramer
      Pages 81-95
    4. Oliver Kramer
      Pages 97-113
  5. Part III: Constraint Handling

    1. Front Matter
      Pages 115-115
  6. Part IV: Summary

    1. Front Matter
      Pages 141-141
    2. Oliver Kramer
      Pages 143-146
  7. Part V: Appendix

    1. Front Matter
      Pages 147-147
    2. Oliver Kramer
      Pages 149-158
    3. Oliver Kramer
      Pages 159-161
  8. Back Matter

About this book

Introduction

Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves.

This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.

Keywords

Computational Intelligence Computer-Aided Design (CAD) Evolution Evolutionary Intelligence Mutation Operator Self-Adaptive Heuristics algorithm algorithms biologically inspired evolutionary algorithm evolutionary computation heuristics metaheuristic optimization

Authors and affiliations

  • Oliver┬áKramer
    • 1
  1. 1.University of DortmundGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-69281-2
  • Copyright Information Springer Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-69280-5
  • Online ISBN 978-3-540-69281-2
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book