Continuous Population-Based Incremental Learning with Mixture Probability Modeling for Dynamic Optimization Problems

  • Adrian Lancucki
  • Jan Chorowski
  • Krzysztof Michalak
  • Patryk Filipiak
  • Piotr Lipinski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8669)


This paper proposes a multimodal extension of PBIL C based on Gaussian mixture models for solving dynamic optimization problems. By tracking multiple optima, the algorithm is able to follow the changes in objective functions more efficiently than in the unimodal case. The approach was validated on a set of synthetic benchmarks including Moving Peaks, dynamization of the Rosenbrock function and compositions of functions from the IEEE CEC’2009 competition. The results obtained in the experiments proved the efficiency of the approach in solving dynamic problems with a number of competing peaks.


evolutionary algorithms estimation of distribution algorithms dynamic optimization problems multimodal optimization 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Adrian Lancucki
    • 1
  • Jan Chorowski
    • 1
  • Krzysztof Michalak
    • 2
  • Patryk Filipiak
    • 1
  • Piotr Lipinski
    • 1
  1. 1.Computational Intelligence Research Group, Institute of Computer ScienceUniversity of WroclawWroclawPoland
  2. 2.Institute of Business InformaticsWroclaw University of EconomicsWroclawPoland

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