Cultured differential evolution for constrained optimization

A cultural algorithm with a differential evolution population is proposed in this paper. This cultural algorithm uses different knowledge sources to influence the variation operator of the differential evolution algorithm, in order to reduce the number of fitness function evaluations required to obtain competitive results. Comparisons are provided with respect to three techniques that are representative of the state-of-the-art in the area. The results obtained by our algorithm are similar (in quality) to those obtained by the other approaches with respect to which it was compared. However, our approach requires a lower number of fitness function evaluations than the others.