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Parameter Setting in Evolutionary Algorithms

  • Book
  • © 2007

Overview

Editors:
  1. Fernando G. Lobo

    1. Departamento de Engenharia Electrónica e Informática, Universidade do Algarve, Faro, Portugal

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  2. Cláudio F. Lima

    1. Departamento de Engenharia Electrónica e Informática, Universidade do Algarve, Faro, Portugal

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  3. Zbigniew Michalewicz

    1. School of Computer Science, University of Adelaide, Adelaide, Australia

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  • Recent advances in evolutionary algorithms
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (15 chapters)

  1. Front Matter

    Pages I-XII
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  2. Parameter Setting in EAs: a 30 Year Perspective

    • Kenneth De Jong
    Pages 1-18

  3. Parameter Control in Evolutionary Algorithms

    • A. E. Eiben, Zbigniew Michalewicz, M. Schoenauer, J. E. Smith
    Pages 19-46

  4. Self-Adaptation in Evolutionary Algorithms

    • Silja Meyer-Nieberg, Hans-Georg Beyer
    Pages 47-75

  5. Adaptive Strategies for Operator Allocation

    • Dirk Thierens
    Pages 77-90

  6. Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms

    • Mike Preuss, Thomas Bartz-Beielstein
    Pages 91-119

  7. Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks

    • Bo Yuan, Marcus Gallagher
    Pages 121-142

  8. Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques

    • Alan Piszcz, Terence Soule
    Pages 143-160

  9. Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms

    • Michael E. Samples, Matthew J. Byom, Jason M. Daida
    Pages 161-184

  10. Adaptive Population Sizing Schemes in Genetic Algorithms

    • Fernando G. Lobo, Cláudio F. Lima
    Pages 185-204

  11. Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements

    • Tian-Li Yu, Kumara Sastry, David E. Goldberg
    Pages 205-223

  12. Parameter-less Hierarchical Bayesian Optimization Algorithm

    • Martin Pelikan, Alexander Hartmann, Tz-Kai Lin
    Pages 225-239

  13. Evolutionary Multi-Objective Optimization Without Additional Parameters

    • Kalyanmoy Deb
    Pages 241-257

  14. Parameter Setting in Parallel Genetic Algorithms

    • Erick Cantú-Paz
    Pages 259-276

  15. Parameter Control in Practice

    • Zbigniew Michalewicz, Martin Schmidt
    Pages 277-294

  16. Parameter Adaptation for GP Forecasting Applications

    • Neal Wagner, Zbigniew Michalewicz
    Pages 295-309

  17. Back Matter

    Pages 311-317
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Keywords

About this book

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.

Editors and Affiliations

  • Departamento de Engenharia Electrónica e Informática, Universidade do Algarve, Faro, Portugal

    Fernando G. Lobo, Cláudio F. Lima

  • School of Computer Science, University of Adelaide, Adelaide, Australia

    Zbigniew Michalewicz

Bibliographic Information

  • Book Title: Parameter Setting in Evolutionary Algorithms

  • Editors: Fernando G. Lobo, Cláudio F. Lima, Zbigniew Michalewicz

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-540-69432-8

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2007

  • Hardcover ISBN: 978-3-540-69431-1Published: 16 March 2007

  • Softcover ISBN: 978-3-642-08892-6Published: 30 November 2010

  • eBook ISBN: 978-3-540-69432-8Published: 03 April 2007

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 318

  • Number of Illustrations: 100 b/w illustrations

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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