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Learning Algorithm Portfolios for Parallel Execution

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Learning and Intelligent Optimization (LION 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7219))

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Abstract

Portfolio-based solvers are both effective and robust, but their promise for parallel execution with constraint satisfaction solvers has received relatively little attention. This paper proposes an approach that constructs algorithm portfolios intended for parallel execution based on a combination of case-based reasoning, a greedy algorithm, and three heuristics. Empirical results show that this method is efficient, and can significantly improve performance with only a few additional processors. On problems from solver competitions, the resultant algorithm portfolios perform nearly as well as an oracle.

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Author information

Authors and Affiliations

  1. Department of Computer Science, The Graduate School of The City University of New York, New York, NY, 10016, USA

    Xi Yun & Susan L. Epstein

  2. Department of Computer Science, Hunter College of The City University of New York, New York, NY, 10065, USA

    Susan L. Epstein

Authors
  1. Xi Yun
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  2. Susan L. Epstein
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Editor information

Editors and Affiliations

  1. Microsoft Research Center, CB3 0FB, Cambridge, UK

    Youssef Hamadi

  2. INRIA Saclay, Université Paris Sud, 91405, Orsay Cedex, France

    Marc Schoenauer

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Yun, X., Epstein, S.L. (2012). Learning Algorithm Portfolios for Parallel Execution. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_23

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  • DOI: https://doi.org/10.1007/978-3-642-34413-8_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34412-1

  • Online ISBN: 978-3-642-34413-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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