Estimating the distribution in an EDA

  • S. Shakya
  • J. McCall
  • D. F. Brown


This paper presents an extension to our work on estimating the probability distribution by using a Markov Random Field (MRF) model in an Estimation of Distribution Algorithm (EDA) [1]. We propose a method that directly samples a MRF model to generate new population. We also present a new EDA, called the Distribution Estimation Using MRF with direct sampling (DEUMd), that uses this method, and iteratively refines the probability distribution to generate better solutions. Our experiments show that the direct sampling of a MRF model as estimation of distribution provides a significant advantage over other techniques on problems where a univariate EDA is typically used.


Markov Random Field Joint Probability Distribution Fitness Evaluation Direct Sampling Distribution Estimation 
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/Wien 2005

Authors and Affiliations

  • S. Shakya
    • 1
  • J. McCall
    • 1
  • D. F. Brown
    • 1
  1. 1.School of ComputingThe Robert Gordon UniversityAberdeenUK

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