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)


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.


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.


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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

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