Parallel genetic algorithm with adaptive genetic parameters tuned by fuzzy reasoning

Abstract

Genetic algorithms (GAs) have several problems, the important of which is\nthat the search ability of ordinary GAs is not always optimal in the early and final stages\nof the search because of fixed GA parameters. Therefore, we have already proposed the\nfuzzy adaptive search method for genetic algorithms which is able to tune the genetic\nparameters according to the search stage by the fuzzy rule.\nIn this paper, a fuzzy adaptive search method for parallel genetic algorithms is proposed,\nin which the high-speed search ability of fuzzy adaptive tuning by FASGA is combined\nwith and the high-quality solution capacity of parallel genetic algorithms. The proposed\nmethod offers improved search performance, and produces high-quality solutions.\nSimulations are performed to confirm the efficiency of the proposed method, which is\nshown to be superior to both ordinary and parallel genetic algorithms.