| Self-Adaptive Heuristics for Evolutionary Computation 2008 Edition Contributor(s): Kramer, Oliver (Author) |
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ISBN: 3540692800 ISBN-13: 9783540692805 Publisher: Springer OUR PRICE: $104.49 Product Type: Hardcover - Other Formats Published: August 2008 |
| Additional Information |
| BISAC Categories: - Mathematics | Applied - Computers | Intelligence (ai) & Semantics - Computers | Cad-cam |
| Dewey: 006.3 |
| Series: Studies in Computational Intelligence |
| Physical Information: 0.6" H x 6.2" W x 9.4" (0.90 lbs) 182 pages |
| Descriptions, Reviews, Etc. |
| Publisher 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 experiments, statistical tests and some theoretical investigations enrich the analysis of the proposed concepts. |