Self-Adaptive Heuristics for Evolutionary Computation
Autor: | Kramer, Oliver |
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EAN: | 9783540692805 |
Sachgruppe: | Informatik, EDV Technik |
Sprache: | Englisch |
Seitenzahl: | 196 |
Produktart: | Gebunden |
Veröffentlichungsdatum: | 19.08.2008 |
Schlagworte: | Intelligenz / Künstliche Intelligenz KI Künstliche Intelligenz - AI Programmieren (EDV) / Evolutionär |
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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.