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Self-Adaptive Heuristics For Evolutionary Computation (Studies In Computational Intelligence)
by Oliver Kramer (Author),
Springer (Publisher)
Product Specifications
Book 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
Book Specifications
ISBN-13 9783540692805
Language English
Binding Hard Bound
Publisher Springer
Publishing Date 2008-10-01