| 书目名称 | Self-Adaptive Heuristics for Evolutionary Computation |
| 编辑 | Oliver Kramer |
| 视频video | |
| 概述 | Presents recent research on Self-Adaptive Heuristics for Evolutionary Computation |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2008 |
| 关键词 | Computational Intelligence; Computer-Aided Design (CAD); Evolution; Evolutionary Intelligence; Mutation; |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-540-69281-2 |
| isbn_softcover | 978-3-642-08878-0 |
| isbn_ebook | 978-3-540-69281-2Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer-Verlag Berlin Heidelberg 2008 |
发表于 2025-3-21 18:22:43
