Self-Adaptive Heuristics for Evolutionary Computation (Studies in Computational Intelligence) (Repost)
Self-Adaptive Heuristics for Evolutionary Computation (Studies in Computational Intelligence) by Oliver Kramer
English | 2008 | ISBN: 3540692800 | 182 Pages | PDF | 4 MB
English | 2008 | ISBN: 3540692800 | 182 Pages | PDF | 4 MB
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.
Self-Adaptive Heuristics for Evolutionary Computation (Studies in Computational Intelligence) (Repost)
