A Strategy of Mutation History Learning in Immune Clonal Selection Algorithm

  • Yutao Qi
  • Xiaoying Pan
  • Fang Liu
  • Licheng Jiao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


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.


Travel Salesman Problem Artificial Immune System Mutation Operation Clonal Selection Algorithm Network Intrusion Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yutao Qi
    • 1
  • Xiaoying Pan
    • 1
  • Fang Liu
    • 1
  • Licheng Jiao
    • 1
  1. 1.Institute of Intelligent Information Processing and National Key Lab of Radar Signal ProcessingXidian UniversityXi’anChina

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