Multimodal Function Optimization Using Species Conservation

  • Márton-Ernő Balázs
  • Li Jianping
  • Geoffrey T. Parks
  • P. John Clarkson
Conference paper

Abstract

This paper introduces species conservation, a new technique for evolving parallel sub-populations. The technique uses a combination of a local mating scheme and distributed elitism to produce parallel convergence to several solutions of a multimodal optimization problem. The primary purpose of the paper is to demonstrate the effectiveness of species conservation in solving complex multimodal optimization problems. For this purpose some experimental results are presented.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    D. Beasley, D.R. Bull and R.R. Martin, “A Sequential Niche Technique for Multimodal Function optimization”, Evolutionary Computation, No 2, Vol 1, pp. 101–125, 1993.CrossRefGoogle Scholar
  2. [2]
    Y. Davidor, “A Naturally Occurring Niche and Species Phenomenon: The Model and First results”, Proceedings of the Fourth International Conference on Genetic Algorithms, University of San Diego, pp. 257–263, 1991.Google Scholar
  3. [3]
    K. Deb and D.E. Goldberg, „An Investigate of Niche and Species Formation in Genetic Function Optimization“, Proceedings of the Third International Conference on Genetic Algorithms, George Mason University, pp. 42–50, 1989.Google Scholar
  4. [4]
    K.A. De Jong.. „An analysis of behavior of a class of genetic adaptive systems“, Doctoral Dissertation, University of Michigan, Dissertation Abstracts International 36(10), 5240B, 1975.Google Scholar
  5. [5]
    F. Glover and M. Laguna, Tabu Search, Kluwer Academic Publishers, 1998.Google Scholar
  6. [6]
    D.E. Goldberg and J. Richardson, „Genetic algorithms with sharing for multimodal function optimization“, Proceedings of the second International Conference on Genetic Algorithm, pp.41–49, 1987.Google Scholar
  7. [7]
    D.E. Goldberg, Genetic Algorithms in search, optimization and machine learning, Reading, Addison Wesley, 1989.Google Scholar
  8. [8]
    J.H. Holland, Adaptation in natural and artificial systems, University of Michigan Press, 1975.Google Scholar
  9. [9]
    O.J. Mengshoel and D.E. Goldberg, “Probability Crowding: Deterministic Crowding with Probabilistic Replacement”, Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida. pp. 409–416, 1999.Google Scholar
  10. [10]
    Y. Michalewicz, Genetic Algorithms + Data Structures = Evolutionary Programs, Springer-Verlag Berlin Heidelberg, New York, 1996.CrossRefGoogle Scholar
  11. [11]
    R. Storn and K. Price, „Differential Evolution — a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces”, Journal of Global Optimization, Kluwer Academic Publishers, Vol. 11, pp. 341–359,1997.MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Márton-Ernő Balázs
    • 2
  • Li Jianping
    • 1
  • Geoffrey T. Parks
  • P. John Clarkson
    • 2
  1. 1.Shijiazhuang Mechanical CollegeShijiazhuangP.R.China
  2. 2.Engineering Design Centre, Engineering DepartmentCambridge UniversityUK

Personalised recommendations