A Self-configuring Multi-strategy Multimodal Genetic Algorithm

In recent years many efficient nature-inspired techniques (based on evolutionary strategies, particle swarm optimization, differential evolution and others) have been proposed for real-valued multimodal optimization (MMO) problems. Unfortunately, there is a lack of efficient approaches for problems with binary representation. Existing techniques are usually based on general ideas of niching. Moreover, there exists the problem of choosing a suitable algorithm and fine tuning it for a certain problem. In this study, an approach based on a metaheuristic for designing multi-strategy genetic algorithm is proposed. The approach controls the interactions of many MMO techniques (different genetic algorithms) and leads to the self-configuring solving of problems with a priori unknown structure. The results of numerical experiments for benchmark problems from the CEC competition on MMO are presented. The proposed approach has demonstrated efficiency better than standard niching techniques and comparable to advanced algorithms. The main feature of the approach is that it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.