Advertisement

Improvement in the Performance of Island Based Genetic Algorithms Through Path Relinking

  • Luis delaOssa
  • José A. Gámez
  • José M. Puerta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4030)

Abstract

In island based genetic algorithms, the population is splitted into subpopulations which evolve independently and ocasionally communicate by sending some individuals. This way, several zones of the landscape are explored in parallel and solutions with different features can be discovered. The interchange of information is a key point for the performance of these algorithms, since the combination of those solutions usually produces better ones. In this work, it is proposed a method based in path relinking which makes the combination process more effective.

Keywords

Genetic Algorithm Scatter Search Island Model Parallel Genetic Algorithm Path Relinking 
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.
    Alba, E., Cotta, C., Troya, J.M.: Numerical and real time analysis of parallel distributed gas with structured and panmictic populations. In: Proceedings of the IEEE Conference on Evolutionary Computing (CEC), vol. 2, pp. 1019–1026 (1999)Google Scholar
  2. 2.
    Alba, E., Troya, J.M.: An analysis of synchronous and asynchronous parallel distributed genetic algorithms with structured and panmictic islands. In: IPPS/SPDP Workshops, pp. 248–256 (1999)Google Scholar
  3. 3.
    Cantú-Paz, E., Goldberg, D.E.: Are multiple runs of genetic algorithms better than one? In: Proceedings of the Genetic and Evolutionary Computation Conference 2003 (2003)Google Scholar
  4. 4.
    Cantú-Paz, E.: A survey of parallel genetic algorithms. Technical Report IlliGAL-97003, Illinois Genetic Algorithms Laboratory. University of Illinois at Urbana-Champaign (1997)Google Scholar
  5. 5.
    Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  6. 6.
    Cotta, C.: Scatter search with path relinking for phylogenetic inference. European Journal of Operational Research 169(2), 520–532 (2006)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Whitley, D., Rana, S., Heckendorm, R.B.: The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology 1(7), 33–47 (1999)Google Scholar
  8. 8.
    Laguna, M., Glover, F., Martí, R.: Fundamentals of scatter search and path relinking. Control and Cybernetics 3(29), 653–684 (2000)Google Scholar
  9. 9.
    Glover, F.: Tabu search - part i. ORSA Journal of Computing 1, 190–206 (1989)MATHMathSciNetGoogle Scholar
  10. 10.
    Glover, F.: Scatter search and path relinking. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 297–316. McGraw-Hill, New York (1999)Google Scholar
  11. 11.
    Goldberg, D.E.: Genetic algorithms in search, optimiation and machine learning. Addison-Wesley, New York (1989)Google Scholar
  12. 12.
    Goldberg, D.E., Deb, K., Clark, J.H.: Genetic algorithms, noise, and the sizing of populations. Complex Systems 6, 333–362 (1992)MATHGoogle Scholar
  13. 13.
    Grefenstette, J.J.: Parallel adaptive algorithms for function optimization. Technical Report CS-81-19, Computer Science Department, Vanderbilt University, Nashville, TN (1981)Google Scholar
  14. 14.
    Grosso, P.B.: Competent simulations of genetic adaptation: Parallel subcomponent interaction in a multilocus model. PhD thesis, University of Michigan (1985)Google Scholar
  15. 15.
    Harik, G., Cantú-Paz, E., Goldberg, D.E., Miller, B.: The gampler’s ruin problem, genetic algorithms, and the sizing of populations. In: Proceedings of the Fourth International Conference on Evolutionary Computation, pp. 7–12. IEEE Press, Los Alamitos (1997)Google Scholar
  16. 16.
    Tolla, P., Alfandari, L., Plateau, A.: Metaheuristics: Computer Decision-Making. In: chapter A path-relinking algorithm for the generalized assignment problem, pp. 1–18. Kluwer Academic Publishers, Dordrecht (2004)Google Scholar
  17. 17.
    Laguna, M., Martí, R.: Grasp and path relinking for 2-layer straight line crossing minimization. INFORMS Journal on Computing 11(1), 44–52 (1999)MATHCrossRefGoogle Scholar
  18. 18.
    José, A., de la Ossa, G.L., Puerta, J.M.: Improving model combination through local search in parallel univariate edas. In: José, A. (ed.) IEEE Congress on Evolutionary Computation, CEC2005, Edinburgh, Scotland, September 2005, vol. 2, pp. 1426–1433. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  19. 19.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)MATHGoogle Scholar
  20. 20.
    Skolicki, Z., De Jong, K.: The influence of migration sizes and intervals on island models. In: Proceedings of Genetic and Evolutionary Computation Conference, GECCO 2005, pp. 1295–1302. ACM Press, New York (2005)CrossRefGoogle Scholar
  21. 21.
    Whitle, D., Rana, S., Heckendorn, R.B.: Island model genetic algorithms and linearly separable problems. In: Corne, D.W. (ed.) AISB-WS 1997. LNCS, vol. 1305, pp. 109–125. Springer, Heidelberg (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Luis delaOssa
    • 1
  • José A. Gámez
    • 1
  • José M. Puerta
    • 1
  1. 1.Intelligent Systems and Data Mining Group, Computer Systems DepartmentUniversity of Castilla-La ManchaAlbaceteSpain

Personalised recommendations