Advertisement

Substructural Neighborhoods for Local Search in the Bayesian Optimization Algorithm

  • Claudio F. Lima
  • Martin Pelikan
  • Kumara Sastry
  • Martin Butz
  • David E. Goldberg
  • Fernando G. Lobo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)

Abstract

This paper studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.

Keywords

Local Search Bayesian Network Memetic Algorithm Estimate Fitness Correct Linkage 
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.
    Larrañaga, P., Lozano, J.A. (eds.): Estimation of distribution algorithms: a new tool for Evolutionary Computation. Kluwer Academic Publishers, Boston (2002)MATHGoogle Scholar
  2. 2.
    Pelikan, M., Goldberg, D.E., Lobo, F.: A survey of optimization by building and using probabilistic models. Computational Optimization and Applications 21(1), 5–20 (2002); Also IlliGAL Report No. 99018MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical Report C3P 826, Caltech Concurrent Computation Program, California Institute of Technology, Pasadena, CA (1989)Google Scholar
  4. 4.
    Hart, W.E.: Adaptive global optimization with local search. PhD thesis, University of California, San Diego, San Diego, CA (1994)Google Scholar
  5. 5.
    Pelikan, M., Goldberg, D.E., Cantú-Paz, E.: BOA: The Bayesian Optimization Algorithm. In: Banzhaf, W., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference GECCO 1999, pp. 525–532. Morgan Kaufmann, San Francisco (1999); Also IlliGAL Report No. 99003Google Scholar
  6. 6.
    Pelikan, M.: Hierarchical Bayesian Optimization Algorithm: Toward a New Generation of Evolutionary Algorithms. Springer, Heidelberg (2005)MATHGoogle Scholar
  7. 7.
    Pearl, J.: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Morgan Kaufmann, San Mateo (1988)Google Scholar
  8. 8.
    Pelikan, M., Sastry, K.: Fitness inheritance in the Bayesian optimization algorithm. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 48–59. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Sastry, K., Goldberg, D.E.: Let’s get ready to rumble: Crossover versus mutation head to head. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 126–137. Springer, Heidelberg (2004); Also IlliGAL Report No. 2004005CrossRefGoogle Scholar
  10. 10.
    Sastry, K., Goldberg, D.E.: Designing competent mutation operators via probabilistic model building of neighborhoods. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3103, pp. 114–125. Springer, Heidelberg (2004); Also IlliGAL Report No. 2004006CrossRefGoogle Scholar
  11. 11.
    Harik, G.R.: Linkage learning via probabilistic modeling in the ECGA. IlliGAL Report No. 99010, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, Urbana, IL (1999)Google Scholar
  12. 12.
    Lima, C.F., Sastry, K., Goldberg, D.E., Lobo, F.G.: Combining competent crossover and mutation operators: A probabilistic model building approach. In: Beyer, H., et al. (eds.) Proceedings of the ACM SIGEVO Genetic and Evolutionary Computation Conference (GECCO 2005), ACM Press, New York (2005)Google Scholar
  13. 13.
    Handa, H.: The effectiveness of mutation operation in the case of estimation of distribution algorithms. Journal of Biosystems (to appear, 2006)Google Scholar
  14. 14.
    Etxeberria, R., Larrañaga, P.: Global optimization using Bayesian networks. In: Rodriguez, A., et al. (eds.) Second Symposium on Artificial Intelligence (CIMAF 1999), Habana, Cuba, pp. 332–339 (1999)Google Scholar
  15. 15.
    Deb, K., Goldberg, D.E.: Analyzing deception in trap functions. Foundations of Genetic Algorithms 2, 93–108 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Claudio F. Lima
    • 1
  • Martin Pelikan
    • 2
  • Kumara Sastry
    • 3
  • Martin Butz
    • 4
  • David E. Goldberg
    • 3
  • Fernando G. Lobo
    • 1
  1. 1.University of AlgarvePortugal
  2. 2.University of Missouri at St. LouisUSA
  3. 3.University of Illinois at Urbana-ChampaignUSA
  4. 4.University of WürzburgGermany

Personalised recommendations