Overview
- Presents recent research on Self-Adaptive Heuristics for Evolutionary Computation
Part of the book series: Studies in Computational Intelligence (SCI, volume 147)
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Table of contents (10 chapters)
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Front Matter
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Introduction
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Part I: Foundations of Evolutionary Computation
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Front Matter
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Part II: Self-Adaptive Operators
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Front Matter
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Part III: Constraint Handling
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Front Matter
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Part IV: Summary
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Front Matter
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Part V: Appendix
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Front Matter
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Back Matter
Keywords
About this book
Evolutionary algorithms are successful biologically inspired meta-heuristics. Their success depends on adequate parameter settings. The question arises: how can evolutionary algorithms learn parameters automatically during the optimization? Evolution strategies gave an answer decades ago: self-adaptation. Their self-adaptive mutation control turned out to be exceptionally successful. But nevertheless self-adaptation has not achieved the attention it deserves.
This book introduces various types of self-adaptive parameters for evolutionary computation. Biased mutation for evolution strategies is useful for constrained search spaces. Self-adaptive inversion mutation accelerates the search on combinatorial TSP-like problems. After the analysis of self-adaptive crossover operators the book concentrates on premature convergence of self-adaptive mutation control at the constraint boundary. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts.
Authors and Affiliations
Bibliographic Information
Book Title: Self-Adaptive Heuristics for Evolutionary Computation
Authors: Oliver Kramer
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-540-69281-2
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2008
Hardcover ISBN: 978-3-540-69280-5Published: 19 August 2008
Softcover ISBN: 978-3-642-08878-0Published: 28 October 2010
eBook ISBN: 978-3-540-69281-2Published: 10 October 2008
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XII, 182
Number of Illustrations: 39 b/w illustrations
Topics: Computer-Aided Engineering (CAD, CAE) and Design, Mathematical and Computational Engineering, Artificial Intelligence