Self-Adaptive Heuristics for Evolutionary Computation: 147 (Studies in Computational Intelligence, 147) - Image 1

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Springer Self-Adaptive Heuristics for Evolutionary Computation: 147 (Studies in Computational Intelligence, 147)

Name: Self-Adaptive Heuristics for Evolutionary Computation: 147 (Studies in Computational Intelligence, 147)
Brand: Springer
Price: 76.42 GBP
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AI & Machine Learning

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Description

This book introduces various types of self-adaptive parameters for evolutionary computation. Besides extensive experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts. From the Back Cover 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 Specifications

Brand: Springer
Format: paperback
ASIN: 3642088783
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 28 October 2010
Listed Since: 20 September 2010

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Springer Self-Adaptive Heuristics for Evolutionary Computation: 147 (Studies in Computational Intelligence, 147)

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