Clonal Selection Approach with Mutations Based on Symmetric α-Stable Distributions for Non-stationary Optimization Tasks

  • Krzysztof Trojanowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)

Abstract

Efficiency of two mutation operators applied in a clonal selection based optimization algorithm AIIA for non-stationary tasks is investigated. In both operators traditional Gaussian random number generator was exchanged by α-stable random number generator and thus α became one of the parameters of the algorithm. Obtained results showed that appropriate tuning of the α parameter allows to outperform the results of algorithms with the traditional operators.

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References

  1. 1.
    Branke, J.: The moving peaks benchmark, http://www.aifb.uni-karlsruhe.de/~jbr/MovPeaks/movpeaks/
  2. 2.
    Branke, J.: Memory enhanced evolutionary algorithm for changing optimization problems. In: Proc. of the Congress on Evolutionary Computation, vol. 3, pp. 1875–1882. IEEE Computer Society Press, Piscataway (1999)Google Scholar
  3. 3.
    de Castro, L.N., Timmis, J.: An artificial immune network for multimodal function optimization. In: Proc. of the Congress on Evolutionary Computation, vol. 1, pp. 699–704. IEEE Press, Piscataway (2002)Google Scholar
  4. 4.
    Gutowski, M.: Levy flights as an underlying mechanizm for a global optimization algorithm. In: Proc. 5th National Conf. on Evolutionary Computation and Global Optimisation, pp. 79–86. Warsaw Univ. of Technology Publishing House, Warsaw (2001)Google Scholar
  5. 5.
    Lee, C.-Y., Yao, X.: Evolutionary programming using mutations based on the levy probability distribution. IEEE Trans. on Evolutionary Computation 8(1), 1–13 (2004)CrossRefGoogle Scholar
  6. 6.
    Morrison, R.W., De Jong, K.A.: A test problem generator for non-stationary environments. In: Proc. of the Congress on Evolutionary Computation, vol. 3, pp. 1859–1866. IEEE Press, Piscataway (1999)Google Scholar
  7. 7.
    Obuchowicz, A., Pretki, P.: Phenotypic evolution with a mutation based on symmetric α-stable distributions. Int. J. Appl. Math. Comput. Sci. 14(3), 289–316 (2004)MATHMathSciNetGoogle Scholar
  8. 8.
    Purchla, M., Malanowski, M., Terlecki, P., Arabas, J.: Experimental comparison of repair methods for box constraints. In: Proc. 7th National Conf. on Evolutionary Computation and Global Optimisation, pp. 135–142. Warsaw Univ. of Technology Publishing House, Warsaw (2004)Google Scholar
  9. 9.
    Trojanowski, K.: Clonal selection principle based approach to non-stationary optimization tasks. In: Proc. 9th National Conf. on Evolutionary Computation and Global Optimisation. Prace Naukowe, Elektronika, vol. 156, pp. 375–384. Warsaw Univ. of Technology Publishing House, Warsaw (2006)Google Scholar
  10. 10.
    Trojanowski, K., Michalewicz, Z.: Searching for optima in non–stationary environments. In: Proc. of the Congress on Evolutionary Computation, vol. 3, pp. 1843–1850. IEEE Press, Piscataway (1999)Google Scholar
  11. 11.
    Trojanowski, K., Wierzchoń, S.T.: Studying properties of multipopulation heuristic approach to non-stationary optimisation tasks. In: IIS 2003:Intelligent Information Processing and Web Mining. Advances in Soft Computing, pp. 23–32. Springer, Heidelberg (2003)Google Scholar
  12. 12.
    Wierzchoń, S.T.: Function optimization by the immune metaphor. Task Quarterly 6(3), 493–508 (2002)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Krzysztof Trojanowski
    • 1
  1. 1.Institute of Computer Science, Polish Academy of Sciences, Ordona 21, 01-237 WarsawPoland

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