A strategy of mutation history learning in immune clonal selection algorithm

Authors:
Yutao Qi;Xiaoying Pan;Fang Liu;Licheng Jiao
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:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

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Abstract

A novel strategy termed as mutation history learning strategy (MHLS) is proposed in this paper. In MHLS, a vector called mutation memory is introduced for each antibody and a new type of mutation operation based on mutation memory is also designed. The vector of mutation memory is learned from a certain antibody's iteration history and used as guidance for its further evolution. The learning and usage of history information, which is absent from immune clonal selection algorithm (CSA), is shown to be an efficient measure to guide the direction of the evolution and accelerate algorithm's converging speed. Experimental results show that MHLS improves the performance of CSA greatly in dealing with the function optimization problems.