A Strategy of Mutation History Learning in Immune Clonal Selection Algorithm.

Conference PaperinLecture Notes in Computer Science · January 2006with5 Reads
Impact Factor: 0.51 · DOI: 10.1007/11903697_10 · Source: DBLP
Conference: Simulated Evolution and Learning, 6th International Conference, SEAL 2006, Hefei, China, October 15-18, 2006, Proceedings


    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.