On the Randomization of Indices Selection for Differential Evolution

This research deals with the hybridization of two softcomputing fields, which are the chaos theory and evolutionary algorithms. This paper investigates the utilization of the two-dimensional discrete chaotic systems, which are Burgers and Lozi maps, as the chaotic pseudo random number generators (CPRNGs) embedded into the selected heuristics, which is differential evolution algorithm (DE). Through the utilization of either chaotic systems or identical identified pseudo random number distribution, it is possible to fully keep or remove the hidden complex chaotic dynamics from the generated pseudo random data series. Experiments are focused on the extended investigation, whether the different randomization types with different pseudo random numbers distribution or hidden complex chaotic dynamics providing the unique sequencing are more beneficial to the heuristic performance. This research utilizes set of 4 selected benchmark functions, and totally four different randomizations; further results are compared against canonical DE.