Title :
Study of Immune Genetic Algorithm Based on "Stretching" Technique
Author :
Hong Lu ; Mu Zhichun
Author_Institution :
Sch. of Inf. Eng., Univ. of Sci. & Technol. Beijing, China
Abstract :
In order to enhance the global optimal search ability of AIA in solving multi-modal function optimization problem, based on mechanism of immune theory and genetic algorithm, a function "stretching" technique is introduced to obtain stretching immune genetic algorithm (SIGA). The algorithm equipped with the technique can narrow the range of extreme value of the objective function, so it reduces searching difficulty and improves the global convergence performance effectively. Using mathematical methods of Markov chains theory, it is proved theoretically that the SIGA is global convergent with probability 1. Simulation results indicate that the SIGA equipped with the "stretching" technique exhibits better convergent performance compared with traditional immune genetic algorithm.
Keywords :
Markov processes; artificial immune systems; genetic algorithms; Markov chains theory; artificial immune algorithm; multimodal function optimization problem; optimal search ability; stretching immune genetic algorithm; stretching technique; Convergence; Electronic mail; Frequency locked loops; Genetic algorithms; Genetic engineering; HDTV; Hafnium; Optimal control; Tellurium; US Department of Transportation; Artificial Immune Algorithm; Function "Stretching" Technique; Multi-Modal Function Optimization;
Conference_Titel :
Control Conference, 2006. CCC 2006. Chinese
Conference_Location :
Harbin
Print_ISBN :
7-81077-802-1
DOI :
10.1109/CHICC.2006.280707