Lamarckian clonal selection algorithm with application

Authors:
Wuhong He;Haifeng Du;Licheng Jiao;Jing Li
Affiliations:
Institute of Intelligent Information Processing and, National Key Lab of Radar Signal Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing and, National Key Lab of Radar Signal Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing and, National Key Lab of Radar Signal Processing, Xidian University, Xi’an, China;Institute of Intelligent Information Processing and, National Key Lab of Radar Signal Processing, Xidian University, Xi’an, China
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Year:
2005

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Abstract

In this paper, Lamarckism and Immune Clonal Selection Theory are integrated to form a new algorithm, Lamarckian Clonal Selection Algorithm (LCSA). In the novel algorithm, the idea that Lamarckian evolution described how organism can evolve through learning, namely the point of “Gain and Convey” is applied, then this kind of learning mechanism is introduced into Standard Clonal Selection Algorithm (SCSA). In the experiments, ten benchmark functions are used to test the performance of LCSA, and the impact of parameters for LCSA is studied with great care. Compared with SCSA and the relevant evolutionary algorithm, LCSA is more robust and has better convergence.