Parallel genetic algorithm with adaptive genetic parameters tuned by fuzzy reasoning
Yoichiro Maeda, Qiang Li · University of Fukui Library (University of Fukui) · 2005
Genetic algorithms (GAs) have several problems, the important of which is \tthat the search ability of ordinary GAs is not always optimal in the early and final stages \tof the search because of fixed GA parameters. Therefore, we have already proposed the \tfuzzy adaptive search method for genetic algorithms which is able to tune the genetic \tparameters according to the search stage by the fuzzy rule. \tIn this paper, a fuzzy adaptive search method for parallel genetic algorithms is proposed, \tin which the high-speed search ability of fuzzy adaptive tuning by FASGA is combined \twith and the high-quality solution capacity of parallel genetic algorithms. The proposed \tmethod offers improved search performance, and produces high-quality solutions. \tSimulations are performed to confirm the efficiency of the proposed method, which is \tshown to be superior to both ordinary and parallel genetic algorithms.