Multiple Populations Guided by the Constraint-Graph for CSP

  • Arturo Nuñez
  • María-Cristina Riff
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1952)

Abstract

In this paper we examine the gain of the performance obtained using multiple populations - that evolve in parallel - of the constraintgraph based evolutionary algorithm (in its dynamic adaptation operators version) with a migration policy. We show that a multiple populations approach outperforms a single population implementation when applying it to the 3-coloring problem. We also evaluate various migration policies.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Adamis, Review of parallel genetic algorithms, Dept. Elect. Comp. Eng, Aristitele Univ. Thessaloniki, Greece, Tech. Rep. 1994.Google Scholar
  2. [2]
    A. Beguelin, J. J. Dongarra, G. A. Geist, R. Manchek, and V. S. Sunderam, A Users’ Guide to PVM Parallel Virtual Machine, Oak Ridge National Laboratory, ORNL/TM-12187, September, 1994Google Scholar
  3. [3]
    Brelaz, New methods to color vertices of a graph. Communications of the ACM, 22,pp. 251–256, 1979.MATHCrossRefMathSciNetGoogle Scholar
  4. [4]
    Cheeseman P., Kanefsky B., Taylor W., Where the Really Hard Problems Are. Proceedings of IJCAI-91, pp. 163–169, 1991Google Scholar
  5. [5]
  6. [6]
    G. Dozier, J. Bowen, and Homaifar, “Solving Constraint Satisfaction Problems Using Hybrid Evolutionary Search,” IEEE Transactions on Evolutionary Computation, Vol. 2, No. 1, 1998.Google Scholar
  7. [7]
    A. E. Eiben, J. I. van Hemert, E. Marchiori, A.G. Steenbeek. Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function. Fifth International Conference on Parallel Problem Solving from Nature ( PPSN-V), LNCS 1498, pp. 196–205, 1998.Google Scholar
  8. [8]
    Kumar. Algorithms for constraint satisfaction problems:a survey. AI Magazine, 13(1):32–44, 1992.Google Scholar
  9. [9]
    Mackworth A.K., Consistency in network of relations. Artificial Intelligence, 8:99–118, 1977.MATHCrossRefGoogle Scholar
  10. [10]
    J. Paredis, Coevolutionary Algorithms, The Handbook of Evolutionary Computation, 1st supplement, BSck, T., Fogel, D., Michalewicz, Z. (eds.), Oxford University Press.Google Scholar
  11. [11]
    Riff M.-C., From Quasi-solutions to Solution: An Evolutionary Algorithm to Solve CSP. Constraint Processing (CP96), Ed. Eugene Freuder, pp. 367–381, 1996.Google Scholar
  12. [12]
    Riff M.-C., Evolutionary Search guided by the Constraint Network to solve CSP. Proc. of the Fourth IEEE Conf on Evolutionary Computation, Indianopolis, pp. 337–342, 1997.Google Scholar
  13. [13]
    Riff M.-C., A network-based adaptive evolutionary algorithm for CSP, In the book “Metaheuristics: Advances and Trends in Local Search Paradigms for Optimisation”, Kluwer Academic Publisher, Chapter 22, pp. 325–339, 1998.Google Scholar
  14. [14]
    Tsang, E. P. K., Wang, C. J., Davenport, A., Voudouris, C., Lau, T. L., A family of stochastic methods for constraint satisfaction and optimization, PACLP’99, London, pp. 359–383, 1999Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Arturo Nuñez
    • 1
  • María-Cristina Riff
    • 1
  1. 1.Computer Science DepartmentUniversidad Técnica Federico Santa MaríaValparaísoChile

Personalised recommendations