Fuzzy adaptive search method for parallel genetic algorithm with combined sub-populations

We have already proposed fuzzy adaptive search method for parallel genetic algorithm (FASPGA) assorted fuzzy adaptive search method for genetic algorithm (FASGA) which is able to tune the genetic parameters according to the search stage by fuzzy rule and parallel genetic algorithm (PGA) which is able to obtain high-quality solutions in the evolution. In this paper, moreover, we propose FASPGA with the operation of dynamically combining sub-populations (C-FASPGA) which combines two elite islands in the final stage of the evolution to find a better solution as early as possible. Furthermore, we also report simulation results of learning Rastrigin function as compared with PGA and FASPGA.