February 2010
International Journal on Artificial Intelligence Tools;Feb2010, Vol. 19 Issue 1, p19
Academic Journal
In this paper, we introduce Lamarckian learning theory into the Clonal Selection Algorithm and propose a sort of Lamarckian Clonal Selection Algorithm, termed as LCSA. The major aim is to utilize effectively the information of each individual to reinforce the exploitation with the help of Lamarckian local search. Recombination operator and tournament selection operator are incorporated into LCSA to further enhance the ability of global exploration. We compare LCSA with the Clonal Selection Algorithm in solving twenty benchmark problems to evaluate the performance of LCSA. The results demonstrate that the Lamarckian local search makes LCSA more effective and efficient in solving numerical optimization problems.


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