Automatic design of mutation parameter adaptation for differential evolution

Abstract

In this paper the Efficient Global Optimization algorithm is applied to design the adaptation strategy for mutation parameter in Differential Evolution. The adaptation strategy is represented as a Taylor series, to allow exploring a search space of different curves. The tuning of the adaptation is performed on the L-NTADE algorithm using the benchmark of Congress on Evolutionary Computation competition on single-objective numerical optimization 2017. The experimental results show that the discovered dependence between the success rate and the parameter in current-to-pbest mutation strategy allows improving the algorithm performance in various cases.