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Towards a New Evolutionary Computation

Advances on Estimation of Distribution Algorithms

  • Book
  • © 2006

Overview

Editors:
  1. Jose A. Lozano

    1. Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastian, Spain

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  2. Pedro Larrañaga

    1. Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastian, Spain

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  3. Iñaki Inza

    1. Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastian, Spain

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  4. Endika Bengoetxea

    1. Intelligent Systems Group, Department of Architecture and Computer Technology, University of the Basque Country, Donostia-San Sebastián, Spain

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  • Introduces new concepts in the area of evolutionary computation

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

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Table of contents (12 chapters)

  1. Front Matter

    Pages I-XV
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  2. Linking Entropy to Estimation of Distribution Algorithms

    • Alberto Ochoa, Marta Soto
    Pages 1-38

  3. Entropy-based Convergence Measurement in Discrete Estimation of Distribution Algorithms

    • Jiri Ocenasek
    Pages 39-50

  4. Real-coded Bayesian Optimization Algorithm

    • Chang Wook Ahn, R. S. Ramakrishna, David E. Goldberg
    Pages 51-73

  5. The CMA Evolution Strategy: A Comparing Review

    • Nikolaus Hansen
    Pages 75-102

  6. Estimation of Distribution Programming: EDA-based Approach to Program Generation

    • Kohsuke Yanai, Hitoshi Iba
    Pages 103-122

  7. Multi-objective Optimization with the Naive \( \mathbb{M} \) ID\( \mathbb{E} \)A

    • Peter A.N. Bosman, Dirk Thierens
    Pages 123-157

  8. A Parallel Island Model for Estimation of Distribution Algorithms

    • Julio Madera, Enrique Alba, Alberto Ochoa
    Pages 159-186

  9. GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm

    • Victor Robles, Jose M. Peña, Pedro Larrañaga, María S. Pérez, Vanessa Herves
    Pages 187-219

  10. Bayesian Classifiers in Optimization: An EDA-like Approach

    • Teresa Miquélez, Endika Bengoetxea, Pedro Larrañaga
    Pages 221-242

  11. Feature Ranking Using an EDA-based Wrapper Approach

    • Yvan Saeys, Sven Degroeve, Yves Van de Peer
    Pages 243-257

  12. Learning Linguistic Fuzzy Rules by Using Estimation of Distribution Algorithms as the Search Engine in the COR Methodology

    • M. Julia Flores, José A. Gámez, José M. Puerta
    Pages 259-280

  13. Estimation of Distribution Algorithm with 2-opt Local Search for the Quadratic Assignment Problem

    • Qingfu Zhang, Jianyong Sun, Edward Tsang, John Ford
    Pages 281-292

  14. Back Matter

    Pages 293-294
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Keywords

About this book

Estimation of Distribution Algorithms (EDAs) are a set of algorithms in the Evolutionary Computation (EC) field characterized by the use of explicit probability distributions in optimization. Contrarily to other EC techniques such as the broadly known Genetic Algorithms (GAs) in EDAs, the crossover and mutation operators are substituted by the sampling of a distribution previously learnt from the selected individuals. EDAs have experienced a high development that has transformed them into an established discipline within the EC field.

This book attracts the interest of new researchers in the EC field as well as in other optimization disciplines, and that it becomes a reference for all of us working on this topic. The twelve chapters of this book can be divided into those that endeavor to set a sound theoretical basis for EDAs, those that broaden the methodology of EDAs and finally those that have an applied objective.

Editors and Affiliations

  • Department of Computer Science and Artificial Intelligence, University of the Basque Country, Donostia-San Sebastian, Spain

    Jose A. Lozano, Pedro Larrañaga, Iñaki Inza

  • Intelligent Systems Group, Department of Architecture and Computer Technology, University of the Basque Country, Donostia-San Sebastián, Spain

    Endika Bengoetxea

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