Self-Adaptive Heuristics for Evolutionary Computation (Paperback, Softcover reprint of hardcover 1st ed. 2008)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Studies in Computational Intelligence, 147
Release date:	October 2010
First published:	2008
Authors:	Oliver Kramer
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Paperback
Pages:	182
Edition:	Softcover reprint of hardcover 1st ed. 2008
ISBN-13:	978-3-642-08878-0
Categories:	Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematics for scientists & engineers Books > Professional & Technical > Technology: general issues > Technical design > Computer aided design (CAD) Books > Computing & IT > Applications of computing > Artificial intelligence > General Promotions
LSN:	3-642-08878-3
Barcode:	9783642088780

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