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A Markov Chain Model of the B-Cell Algorithm

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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

An exact Markov chain model of the B-cell algorithm (BCA) is constructed via a novel possible transit method. The model is used to formulate a proof that the BCA is convergent absolute under a very broad set of conditions. Results from a simple numerical example are presented, we use this to demonstrate how the model can be applied to increase understanding of the performance of the BCA in optimizing function landscapes as well as giving insight into the optimal parameter settings for the BCA.

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References

  1. Kelsey, J., Timmis, J.: Immune Inspired Somatic Contiguous Hypermutation for Function Optimisation. In: CantuPaz, et al. (eds.) Proc. of Genetic and Evolutionary Computation Conference (GECCO). LNCS, vol. 2723, pp. 207–218. Springer, Heidelberg (2003)

    Google Scholar 

  2. Kelsey, J., Timmis, J., Hone, A.: Chasing Chaos. In: Sarker, R., Reynolds, R., Abbass, H., Kay-Chen, T., McKay, R. (eds.) Proceedings of the Congress on Evolutionary Computation, Canberra, Australia, December 2003, pp. 413–419. IEEE, Los Alamitos (2003)

    Google Scholar 

  3. Rosin-Arbesfeld, R., Townsley, F., Bienz, M.: The APC tumour suppressor has a nuclear export function. Letters to Nature 406, 1009–1012 (2000)

    Article  Google Scholar 

  4. Hone, A., Kelsey, J.: Optima, extrema, and artificial immune systems. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 80–90. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  5. Farmer, J.D., Packard, N.H., Perelson, A.S.: The Immune System, Adaptation, and Machine Learning. Physica D 22, 187–204 (1986)

    Article  MathSciNet  Google Scholar 

  6. Grimmett, G.R., Stirzaker, D.R.: Probability and Random Processes. Oxford University Press, Oxford (1982)

    MATH  Google Scholar 

  7. Villalobos-Arias, M., Coello Coello, C.A., Hernández-Lerma, O.: Convergence analysis of a multiobjective artificial immune system algorithm. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 226–235. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Seneta, E.: Non-Negative Matrices and Markov Chains. Springer, New York (1981)

    MATH  Google Scholar 

  9. Nix, A.E., Vose, M.D.: Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence 5, 79–88 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  10. Vose, M.D.: Modeling Simple Genetic Algorithms. Evolutionary Computation 3(4) (1996)

    Google Scholar 

  11. De Jong, K.A., Spears, W.M., Gordon, D.F.: Using Markov Chains to Analyze GAFOs. In: Proceedings of FOGA 1994, Estes Park, CO, pp. 115–137. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  12. de Castro, L., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems 6, 239–251 (2002)

    Google Scholar 

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

    MATH  Google Scholar 

  14. Cutello, V., Nicosia, G., Pavone, M.: Exploring the capability of immune algorithms: A characterization of hypermutation operators. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds.) ICARIS 2004. LNCS, vol. 3239, pp. 263–276. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  15. Brzezniak, Z., Zastawniak, T.: Basic Stochastic Processes. Springer, Heidelberg (1999)

    MATH  Google Scholar 

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© 2005 Springer-Verlag Berlin Heidelberg

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Clark, E., Hone, A., Timmis, J. (2005). A Markov Chain Model of the B-Cell Algorithm. 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_24

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  • DOI: https://doi.org/10.1007/11536444_24

  • 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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