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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)

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.

Keywords

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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References

  1. 1.
    De Castro, L.N., Von Zuben, F.J.: The Clonal Selection Algorithm with Engineering Applications[C]. In: Proceedings of GECCO 2000, Workshop on Artificial Immune Systems and Their Applications, pp. 36–37 (2000)Google Scholar
  2. 2.
    Kim, J., Bentley, P.J.: Towards an Artificial Immune System for Network Intrusion Detection: An Investigation of Dynamic Clonal Selection. In: Proceedings of Congress on Evolutionary Computation 2002, pp. 1015–1020 (2000)Google Scholar
  3. 3.
    Liu, R., Du, H., Jiao, L.: Immunity Clonal Strategies. In: Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2003), p. 290 (2003)Google Scholar
  4. 4.
    Dasgupta, D., et al.: Artificial immune systems in industrial applications. In: IPMM 1999. Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, pp. 257–267. IEEE press, Los Alamitos (1999)CrossRefGoogle Scholar
  5. 5.
    de Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems 6(3), 239–251 (2002)Google Scholar
  6. 6.
    Cooper, K.D., et al.: Procedure Cloning. In: Proceedings of the 1992 International Conference on Computer Languages, pp. 96–105 (1992)Google Scholar
  7. 7.
    Balazinska, M., et al.: Advanced clone-analysis to support object-oriented system refactoring. In: Proceedings: Seventh Working Conference on Reverse Engineering, pp. 98–107 (2000)Google Scholar
  8. 8.
    Esmaili, N., et al.: Behavioural cloning in control of a dynamic system. In: IEEE International Conference on Systems, Man and Cybernetics Intelligent Systems for the 21st Century, vol. 3, pp. 2904–2909 (1995)Google Scholar
  9. 9.
    Hybinette, M., et al.: Cloning: A Novel Method for Interactive Parallel Simulation. In: Proceedings of the 1997 Winter Simulation Conference, pp. 444–451 (1997)Google Scholar
  10. 10.
    Li, M., Kou, J., Lin, D., Li, S.: Principles and Applications of Genetic Algorithm [M], pp. 399–403. Science press (2002)Google Scholar

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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