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Dynamic algorithm selection for pareto optimal set approximation

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Abstract

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.

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Authors and Affiliations

  1. School of Mathematical Sciences, Monash University, Clayton, Australia

    Ingrida Steponavičė, Kate Smith-Miles & Laura Villanova

  2. Department of Econometrics and Business Statistics, Monash University, Clayton, Australia

    Rob J. Hyndman

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  1. Ingrida Steponavičė
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  2. Rob J. Hyndman
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  3. Kate Smith-Miles
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  4. Laura Villanova
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Correspondence to Ingrida Steponavičė.

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Steponavičė, I., Hyndman, R.J., Smith-Miles, K. et al. Dynamic algorithm selection for pareto optimal set approximation. J Glob Optim 67, 263–282 (2017). https://doi.org/10.1007/s10898-016-0420-x

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