Differential evolution vs. the functions of the 2nd ICEO

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^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