Nie XingXin
College of management Xi`an University of Architecture and Technology, China
Lu CaiWu
College of management Xi`an University of Architecture and Technology, China
Liu ShuXiang
College of management Xi`an University of Architecture and Technology, China
Shen Yang Yang
College of management Xi`an University of Architecture and Technology, China
ABSTRACT
The three-factor about replication, Exchange, mutation probabilities and their interaction is important on the convergence of the genetic algorithm. In order to analysis the effect. This study designed an three-factor cross-group experiment of three probability about reproduction, crossover and mutation, we use analysis of variance on empirical datum to obtain the effect of reproduction, crossover and mutation on convergence of Genetic algorithm. Conclusion of study show that, the influence of reproduction is the most significant, the influence of crossover is the second, the influence of mutation is not significant. But, as the main way of producing new individuality, Reproduction probability is important for getting overall situation optimal solution. In addition, the instrumental error is great which show that the calculate result of Genetic algorithm is randomness.
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How to cite this article
Nie XingXin, Lu CaiWu, Liu ShuXiang and Shen Yang Yang, 2013. Analysis of Variance for A Influence of Genetic Probability on the Convergence
times of Genetic Algorithm. Information Technology Journal, 12: 6807-6811.
DOI: 10.3923/itj.2013.6807.6811
URL: https://scialert.net/abstract/?doi=itj.2013.6807.6811
DOI: 10.3923/itj.2013.6807.6811
URL: https://scialert.net/abstract/?doi=itj.2013.6807.6811
REFERENCES
- Bai, X.J., G.H. Peng and X. Chen, 2009. Bezier curve fitting based on genetic algorithm and steepest descent algorithm. Comput. Eng. Design.
Direct Link - Fallah-Jamshidi, S., M. Amiri and N. Karimi, 2010. Nonlinear continuous multi-response problems: A novel two-phase hybrid genetic based metaheuristic. Applied Soft Comput., 10: 1274-1283.
CrossRef - Tan, Y.H., S.H. Chen, G.M. Zhang and Z.T. Xiong, 2013. Adaptive impedance matching using quantum genetic algorithm. J. Central South Univ., 20: 977-981.
CrossRef - Yu, S.Y. and S.Q. Zuo, 2010. Convergence and convergence rate analysis of elitist genetic algorithm based on martingale approach. Control Theory Appl., 7: 843-848.
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