Self-Adaptive Heuristics for Evolutionary Computation
Publication year: 2008
ISBN: 978-3-540-69281-2
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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.
Subject: Engineering, Computational Intelligence, Computer-Aided Design (CAD), Evolution, Evolutionary Intelligence, Mutation, Operator, Self-Adaptive Heuristics, algorithm, algorithms, biologically inspired, evolutionary algorithm, evolutionary computation, heuristics, metaheuristic, optimization
