A Novel Immune Clonal Algorithm

  • Yangyang Li
  • Fang Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


This paper proposes a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QICA is also characterized by the representation of the antibody (individual), the evaluation function, and the population dynamics. However, in QICA, antibody is proliferated and divided into a set of subpopulation groups. Antibodies in a subpopulation group are represented by multi-state gene quantum bits. In the antibody’s updating, the scabilitable quantum rotation gate strategy and dynamic adjusting angle mechanism are applied to guide searching. Theoretical analysis has proved that QICA converges to the global optimum. Some simulations are given to illustrate its efficiency and better performance than its counterpart.


Clonal Selection Artificial Immune System Clonal Size Antibody Population Clonal Selection Theory 
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  1. 1.
    De Castro, L.N., Von Zuben, F.J.: Artificial Immune Systems: Part II—A Survey of Applications. FEEC/Univ. Campinas, Campinas, Brazil (2000), [Online] Available:
  2. 2.
    Moore, M., Narayanan, A.: Quantum-Inspired Computing. Dept. Comput. Sci., Univ. Exeter, Exeter, UK (1995)Google Scholar
  3. 3.
    Li, Y.Y., Jiao, L.C.: Quantum-Inspired Immune Clonal Algorithm. In: Jacob, C., Pilat, M.L., Bentley, P.J., et al. (eds.) Proceedings of the 4th International Conference on Artificial Immune Systems, Banff, Alberta, Canada, August 2005, pp. 304–317 (2005)Google Scholar
  4. 4.
    Burnet, F.M.: Clonal Selection and After. In: Bell, G.I., Perelson, A.S., Pimbley Jr., G.H. (eds.) Theoretical Immunology, pp. 63–85. Marcel Dekker Inc., New York (1978)Google Scholar
  5. 5.
    Du, H.F., Jiao, L.C., Wang, S.A.: Clonal Operator and Antibody Clone Algorithms. In: Shichao, Z., Qiang, Y., Chengqi, Z. (eds.) Proceedings of the First International Conference on Machine Learning and Cybernetics, pp. 506–510. IEEE, Beijing (2002)Google Scholar
  6. 6.
    Runarsson, T.P., Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Trans. Evol. Comput. 4(3), 284–294 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yangyang Li
    • 1
  • Fang Liu
    • 2
  1. 1.Institute of Intelligent Information ProcessingXidian UniversityXi’anChina
  2. 2.Computer schoolXidian UniversityXi’anChina

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