Adaptive immune-genetic algorithm for global optimization to multivariable function * * This project was supported by the Research Fund for the Doctoral Program of Higher Education of China (20020008004).

Journal of Systems Engineering and Electronics

Volumn 18, Issue 3, 2007, Pages 655-660

  Yongshou, Dai ^a   Yuanyuan, Li ^a   Lei, Wei ^a   Junling, Wang ^a   Deling, Zheng ^b  

^a CHINA UNIVERSITY OF PETROLEUM   (China)

^b UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

Author keywords

density operator; function optimization; hyper mutation; immune genetic algorithm

Indexed keywords

DENSITY OPERATOR; FUNCTION OPTIMIZATION; HYPER-MUTATION; IMMUNE-GENETIC ALGORITHM;

COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; GENETIC ALGORITHMS; GLOBAL OPTIMIZATION; POPULATION DYNAMICS; SEARCH ENGINES;

ADAPTIVE SYSTEMS;

EID: 34848880140     PISSN: 10044132     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1004-4132(07)60144-3     Document Type: Article

References (8)

1. Holland J. Adaptation in natural and artificial systems (1975), University of Michigan Press
2. Rudolph G. Convergence analysis of canonical genetical algorithms. IEEE Trans. on Neural Networks 5 1 (1994) 96-101
3. Meng Hongyun, Liu Sanyang. ISPEA: improvement for the strength paroto evolutionary algorithm for multiobjective optimization with immunity. Proceedings of the Fifth Intl. Conf. Computational Intelligence and Multimedia Applications. IEEE, 2003.
4. De Castro L., and Timmis J. An artificial immune network for multimodal function optimization. Proceeding of IEEE Cong. Evolutionary Computation 1 (2002) 674-699
5. Dasgupta D. Artificial immune system and their applications (1999), SpringerVerlag
6. Dasgupta D., and Nii A.-O. Immunity-based systems: a survey. IEEE International Conference on Systems. Man and Cybernetics (1997) 369-374
7. De Castro L N, Von Zuben F J, Artificial immune systems: part I-basic theory and applications. Technical Report RT-DCA 01/99, 1999.
8. Srinivas M., and Patnaik L. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Trans on Systems, Man, and Cybernetics 24 4 (1994) 656-665