The analysis of global convergence and computational efficiency for genetic algorithm

Kongzhi Lilun Yu Yingyong/Control Theory and Applications

Volumn 13, Issue 4, 1996, Pages

  Yun, Weimin ^a   Xi, Yugeng ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Adaptive genetic algorithm (SGA); Computational efficiency; Global convergence; Optimum maitaining SGA (OMSGA); Simple genetic algorthm (SGA)

Indexed keywords

EID: 77949991693     PISSN: 10008152     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (10)

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2. Eiben, A. E.,Aarts,E. H.and Van Hee,K. M., Global Convergence of Genetic Algorithms: An Infinite Markov-Chain Analysis. Parallel Problem Solving from Nature. Schwefel,H. P. and Manner, R. .Eds. Heidelberg. Berlin: Springer-verlag, 1991:4-12
3. Rudolph,G., Convergence Properties of Canonical Genetic Algorithms. IEEE Trans, on Neural Networks. 1991. 5 (1):96-101
4. Qi.X. .Palmieri.F., Theoretical Analysis of Evolutionary Algorithms with an Infinite Population Size in Continuous Space,Part I: Basic Properties of selection and Mutation, IEEE Trans.on Neural Networks,1994.102-119
5. Back,T., The Interaction of Mutation Rate,Selection and Self-Adaption within Genetic Algorithms. Parallel Problem Solving from Nature,2,'Amsterdam, North Holland, 1992,84-94
6. Muhlenbein.H., How Genetic Algorithms Really Work. I: Mutation and Hillclimbing. Parallel Problen Solving from Nature,2,Amsterdam,North Holland,1992.15-25
7. Scrinvas,M. ,Patnaik,L. M., Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Trans. SMC, 1994,24(4); 656-666