Journal of Global Optimization

, Volume 67, Issue 1–2, pp 263–282 | Cite as

Dynamic algorithm selection for pareto optimal set approximation

  • Ingrida Steponavičė
  • Rob J. Hyndman
  • Kate Smith-Miles
  • Laura Villanova


This paper presents a meta-algorithm for approximating the Pareto optimal set of costly black-box multiobjective optimization problems given a limited number of objective function evaluations. The key idea is to switch among different algorithms during the optimization search based on the predicted performance of each algorithm at the time. Algorithm performance is modeled using a machine learning technique based on the available information. The predicted best algorithm is then selected to run for a limited number of evaluations. The proposed approach is tested on several benchmark problems and the results are compared against those obtained using any one of the candidate algorithms alone.


Multiobjective optimization Expensive black-box function Machine learning Classification Algorithm selection Hypervolume metric Features 


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Ingrida Steponavičė
    • 1
  • Rob J. Hyndman
    • 2
  • Kate Smith-Miles
    • 1
  • Laura Villanova
    • 1
  1. 1.School of Mathematical SciencesMonash UniversityClaytonAustralia
  2. 2.Department of Econometrics and Business StatisticsMonash UniversityClaytonAustralia

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