DocumentCode
3298301
Title
Research on multi-object clonal genetic algorithm for the 0-1 knapsack
Author
Xing, Wang ; Wenpeng, Zhang
Author_Institution
Software Dept., Nanyang Normal Univ., Nanyang, China
fYear
2010
fDate
25-27 June 2010
Firstpage
82
Lastpage
85
Abstract
Immune clonal algorithm (ICA) is applied to genetic algorithm (GA) to develop a class of multi-object clonal genetic algorithm (MOCGA) for combinatorial optimization. With the condition of preserving simulated annealing advantages, MOCGA takes advantage of ICA algorithm so as to avoid premature convergence. To demonstrate its effectiveness and applicability, experiments are carried out on the 0-1 knapsack problem. The results show that MOCGA performs well, without premature convergence as compared to GA.
Keywords
Biological cells; Convergence; Costs; Educational technology; Genetic algorithms; Helium; Independent component analysis; Simulated annealing; Software algorithms; Testing; genetic algorithm; knapsack problem; multi-object clonal genetic algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Educational and Network Technology (ICENT), 2010 International Conference on
Conference_Location
Qinhuangdao, China
Print_ISBN
978-1-4244-7660-2
Electronic_ISBN
978-1-4244-7662-6
Type
conf
DOI
10.1109/ICENT.2010.5532132
Filename
5532132
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