Self-Adaptive Heuristics for Evolutionary Computation / Edition 1

Self-Adaptive Heuristics for Evolutionary Computation / Edition 1

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by Oliver Kramer
     
 

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

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Overview

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.

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Product Details

ISBN-13:
9783540692805
Publisher:
Springer Berlin Heidelberg
Publication date:
09/18/2008
Series:
Studies in Computational Intelligence Series, #147
Edition description:
2008
Pages:
182
Product dimensions:
6.20(w) x 9.40(h) x 0.60(d)

Table of Contents

1 Introduction 1

Pt. I Foundations of Evolutionary Computation

2 Evolutionary Algorithms 9

3 Self-Adaptation 29

Pt. II Self-Adaptive Operators

4 Biased Mutation for Evolution Strategies 51

5 Self-Adaptive Inversion Mutation 81

6 Self-Adaptive Crossover 97

Pt. III Constraint Handling

7 Constraint Handling Heuristics for Evolution Strategies 117

Pt. IV Summary

8 Summary and Conclusion 143

Pt. V Appendix

A Continuous Benchmark Functions 149

B Discrete Benchmark Functions 159

References 163

List of Figures 173

List of Tables 175

Index 179

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