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 constrainedsearch 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. Presents recent research on Self-Adaptive Heuristics for Evolutionary Computation INDICE: Part I Foundations of Evolutionary Computation.- Evolutionary Algorithms.- Self-Adaptation.- Part II Self-Adaptive Operators.- Biased Mutation for Evolution Strategies.- Self-Adaptive Inversion Mutation.- Self-Adaptive Crossover.- Part III Constraint Handling.- Constraint Handling Heuristics for Evolution Strategies.
- ISBN: 978-3-540-69280-5
- Editorial: Springer
- Encuadernacion: Cartoné
- Páginas: 180
- Fecha Publicación: 01/07/2008
- Nº Volúmenes: 1
- Idioma: Inglés