Advertisement

On Some Properties of the B-Cell Algorithm in Non-Stationary Environments

  • Krzysztof Trojanowski
  • Sławomir T. Wierzchoń
Conference paper

Abstract

Mammalian immune system and especially clonal selection principle, responsible for coping with external intruders, is an inspiration for a set of heuristic optimization algorithms. In this paper we focus our attention on an instance of a clonal selection algorithm called BCA. This algorithm admits very good exploratory abilities when solving stationary optimization problems. We try to explain this capability studying the behavior of the mutation operator being distinguishable feature of BCA. Later we apply it to solve non-stationary optimization problem.

Keywords

Mutation Operator Clonal Selection Artificial Immune System Clonal Selection Algorithm Heuristic Optimization Algorithm 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    T. Blackwell. Particle swarm optimization in dynamic environments. URL: http://igor.gold.ac.uk/∼mas01tb/papers/PSOdynenv.pdfGoogle Scholar
  2. [2]
    J. Branke. The moving peaks benchmark. URL: http://www.aifb.uni-karlsruhe.de/ jbr/MovPeaks/movpeaks/.Google Scholar
  3. [3]
    J. Branke. Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers, 2002.Google Scholar
  4. [4]
    H.G. Cobb and J.J. Grefenstette. The immune learning mechanisms: Reinforcement and recruitment and their applications. In Proceedings of the Fifth International Conference on Genetic Algorithms. Morgan Kaufmann, 1993.Google Scholar
  5. [5]
    L. N. de Castro. Immune, swarm, and evolutionary algorithms. part 1: Basic concepts, part 2: Philosophical comparisons. In: Proc. of Internat. Conf. on Neural Info. Processing, ICONIP, Workshop on Artificial Immune Systems, 2002, vol. 3, pp. 1464–1473.Google Scholar
  6. [6]
    L. N. de Castro and J. Timmis. Artificial Immune Systems: A New Computational Intelligence Approach. Springer-Verlag, 2002.Google Scholar
  7. [7]
    A. Gaspar and P. Collard. From GAs to artificial immune systems: Improving adaptation in time dependent optimization. In Proc. of the Congress on Evolutionary Computation, vol. 3, IEEE Press, Piscataway, NJ, 1999, pp 1859–1866.Google Scholar
  8. [8]
    M. Guntsch, M. Middendorf, H.Schmeck. An ant colony optimization approach to dynamic TSP. In Proc. Genetic Evol. Comput. Conf. Morgan Kaufmann 2001, pp. 860-867Google Scholar
  9. [9]
    Y. Jin, J. Branke. Evolutionary optimization in uncertain environments – A survey. IEEE Trans. on Evolutionary Computation, 4(2005)303-317CrossRefGoogle Scholar
  10. [10]
    J. Kelsey and J. Timmis. Immune inspired somatic contiguous hypermutation for function optimisation. In Genetic Evol. Comput. Conf – GECCO 2003, Springer, 2003, pp. 207–218.Google Scholar
  11. [11]
    R.W. Morrison. Designing Evolutionary Algorithms for Dynamics Environments. Natural Computing Series. Springer, 2002.Google Scholar
  12. [12]
    R. W. Morrison and K. A. De Jong. A test problem generator for non-stationary environments. In Proc. of the Congress on Evolutionary Computation, vol. 3, IEEE Press, Piscataway, NJ, 1999, pp. 1859–1866.Google Scholar
  13. [13]
    K. Trojanowski. Clonal selection principle based approach to non-stationary optimization tasks. In The 9 th National Conference on Evolutionary Computation and Global Optimisation. Oficyna Wydawnicza Politechniki Warszawskiej, Warszawa, 2006.Google Scholar
  14. [14]
    K. Trojanowski and Z. Michalewicz. Searching for optima in non–stationary environments. In Proc. of the Congress on Evolutionary Computation, vol. 3, IEEE Press, Piscataway, NJ, 1999, pp. 1843–1850.Google Scholar
  15. [15]
    K. Trojanowski and S. T. Wierzchoń. Studying properties of multipopulation heuristic approach to nonstationary optimisation tasks. In IIS 2003: Intelligent Information Processing and Web Mining, Springer, 2003, pp. 23–32.Google Scholar
  16. [16]
    K. Trojanowski and S. T. Wierzchoń. A comparison of clonal selection based algorithms for non-stationary optimisation tasks. In IIPWM 2006: Intelligent Information Processing and Web Mining, Advances in Soft Computing 5, Springer, 2006, pp. 41–52.Google Scholar
  17. [17]
    S.T. Wierzchoń. Function optimization by the immune metaphor. Task Quarterly, 6(2002)493–508.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Krzysztof Trojanowski
    • 1
  • Sławomir T. Wierzchoń
    • 1
  1. 1.Institute of Computer Sci., Polish Acad. of SciencesOrdona 21Poland

Personalised recommendations