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.
Supported by the National Natural Science Foundation of China under Grant Nos. 60372045, 60133010 and the National Grand Fundamental Research 973 Program of China under Grant No.2001CB309403.
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Qi, Y., Pan, X., Liu, F., Jiao, L. (2006). A Strategy of Mutation History Learning in Immune Clonal Selection Algorithm. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_10
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DOI: https://doi.org/10.1007/11903697_10
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