DocumentCode
1560956
Title
Discussion on the convergence rate of Immune Genetic Algorithm
Author
Luo, Xiaoping ; Wei, Wei
Author_Institution
Coll. of Electr. Eng., Zhejiang Univ., Hangzhou, China
Volume
3
fYear
2004
Firstpage
2275
Abstract
Markov chain was used in a new Immune Genetic Algorithm (IGA) modeling. The convergence rate to absorbed-state was got using norm. Next, the convergence rate to optimum was analyzed quantificationally, it was found that several parameters such as the population size, population distribution, mutation probability etc. would all affect the optimization and thus the results can be helpful in a study on how to improve the performance of IGA.
Keywords
Markov processes; artificial intelligence; convergence; genetic algorithms; statistical distributions; Markov chain; convergence rate; immune genetic algorithm modeling; mutation probability; optimization; population distribution; population size; Algorithm design and analysis; Artificial intelligence; Convergence; Design optimization; Educational institutions; Entropy; Genetic algorithms; Genetic mutations; Immune system; Performance analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN
0-7803-8273-0
Type
conf
DOI
10.1109/WCICA.2004.1341995
Filename
1341995
Link To Document