Entropic Differential Evolution - ℯDE

Evolutionary Algorithms have found great utility in most engineering applications, especially in control systems engineering in which tuning optimization problems may be nonlinear, multimodal and discontinuous functions of the optimization parameters. The performance of most Evolutionary Algorithms is quite sensitive to their control parameters. This paper considers the tuning problem for a variant of Evolutionary Algorithms known as Differential Evolution (DE). Differential Evolution is sensitive to loss of diversity because its mutation is a function of the hamming distance of the genotypes. Using the joint genotypic and phenotypic entropies, the paper proposes a fully adaptive Differential Evolution which requires no parameter tuning; the user is only required to set the population size. Furthermore, when tested on a standard set of benchmark objective functions the proposed algorithm shows improvements in convergence rate and the number of objective function evaluations compared to a standard, optimally tuned, and a self-adaptive differential evolution.