Self-Adaptive Heuristics for Evolutionary Computation

• Main Author: Kramer, Oliver (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Series: Studies in Computational Intelligence, 147
• Subjects: Computer science.; Artificial intelligence.; Computer-aided engineering.; Applied mathematics.; Engineering mathematics.; Computer Science.; Computer-Aided Engineering (CAD, CAE) and Design.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

 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. 

 I: Foundations of Evolutionary Computation -- Evolutionary Algorithms -- Self-Adaptation -- II: Self-Adaptive Operators -- Biased Mutation for Evolution Strategies -- Self-Adaptive Inversion Mutation -- Self-Adaptive Crossover -- III: Constraint Handling -- Constraint Handling Heuristics for Evolution Strategies -- IV: Summary -- Summary and Conclusion -- V: Appendix -- Continuous Benchmark Functions -- Discrete Benchmark Functions.