Ensemble methods are widely used to improve decision making in the field of statistics and machine learning. On average, the collective solution of multiple algorithms provides better performance than could be obtained from any of the constituent algorithms. The ensemble concept can be also used in the field of evolutionary algorithms. The main idea is to include many search algorithms in the ensemble and to design effective control of interaction of algorithms. Such interaction is implemented in different forms of island models, coevolutionary schemes, population-based algorithm portfolios and others. In this paper, a metaheuristic for designing multi-strategy genetic algorithm for multimodal optimization is proposed. Multimodal optimization is the problem of finding many or all global and local optima. In recent years many efficient multimodal techniques have been proposed in the field of population-based nature-inspired search algorithms. The majority of techniques are designed 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, a binary representation is used. There is a lack of efficient approaches for problems with binary representation. Moreover, binary and binarized problems are usually “black-box” optimization problems, thus there exists the problem of choosing a suitable algorithm and fine tuning it for a certain problem. The proposed approach contains many different multimodal genetic algorithms, which implement different search strategies. The metaheuristic adaptively controls the interactions of many search techniques and leads to the self-configuring solving of problems with a priori unknown structure. We present the results of numerical experiments for classical binary benchmark problems and benchmark problems from the CEC 2013 competition on multimodal optimization. We also present the results for some real-world problems.
Sopov, E. (2017). Self-configuring ensemble of multimodal genetic algorithms. In Studies in Computational Intelligence (Vol. 669, pp. 56–74). Springer Verlag. https://doi.org/10.1007/978-3-319-48506-5_4