Advertisement

An Ant-Based Rule for UMDA’s Update Strategy

  • C. M. Fernandes
  • C. F. Lima
  • J. L. J. Laredo
  • A. C. Rosa
  • J. J. Merelo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5778)

Abstract

This paper investigates an update strategy for the Univariate Marginal Distribution Algorithm (UMDA) probabilistic model inspired by the equations of the Ant Colony Optimization (ACO) computational paradigm. By adapting ACO’s transition probability equations to the univariate probabilistic model, it is possible to control the balance between exploration and exploitation by tuning a single parameter. It is expected that a proper balance can improve the scalability of the algorithm on hard problems with bounded difficulties and experiments conducted on such problems with increasing difficulty and size confirmed these assumptions. These are important results because the performance is improved without increasing the complexity of the model, which is known to have a considerable computational effort.

Keywords

Problem Size Loss Correction Bayesian Optimization Algorithm Univariate Marginal Distribution Algorithm Complex EDAs 
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.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: from natural to artificial systems. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar
  2. 2.
    Branke, J., Lode, C., Shapiro, J.: Adressing sampling errors and diversity loss in UMDA. In: 2007 Genetic and Evolutionary Computation Conference, pp. 508–515. ACM, New York (2007)Google Scholar
  3. 3.
    Dorigo, M.: Optimization, learning and natural algorithms. Doctoral thesis, Politecnico de Milano, Italy (1992)Google Scholar
  4. 4.
    Fernandes, C., Rosa, A., Ramos, V.: Binary Ant Algorithm. In: 2007 Genetic and Evolutionary Computation Conference, pp. 41–48. ACM, New York (2007)Google Scholar
  5. 5.
    Fernandes, C.M., Lima, C., Rosa, A.C.: UMDAs for dynamic optimization problems. In: 2008 Genetic and Evolutionary Computation Conference, pp. 399–406. ACM, New York (2008)Google Scholar
  6. 6.
    Grassé, P.-P.: La reconstrucion du nid et les coordinations interindividuelles chez bellicositermes et cubitermes sp. La théorie de la stigmergie: Essai d’interpretation du comportement des termites constructeurs. Insectes Sociétés (6), 41–80 (1959)Google Scholar
  7. 7.
    Holland, J.H.: Adaptation in natural and artificial systems. The University of Michigan Press, MIT Press, Ann Arbor (1975)Google Scholar
  8. 8.
    Larrañaga, P., Lozano, J.A.: Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston (2002)CrossRefzbMATHGoogle Scholar
  9. 9.
    Pelikan, M., Goldberg, D.E., Cantu-Paz, E.: BOA: The Bayesian Optimization Algorithm. In: 1999 Genetic and Evolutionary Computation Conference, pp. 525–532. Morgan Kaufmann, San Francisco (1999)Google Scholar
  10. 10.
    Mühlenbein, H., Paass, G.: From recombination of genes to the estimation of distribution I, binary parameters. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN IV 1996. LNCS, vol. 1141, pp. 178–187. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  11. 11.
    Sastry, K.: Evaluation-relaxation schemes for Genetic and Evolutionary Algorithms. Msc Thesis, University of Illinois, Urbana, IL, USA (2001)Google Scholar
  12. 12.
    Zlochin, M., Biratari, M., Meulequ, N., Dorigo, M.: Modelbased search for combinatorial optimization: A critical survey. Annals of Operations Research 131(1-4), 373–395 (2004)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • C. M. Fernandes
    • 1
    • 2
  • C. F. Lima
    • 3
  • J. L. J. Laredo
    • 1
  • A. C. Rosa
    • 2
  • J. J. Merelo
    • 1
  1. 1.Department of Computer’s Architecture and TechnologyUniversity of GranadaSpain
  2. 2.LaSEEB-ISR-ISTTechnical Univ. of Lisbon (IST)Portugal
  3. 3.Informatics Lab.University of AlgarveFaroPortugal

Personalised recommendations