Self-Adaptive Heuristics for Evolutionary Computation (Studies in Computational Intelligence)@16999 Rs [Mrp:-16999]

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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.


Product Details :

Author Oliver Kramer
Binding Paperback
Brand imusti
EAN 9783642088780
Edition Softcover reprint of hardcover 1st ed. 2008
Format Import
ISBN 3642088783
Label Springer
Manufacturer Springer
MPN 39 black & white illustrations, 38 black
NumberOfItems 1
NumberOfPages 182
PartNumber 39 black & white illustrations, 38 black
ProductGroup Book
ProductTypeName ABIS_BOOK
PublicationDate 2010-10-28
Publisher Springer
Studio Springer
Title Self-Adaptive Heuristics for Evolutionary Computation (Studies in Computational Intelligence)