An Improved Adaptive Algorithm for Controlling the Probabilities of Crossover and Mutation Based on a Fuzzy Control Strategy

Authors:
Qing Li;Xinhai Tong;Sijiang Xie;Guangjun Liu
Affiliations:
University of Science and Technology, China;Beijing Electronic Science and Technology Institute, China;Beijing Electronic Science and Technology Institute, China;Ryerson University, Canada
Venue:
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Year:
2006

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Abstract

An improved adaptive algorithm for controlling the probabilities of crossover and mutation with fuzzy logic is proposed in this paper. The changes of average fitness value and standard deviation between two continuous generations are selected as input and the changes of crossover probability and mutation probability are the output variables. Two adaptive scaling factors are introduced for normalizing the input variables and new fuzzy rules based on domain heuristic knowledge are investigated for adjusting the probabilities of crossover and mutation. Numerical simulation studies of three different test functions are carried out, and the simulation results show that the genetic algorithm with the proposed adaptive fuzzy controller exhibits improved search speed and quality.