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The Quaternion Model of Artificial Immune Response

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Artificial Immune Systems (ICARIS 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3627))

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Abstract

A quaternion model of artificial immune response (AIR) is proposed in this paper. The model abstracts four elements to simulate the process of immune response, namely, antigen, antibody, rules of interaction among antibodies, and the drive algorithm describing how the rules are applied to antibodies. Inspired by the biologic immune system, we design the set of rules as three subsets, namely, the set of clonal selection rules, the set of immunological memory rules, and the set of immunoregulation rules. An example of the drive algorithm is given and a sufficient condition of its convergence is deduced.

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References

  1. Farmer, J.D., Packard, N.H., Perelson, A.S.: The immune system, adaptation, and machine learning. Physica D 2(1-3), 187–204 (1986)

    Article  MathSciNet  Google Scholar 

  2. Dasgupta, D., Forrest, S.: Artificial immune systems in industrial applications. In: Proceedings of the Second International Conference on Intelligent Processing and Manufacturing of Materials, Hawaii, USA, July 10-15, pp. 257–267 (1999)

    Google Scholar 

  3. Dasgupta, D.: Artificial Neural Networks and Artificial Immune Systems: Similarities and Differences. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Orlando, USA, October 12-15, vol. 1 (1997)

    Google Scholar 

  4. Gasper, A., Collard, P.: From GAs to artificial immune systems: improving adaptation in time dependent optimization. In: Proceedings of the Congress on Evolutionary Computation (CEC 1999), pp. 1859–1866. IEEE Press, Los Alamitos (1999)

    Chapter  Google Scholar 

  5. de Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  6. Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Heidelberg (1999)

    MATH  Google Scholar 

  7. Abbas, A.K., Lichtman, A.H., Pober, J.S.: Cellular and Molecular Immunology, 4th edn. W.B. Saunders Co., New York (2000)

    Google Scholar 

  8. Dittrich, P., Ziegler, J., Banzhaf, W.: Artificial Chemistries–A Review. Artificial Life 7, 225–275 (2001)

    Article  Google Scholar 

  9. Tarakanov, A.O., Skormin, V.A., Sokolova, S.P.: Immunocomputing: Principles and Applications. Springer, Heidelberg (2003)

    MATH  Google Scholar 

  10. 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 

  11. Kim, J., Bentley, P.J.: Towards an Artificial Immune System for Network Intrusion Detection: an Investigation of Clonal Selection with a Negative Selection Operator. In: IEEE Neural Networks Council. Proceedings of the 2001 Congress on Evolutionary Computation, Seoul Korea, vol. 2, pp. 1244–1252. IEEE, Los Alamitos (2001)

    Google Scholar 

  12. Jiao, L., Wang, L.: A novel genetic algorithm based on immunity. IEEE Transactions on Systems, Man and Cybernetics, Part A 30(5), 552–561 (2000)

    Article  Google Scholar 

  13. Gong, M., Du, H., Jiao, L., Wang, L.: Immune Clonal Selection Algorithm for Multiuser Detection in DS-CDMA Systems. In: Webb, G.I., Yu, X. (eds.) Advances in Artificial Intelligence: Proceedings of the 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, pp. 1219–1225 (2004)

    Google Scholar 

  14. Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York (1996)

    MATH  Google Scholar 

  15. Wolpert, D.H., Macready, W.G.: No Free Lunch Theorems for Search. Santa Fe Institute Techinical Report SFI-TR-05-010, Santa Fe Institute, Santa Fe, NM (1995)

    Google Scholar 

  16. Forrest, S., Hofmeyr, S.A.: Immunology as information processing. In: Segel, L.A., Cohen, I. (eds.) Design Principles for the Immune System and Other Distributed Autonomous Systems. Santa Fe Institute Studies in the Sciences of Complexity. Oxford University Press, New York (2001)

    Google Scholar 

  17. Warrender, C., Forrest, S., Legal, L.: Effective Feedback in the Immune System. In: Genetic and Evolutionary Computation Conference Workshop Program, pp. 329–332. Morgan Kaufman, San Francisco (2001)

    Google Scholar 

  18. Hofmeyr, S., Forrest, S.: Immunity by Design: An Artificial Immune System. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 1289–1296. Morgan-Kaufmann, San Francisco (1999)

    Google Scholar 

  19. Jiao, L.C., Gong, M.G., Shang, R.H., DU, H.F., Lu, B.: Clonal Selection with Immune Dominance and Anergy Based Multiobjective Optimization. In: Coello Coello, C.A., Hernández Aguirre, A., Zitzler, E. (eds.) EMO 2005. LNCS, vol. 3410, pp. 474–489. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  20. Du, H.F., Gong, M.G., Jiao, L.C., Liu, R.C.: A novel artificial immune system algorithm for high-dimensional function numerical optimization. Progress In Natural Science 15(5), 463–471 (2005)

    Article  MATH  MathSciNet  Google Scholar 

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Gong, M., Jiao, L., Liu, F., Du, H. (2005). The Quaternion Model of Artificial Immune Response. In: Jacob, C., Pilat, M.L., Bentley, P.J., Timmis, J.I. (eds) Artificial Immune Systems. ICARIS 2005. Lecture Notes in Computer Science, vol 3627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11536444_16

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  • DOI: https://doi.org/10.1007/11536444_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28175-7

  • Online ISBN: 978-3-540-31875-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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