A parallel genetic algorithm on the CM-2 for multi-modal optimization

A genetic algorithm is an optimization method well suited to be implemented on a SIMD machine; it deals with a population of individuals (Multiple Data) that evolve in parallel and undergo the same operations (Single Instruction). This paper presents a genetic algorithm with a dynamic division mechanism conceived on the Connection Machine-2 to treat multimodal optimization problems, i.e. search spaces with multiple maxima. The general idea of the algorithm is to dynamically divide the population into an increasing number of subpopulations to allow specialization on the different maxima discovered during the search process. The method is flexible because it requires practically no a-priori knowledge about the fitness function. Results of applications to multi-modal two-dimensional landscapes are presented