ENGLISH

Self-Adaptive Heuristics for Evolutionary Computation

By Oliver Kramer (auth.)

Book information

Publisher
Springer-Verlag Berlin Heidelberg
Year
2008
ISBN
3540692800, 9783540692805
DOI
10.1007/978-3-540-69281-2
Open Library ID
OL23191758M
Language
english
Format
PDF
Filesize
4 MB (4582760 bytes)
Series
Studies in Computational Intelligence 147
Edition
1
Pages
182\178
Topic
Education
Time added
2011-06-04 13:46:07
Appl.Mathematics/Computational Methods of Engineer

Description

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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