Univariate Marginal Distribution Algorithm with Markov Chain Predictor in Continuous Dynamic Environments

  • Patryk Filipiak
  • Piotr Lipinski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8669)


This paper presents an extension of the continuous Univariate Marginal Distribution Algorithm with the prediction mechanism based on a Markov chain model in order to improve the reactivity of the algorithm in continuous dynamic optimization problems. Also a population diversification into exploring, exploiting and anticipating fractions is proposed with the auto-adaptation mechanism for updating dynamically the sizes of these fractions. The proposed approach is tested on the popular benchmark functions with the recurring type of changes.


Search Space Good Individual Markov Chain Model Distribution Algorithm Prediction Mechanism 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Patryk Filipiak
    • 1
  • Piotr Lipinski
    • 1
  1. 1.Computational Intelligence Research Group, Institute of Computer ScienceUniversity of WroclawPoland

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