Parallel genetic algorithm with adaptive genetic parameters tuned by fuzzy reasoning

Genetic algorithms (GAs) have several problems, the importance of which is that the search ability of ordinary GAs is not always optimal in the early and final stages of the search because of fixed GA parameters. Therefore, the fuzzy adaptive search method for genetic algorithms has been proposed, which is able to tune the genetic parameters according to the search stage by the fuzzy rule. In this paper, a fuzzy adaptive search method for parallel genetic algorithms is developed, in which the high-speed search ability of fuzzy adaptive tuning by FASGA is combined with the high-quality solution capacity of parallel genetic algorithms. The proposed method offers improved search performance, and produces high-quality solutions. Simulations are performed to confirm the efficiency of the theoretic results, which is shown to be superior to both ordinary and parallel genetic algorithms.