Evolutionary algorithm with ensemble strategies based on maximum a posteriori for continuous optimization

Abstract To be efficient and reach the best solution, evolutionary algorithms (EAs) are composed of three evolutionary operators: selection, crossover and mutation. Nevertheless, there is no established rule to choose them from a large panel of operators available in the literature. In this paper, a Maximum A Posteriori (MAP) principle based rule is proposed to palliate the operators’ selection problem for solving different kinds of continuous optimization problems. This learning based approach allows switching between different strategies during the optimization. This algorithm is called Maximum a posteriori based Evolutionary Algorithm (MEA). Experimental analysis was performed and proved its efficiency and robustness on a large set of 34 problems.

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