概要
Self-adaptive differential evolution approaches (self-adaptive DEs) often suffer to boost their performances under a limited number of fitness evaluations, since they heavily rely on the trial-and-error process required to adapt algorithmic configurations. In order to enhance the performance in early generations, this paper presents a generalized prior-validation framework for algorithmic configurations, which can be applicable to major variants of self-adaptive DEs that adapt the scaling factor, the crossover rate, and/or the mutation/crossover strategies for each individual. Experimental results on benchmark problems show that the proposed method successfully boosts the performances of jDE, SaDE, and JADE. Thus, the proposed method reveals a possibility of self-adaptive DEs toward computationally-expensive optimization problems where self-adaptive DEs have had a difficulty.
参考
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