EVOLUTIONARY OPTIMIZATION IN MULTIMODAL SEARCH SPACE

Optimization by a simple evolution strategy based on a mutation and se lection scheme without recombination was tested for its efficiency in multimodal search space. A modified Rastrigin function served as an ob jective function providing fitness landscapes with many local optima. It turned out that the evolutionary algorithm including adaptive steps ize control is well-suited for optimization. The process is able to ef ficiently surmount local energy barriers and converge to the global op timum. The relation between the optimization time available and the op timal number of offspring was investigated and a simple rule proposed. Several numbers of offspring are nearly equally suited in a smooth se arch space, whereas in rough fitness landscapes an optimum is observed . In either case both very large and very small numbers of offspring t urned out to be unfavourable for optimization.