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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.
Produkteigenschaften
- Artikelnummer: 9783540692805
- Medium: Buch
- ISBN: 978-3-540-69280-5
- Verlag: Springer Berlin Heidelberg
- Erscheinungstermin: 19.08.2008
- Sprache(n): Englisch
- Auflage: 2008
- Serie: Studies in Computational Intelligence
- Produktform: Gebunden
- Gewicht: 465 g
- Seiten: 182
- Format (B x H x T): 160 x 241 x 16 mm
- Ausgabetyp: Kein, Unbekannt
Themen
- Mathematik | Informatik
- EDV | Informatik
- Angewandte Informatik
- Computeranwendungen in Wissenschaft & Technologie
- Mathematik | Informatik
- EDV | Informatik
- Angewandte Informatik
- Computeranwendungen in Wissenschaft & Technologie