Application of Adaptive Genetic Algorithm in Function Optimization
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32858
Application of Adaptive Genetic Algorithm in Function Optimization

Authors: Panpan Xu, Shulin Sui


The crossover probability and mutation probability are the two important factors in genetic algorithm. The adaptive genetic algorithm can improve the convergence performance of genetic algorithm, in which the crossover probability and mutation probability are adaptively designed with the changes of fitness value. We apply adaptive genetic algorithm into a function optimization problem. The numerical experiment represents that adaptive genetic algorithm improves the convergence speed and avoids local convergence.

Keywords: Genetic algorithm, Adaptive genetic algorithm, Function optimization.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1668


[1] Y.X. Yuan, W.Y. Sun. Optimization Theory and Methods, Beijing: Science Press, 2007, pp.1-50.
[2] L.Y. Jia, X. Du. Study of Parallel Genetic Algorithm, Journal of Hunan City University, 2006, 15(3), pp.72-74.
[3] X.L. Wang, J. lu. Optimization Methods and Optimal Control, Harbin: Harbin Engineering University Press, 2006, pp. 3-74.
[4] J. Liu, W.C. Zhong, F. Liu. Organizational Evolutionary Optimization, Journal of computers, 2004, 27(2), pp. 157-167.
[5] Y. Zeng. Application of Improved Genetic Algorithm in Nonlinear Equations, Journal of East China Jiaotong University, 2004, 21(4), pp.39-41.
[6] Y.F. Sun, Z.J. Wang. Application of Genetic Algorithm in Function Optimization Progress, Control and Decision, 1996, 11(4), pp. 425-431.
[7] G.Y. Liao. Adaptive Genetic Algorithm, Technology Square, 2007(3), pp. 70-72.
[8] C.Z. Chen, N. Wang. Adaptive Approach and Mechanism of Crossover and Mutation Probability in genetic algorithm, Control Theory & Applications, 2002, 19(1), pp. 41-43.
[9] J.Z. Zhang, T. Jiang. Improved Adaptive Genetic Algorithm, Computational Engineering and Applications, 2010, 46(11), pp.53-55.
[10] Z.W. Ren, Z. San. Improved Adaptive Genetic Algorithm and Its Application in System Identification, Journal of System Simulation, 2006, 18(1), pp. 41-43.
[11] L. Zhang, Y. Liu, W. He. Application of Adaptive Genetic Algorithm in License Plate Location, Computer Application, 2008, 28(1), PP. 185-188.
[12] W.L. Wang, Q. D. Wu, Y. Song. Adaptive genetic algorithm shop scheduling problem, Systems Engineering Theory and Practice, 2004, 12(2), pp. 58-62.