Entropy driven self-adaptive differential evolution

Abstract

Self-adaptiveDE is one of the most efficient and popular modifications of evolution algorithm (Differential Evolution). The study deals with the possibility of using the entropy evaluation of population for SaDE algorithm modification, with an impact on its convergence towards global extreme. Further, the unavoidable increase of computational complexity is discussed. The paper proposes an algorithm based on the efficiency principles of SaDE algorithm. This proposed algorithm uses the above mentioned entropy calculation. In order to increase the algorithm efficiency, a recursive differential evolution was applied; it modifies its scale factors to gain the best convergence abilities, with respect to its robustness. Detailed performance comparisons of this algorithm on the currently used benchmark-functions are included.