Erschienen: 19.08.2008 Abbildung von Kramer | Self-Adaptive Heuristics for Evolutionary Computation | 2008 | 147

Kramer

Self-Adaptive Heuristics for Evolutionary Computation

2008. Buch. xii, 182 S. 39 s/w-Abbildungen, 38 s/w-Tabelle, Bibliographien. Hardcover

Springer. ISBN 978-3-540-69280-5

Format (B x L): 15,5 x 23,5 cm

Gewicht: 461 g

In englischer Sprache

Produktbeschreibung

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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