Learning a Reactive Restart Strategy to Improve Stochastic Search

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Restart strategies Adaptive methods Parameter tuning 
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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Serdar Kadioglu
    • 1
  • Meinolf Sellmann
    • 2
  • Markus Wagner
    • 3
  1. 1.Department of Computer ScienceBrown UniversityProvidenceUSA
  2. 2.Cortlandt ManorCortlandtUSA
  3. 3.Optimisation and LogisticsThe University of AdelaideAdelaideAustralia

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