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Towards a New Evolutionary Computation

Advances in the Estimation of Distribution Algorithms

  • Jose A. Lozano
  • Pedro Larrañaga
  • Iñaki Inza
  • Endika Bengoetxea

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 192)

Table of contents

  1. Front Matter
    Pages I-XV
  2. Alberto Ochoa, Marta Soto
    Pages 1-38
  3. Chang Wook Ahn, R. S. Ramakrishna, David E. Goldberg
    Pages 51-73
  4. Nikolaus Hansen
    Pages 75-102
  5. Julio Madera, Enrique Alba, Alberto Ochoa
    Pages 159-186
  6. Victor Robles, Jose M. Peña, Pedro Larrañaga, María S. Pérez, Vanessa Herves
    Pages 187-219
  7. Teresa Miquélez, Endika Bengoetxea, Pedro Larrañaga
    Pages 221-242
  8. Yvan Saeys, Sven Degroeve, Yves Van de Peer
    Pages 243-257
  9. Qingfu Zhang, Jianyong Sun, Edward Tsang, John Ford
    Pages 281-292
  10. Back Matter
    Pages 293-294

About this book

Introduction

This is a nicely edited volume on Estimation of Distribution Algorithms (EDAs) by leading researchers on this important topic.

It covers a wide range of topics in EDAs, from theoretical analysis to experimental studies, from single objective to multi-objective optimisation, and from parallel EDAs to hybrid EDAs. It is a very useful book for everyone who is interested in EDAs, evolutionary computation or optimisation in general.

Xin Yao, IEEE Fellow
Editor-in-Chief, IEEE Transactions on Evolutionary Computation

______________________________________________________________

Estimation of Distribution Algorithms (EDAs) have "removed genetics"

from Evolutionary Algorithms (EAs). However, both approaches (still) have a lot in common, and, for instance, each one could be argued to in fact include the other! Nevertheless, whereas some theoretical approaches that are specific to EDAs are being proposed, many practical issues are common to both fields, and, though proposed in the mid 90's only, EDAs are catching up fast now with EAs, following many research directions that have proved successful for the latter:

opening to different search domains, hybridizing with other methods (be they OR techniques or EAs themselves!), going parallel, tackling difficult application problems, and the like.

This book proposes an up-to-date snapshot of this rapidly moving field, and witnesses its maturity. It should hence be read ... rapidly, by anyone interested in either EDAs or EAs, or more generally in stochastic optimization.

Marc Schoenauer
Editor-in-Chief, Evolutionary Computation

Keywords

Wrapper algorithm algorithms artificial intelligence evolution evolutionary algorithm evolutionary computation fuzzy genetic algorithms learning model mutation operator optimization programming

Editors and affiliations

  • Jose A. Lozano
    • 1
  • Pedro Larrañaga
    • 1
  • Iñaki Inza
    • 1
  • Endika Bengoetxea
    • 2
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of the Basque CountryDonostia-San SebastianSpain
  2. 2.Intelligent Systems Group, Department of Architecture and Computer TechnologyUniversity of the Basque CountryDonostia-San SebastiánSpain

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-32494-1
  • Copyright Information Springer Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-29006-3
  • Online ISBN 978-3-540-32494-2
  • Series Print ISSN 1434-9922
  • Buy this book on publisher's site
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