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Towards a New Evolutionary Computation

Advances on Estimation of Distribution Algorithms

  • Book
  • © 2006

Access provided by Autonomous University of Puebla

Overview

  • 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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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.

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Keywords

Table of contents (12 chapters)

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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