LAMARCKIAN LEARNING IN CLONAL SELECTION ALGORITHM FOR NUMERICAL OPTIMIZATION
2010 ◽
Vol 19
(01)
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pp. 19-37
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Keyword(s):
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.
2017 ◽
Vol 25
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pp. 1751-1761
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2012 ◽
pp. 218-231
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2011 ◽
pp. 390-399
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Keyword(s):
2012 ◽
Vol 6
(11)
◽
pp. 257-264
Keyword(s):
2011 ◽
Vol 6
(7)
◽
pp. 225-233
Keyword(s):