Improving Evolutionary Algorithms with Multi-representation Island Models

  • Zbigniew Skolicki
  • Kenneth De Jong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)

Abstract

We present an island model that uses different representations in each island. The model transforms individuals from one representation to another during migrations. We show that such a model helps the evolutionary algorithm to escape from local optima and to solve problems that are difficult for single representation EAs. We illustrate this approach with a two population island model in which one island uses a standard binary encoding and the other island uses a standard reflective Gray code. We compare the performance of this multi-representation island model with single population EAs using only binary or Gray codes. We show that, on a variety of difficult multi-modal test functions, the multi-representation island model does no worse than a standard EA on all of the functions, and produces significant improvements on a subset of them.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Wright, S.: The roles of mutation, inbreeding, crossbreeding and selection in evolution. In: Jones, D.F. (ed.) Proceedings of the Sixth International Conference of Genetics, Brooklyn Botanic Garden, pp. 356–366 (1932)Google Scholar
  2. 2.
    Grosso, P.: Computer Simulations of Genetic Adaptation: Parallel Subcomponent Interaction in a Multilocus Model. PhD thesis, University of Michigan, Ann Arbor, MI (1985)Google Scholar
  3. 3.
    Gordon, V., Whitley, D., Bohn, A.: Dataflow parallelism in genetic algorithms. In: Manner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, vol. 2, pp. 542–553. Elsevier Science, Amsterdam (1992)Google Scholar
  4. 4.
    Manderick, B., Spiessens, P.: Fine-grained parallel genetic algorithms. In: Schaffer, J.D. (ed.) Proceedings of the Third Int. Conf. on Genetic Algorithms, p. 428. Morgan Kauffman, San Francisco (1989)Google Scholar
  5. 5.
    Mühlenbein, H.: Parallel genetic algorithms, population genetic and combinatorial optimization. In: Schaffer, J. (ed.) Proceedings on the Third International Conference on Genetic Algorithms, pp. 416–421. Morgan Kaufmann, San Francisco (1989)Google Scholar
  6. 6.
    Sarma, J.: An Analysis of Decentralized and Spatially Distributed Genetic Algorithms. PhD thesis, George Mason University, Fairfax, VA (1998)Google Scholar
  7. 7.
    Cantú-Paz, E.: Migration policies, selection pressure, and parallel evolutionary algorithms. Journal of Heuristics 7, 311–334 (2001)MATHCrossRefGoogle Scholar
  8. 8.
    Whitley, D., Rana, S., Heckendorn, R.B.: The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology 7, 33–47 (1999)Google Scholar
  9. 9.
    Toussaint, M.: Self-adaptive exploration in evolutionary search. Technical Report IRINI-2001-05, Institute for Neuroinformatics, Ruhr-University Bochum (2001)Google Scholar
  10. 10.
    Toussaint, M., Igel, C.: Neutrality: A necessity for self-adaptation. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2002), pp. 1354–1359 (2002) Google Scholar
  11. 11.
    Eby, D., Averill, R., Goodman, E., Punch, W.: The optimization of flywheels using an injection island genetic algorithm. In: Bentley, P. (ed.) Evolutionary Design by Computers, pp. 167–190. Morgan Kaufmann, San Francisco (1999)Google Scholar
  12. 12.
    Rana, S., Whitley, L.: Bit representation with a twist. In: Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA 1997), Morgan Kaufmann, San Francisco (1997)Google Scholar
  13. 13.
    Barbulescu, L., Watson, J.P., Whitley, D.: Dynamic representations and escaping local optima: Improving genetic algorithms and local search. In: AAAI/IAAI, pp. 879–884 (2000) Google Scholar
  14. 14.
    Rowe, J., Whitley, D., Barbulescu, L., Watson, J.P.: Properties of gray and binary representations. Evolutionary Computation 12, 47–76 (2004)CrossRefGoogle Scholar
  15. 15.
    Whitley, D.L., Rana, S., Heckendorn, R.B.: Representation issues in neighborhood search and evolutionary algorithms. In: Quagliarelli, D., Periaux, J., Poloni, C., Winter, G. (eds.) Genetic Algorithms in Engineering and Computer Science, pp. 39–57. John Wiley & Sons Ltd, Chichester (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Zbigniew Skolicki
    • 1
  • Kenneth De Jong
    • 1
  1. 1.George Mason UniversityFairfaxUSA

Personalised recommendations