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Algorithm Portfolios for Noisy Optimization: Compare Solvers Early

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Learning and Intelligent Optimization (LION 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8426))

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Abstract

Noisy optimization is the optimization of objective functions corrupted by noise. A portfolio of algorithms is a set of algorithms equipped with an algorithm selection tool for distributing the computational power among them. We study portfolios of noisy optimization solvers, show that different settings lead to different performances, obtain mathematically proved performance (in the sense that the portfolio performs nearly as well as the best of its’ algorithms) by an ad hoc selection algorithm dedicated to noisy optimization. A somehow surprising result is that it is better to compare solvers with some lag; i.e., recommend the current recommendation of the best solver, selected from a comparison based on their recommendations earlier in the run.

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Author information

Authors and Affiliations

  1. TAO, INRIA-CNRS-LRI, University of Paris-Sud, 91190, Gif-sur-Yvette, France

    Marie-Liesse Cauwet, Jialin Liu & Olivier Teytaud

Authors
  1. Marie-Liesse Cauwet
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  2. Jialin Liu
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  3. Olivier Teytaud
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Corresponding author

Correspondence to Olivier Teytaud .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. AT&T Labs Research, Middletown, New Jersey, USA

    Mauricio G.C. Resende

  3. University of Florida, Gainesville, Florida, USA

    Chrysafis Vogiatzis

  4. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Jose L. Walteros

Appendix

Appendix

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Cauwet, ML., Liu, J., Teytaud, O. (2014). Algorithm Portfolios for Noisy Optimization: Compare Solvers Early. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-09584-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09583-7

  • Online ISBN: 978-3-319-09584-4

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