Experimental Assessment of Differential Evolution with Grid-Based Parameter Adaptation

摘要

Evolutionary algorithms have been long established as an essential field of research in Computational Intelligence. Differential Evolution is placed among the most successful algorithms of this type. However, it has proved to be highly sensitive on its parameters. For this purpose, offline and online parameter-control methods have been proposed. Recently, a grid-based parameter adaptation procedure was introduced and successfully applied on Differential Evolution. Despite the generality of the method, the resulting algorithm was capable to compete with already tuned adaptive algorithms on high-dimensional test problems without any additional preprocessing. The present work extends the experimental study of this approach on the state-of-the-art CEC-2013 test suite. Two variants of the algorithm are considered and different initial conditions are tested to shed light on its performance aspects. Comparisons with other algorithms as well as between the proposed approaches are reported. The results verify the potential of the grid-based parameter adaptation method as a general-purpose alternative for parameter setting.