A Genetic Algorithm Using a Mixed Crossover Strategy

Authors:
Li-Yan Zhuang;Hong-Bin Dong;Jing-Qing Jiang;Chu-Yi Song
Affiliations:
College of Mathematics and Computer Science, Inner Mongolia University for, Nationalities, Tongliao, P.R. China 028043;Department of Computer Science, Harbin Normal University, Harbin, P.R. China 150080;College of Mathematics and Computer Science, Inner Mongolia University for, Nationalities, Tongliao, P.R. China 028043;College of Mathematics and Computer Science, Inner Mongolia University for, Nationalities, Tongliao, P.R. China 028043
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Year:
2008

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Abstract

Function Optimization is a typical problem. A mixed crossover strategy genetic algorithm for function optimization is proposed in this paper. Four crossover strategies are mixed in this algorithm and the performance is improved compared with traditional genetic algorithm using single crossover strategy. The numerical experiment is carried out on nine traditional functions and the results show that the proposed algorithm is superior to four single pure crossover strategy genetic algorithms in the convergence rate for function optimization problems.