Fuzzy adaptive search method for parallel genetic algorithm with island combination process

Authors:
Yoichiro Maeda;Masahide Ishita;Qiang Li
Affiliations:
Department of Human and Artificial Intelligent Systems, Faculty of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan;Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan;Department of Human and Artificial Intelligent Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan
Venue:
International Journal of Approximate Reasoning
Year:
2006

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Abstract

Genetic algorithms (GAs) pose several problems. Probably, the most important one 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. To solve this problem, we proposed the fuzzy adaptive search method for genetic algorithms (FASGA) that is able to tune the genetic parameters according to the search stage by the fuzzy reasoning. In this paper, a fuzzy adaptive search method for parallel genetic algorithms (FASPGA) is proposed, in which the high-speed search ability of fuzzy adaptive tuning by FASGA is combined with the high-quality solution finding capacity of parallel genetic algorithms. The proposed method offers improved search performance, and produces high-quality solutions. Moreover, we also propose FASPGA with an operation of combining dynamically 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. Simulations are performed to confirm the efficiency of the proposed method, which is shown to be superior to both ordinary and parallel genetic algorithms.