Comparative Studies of Fuzzy Genetic Algorithms

Authors:
Qing Li;Yixin Yin;Zhiliang Wang;Guangjun Liu
Affiliations:
School of Information Engineering, University of Science and Technology Beijing, 100083 Beijing, China;School of Information Engineering, University of Science and Technology Beijing, 100083 Beijing, China;School of Information Engineering, University of Science and Technology Beijing, 100083 Beijing, China;Department of Aerospace Engineering, Ryerson University, M5B 2K3 Toronto, Canada
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

Many adaptive schemes for controlling the probabilities of crossover and mutation in genetic algorithms with fuzzy logic have been reported in recent years. However, there has not been known work on comparative studies of these algorithms. In this paper, several fuzzy genetic algorithms are briefly summarized first, and they are studied in comparison with each other under the same simulation conditions. The simulation results are analyzed in terms of search speed and search quality.