Differential evolution vs. the functions of the 2/sup nd/ ICEO

Abstract

Differential evolution (DE) is a simple evolutionary algorithm for numerical optimization whose most novel feature is that it mutates vectors by adding weighted, random vector differentials to them. A new version of the DE algorithm is described and the results of its attempts to optimize the 7 real-valued functions of the 2/sup nd/ ICEO are tabulated. DE succeeded in finding each function's global minimum, although the number of evaluations needed in one instance was unacceptably high. Despite this lone difficulty, DE's speed of execution across the remaining test bed, in addition to its simplicity, robustness and ease of use, suggest that it is a valuable tool for continuous numerical optimization.