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International Conference on Learning and Intelligent Optimization

LION 2017: Learning and Intelligent Optimization pp 109–123Cite as

Learning a Reactive Restart Strategy to Improve Stochastic Search

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10556))

Abstract

Building on the recent success of bet-and-run approaches for restarted local search solvers, we introduce the idea of learning online adaptive restart strategies. Universal restart strategies deploy a fixed schedule that runs with utter disregard of the characteristics that each individual run exhibits. Whether a run looks promising or abysmal, it gets run exactly until the predetermined limit is reached. Bet-and-run strategies are at least slightly less ignorant as they decide which trial to use for a long run based on the performance achieved so far. We introduce the idea of learning fully adaptive restart strategies for black-box solvers, whereby the learning is performed by a parameter tuner. Numerical results show that adaptive strategies can be learned effectively and that these significantly outperform bet-and-run strategies.

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Notes

  1. 1.

    We say black-box because we do not need to know anything about the inner workings of the solver. However, we make two assumptions. First, that we can set a time limit to the solver where it stops, and that we can add more time and continue the interrupted computation later. Second, that whenever the solver stops it returns information when it found the first solution, when it found the best solution so far, and what the quality of the best solution found so far is.

  2. 2.

    This best possible solution is the best solution provided within the given time limit by any of the 10,000 runs we conducted.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Brown University, Providence, RI, USA

    Serdar Kadioglu

  2. Cortlandt Manor, Cortlandt, NY, USA

    Meinolf Sellmann

  3. Optimisation and Logistics, The University of Adelaide, Adelaide, SA, Australia

    Markus Wagner

Authors
  1. Serdar Kadioglu
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  2. Meinolf Sellmann
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  3. Markus Wagner
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Corresponding author

Correspondence to Markus Wagner .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Calabria, Rende, Italy

    Dmitri E. Kvasov

  3. University of Calabria, Rende, Italy

    Yaroslav D. Sergeyev

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Kadioglu, S., Sellmann, M., Wagner, M. (2017). Learning a Reactive Restart Strategy to Improve Stochastic Search. In: Battiti, R., Kvasov, D., Sergeyev, Y. (eds) Learning and Intelligent Optimization. LION 2017. Lecture Notes in Computer Science(), vol 10556. Springer, Cham. https://doi.org/10.1007/978-3-319-69404-7_8

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  • DOI: https://doi.org/10.1007/978-3-319-69404-7_8

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  • Online ISBN: 978-3-319-69404-7

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