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Parameter Setting in Evolutionary Algorithms

  • Fernando G. Lobo
  • Cláudio F. Lima
  • Zbigniew Michalewicz

Part of the Studies in Computational Intelligence book series (SCI, volume 54)

Table of contents

  1. Front Matter
    Pages I-XII
  2. A. E. Eiben, Zbigniew Michalewicz, M. Schoenauer, J. E. Smith
    Pages 19-46
  3. Silja Meyer-Nieberg, Hans-Georg Beyer
    Pages 47-75
  4. Michael E. Samples, Matthew J. Byom, Jason M. Daida
    Pages 161-184
  5. Fernando G. Lobo, Cláudio F. Lima
    Pages 185-204
  6. Tian-Li Yu, Kumara Sastry, David E. Goldberg
    Pages 205-223
  7. Martin Pelikan, Alexander Hartmann, Tz-Kai Lin
    Pages 225-239
  8. Erick Cantú-Paz
    Pages 259-276
  9. Zbigniew Michalewicz, Martin Schmidt
    Pages 277-294
  10. Neal Wagner, Zbigniew Michalewicz
    Pages 295-309
  11. Back Matter
    Pages 311-317

About this book

Introduction

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.

Keywords

algorithm algorithms calculus control evolution evolutionary algorithm evolutionary computation evolutionary strategies genetic algorithms genetic programming multi-objective optimization mutation operator optimization programming

Editors and affiliations

  • Fernando G. Lobo
    • 1
  • Cláudio F. Lima
    • 2
  • Zbigniew Michalewicz
    • 3
  1. 1.Departamento de Engenharia Electrónica e InformáticaUniversidade do AlgarveFaroPortugal
  2. 2.Departamento de Engenharia Electrónica e InformáticaUniversidade do AlgarveFaroPortugal
  3. 3.School of Computer ScienceUniversity of AdelaideAdelaideAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-69432-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-69431-1
  • Online ISBN 978-3-540-69432-8
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site