Using genetic algorithm for magnitude model parameter identification

	Using genetic algorithm for magnitude model parameter identification
	Tan B.; Zhou H.; Zhang J.; Tang L.; Guo S.
	2007
发表期刊	Journal of Information and Computational Science
ISSN	15487741
卷号	4 期号:4 页码:1105-1112
摘要	The parameter identification is actually a complicated nonlinear optimization problem. The traditional optimization algorithms are apt to be trapped into local minimum and the computation efficiencies are quite low. However genetic algorithm is a directed random search method, which has a good probability converge to global optimum. A genetic algorithm called Random Sampling and Space Reduction Genetic Algorithm RSSRGA was designed to evaluate magnitude model parameter. The method is based on Simple Genetic Algorithm (SGM). A set of magnitude measurement data was used to test the algorithms. Three GA models, SGA, RSGA (Rand Sample Genetic Algorithm) and RSSRGA were evaluated, and all models can find the satisfied solutions, but random sampling space reduction genetic algorithm has the fastest convergence rate.
收录类别	EI
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/34598
专题	中科院长春光机所知识产出
推荐引用方式 GB/T 7714	Tan B.,Zhou H.,Zhang J.,et al. Using genetic algorithm for magnitude model parameter identification[J]. Journal of Information and Computational Science,2007,4(4):1105-1112.
APA	Tan B.,Zhou H.,Zhang J.,Tang L.,&Guo S..(2007).Using genetic algorithm for magnitude model parameter identification.Journal of Information and Computational Science,4(4),1105-1112.
MLA	Tan B.,et al."Using genetic algorithm for magnitude model parameter identification".Journal of Information and Computational Science 4.4(2007):1105-1112.