On the tuning of complex dynamics embedded into differential evolution

Abstract

This research deals with the hybridization of the two soft-computing fields, which are chaos theory and evolutionary computation. This paper aims on the experimental investigations on the chaos-driven evolutionary algorithm Differential Evolution (DE) concept. This research represents the continuation of the satisfactory results obtained by means of chaos embedded (driven) DE, which utilizes the chaotic dynamics in the place of pseudorandom number generators This work is aimed at the tuning of the complex chaotic dynamics directly injected into the DE. To be more precise, this research investigates the influence of different parameter settings for discrete chaotic systems to the performance of DE. Repeated simulations were performed on the IEEE CEC 13 benchmark functions set in dimension of 30. Finally, the obtained results are compared with canonical DE and jDE.