Self-configuring ensemble of genetic algorithms for multimodal optimization problems

Multimodal optimization (MMO) is the problem of finding a set of all global and local optima or a good approximation of that set. In recent years many efficient nature-inspired and evolutionary techniques (based on ES, PSO, DE and others) have been proposed for real-valued problems. At the same time, many real-world problems contain variables of many different types, including integer, rank, binary and others. In this case, the weakest representation (namely binary representation) is used. 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, a novel approach based on a metaheuristic for designing multi-strategy genetic algorithm is proposed. The approach controls the interactions of many search techniques (different genetic algorithms for MMO) and leads to the self-configuring solving of problems with a priori unknown structure (“black-box” optimization). The results of numerical experiments and of comparison with other popular techniques for classical benchmark problems and benchmark problems from the CEC’2013 competition on MMO are presented. The proposed approach has demonstrated efficiency better than standard niching techniques and comparable to modern advanced algorithms. The main feature and advantage of the approach is that it does not require the participation of the human-expert, because it operates in an automated, self-configuring way.