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Experimental Comparison of BTD and Intelligent Backtracking: Towards an Automatic Per-instance Algorithm Selector

  • Conference paper
Principles and Practice of Constraint Programming (CP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8656))

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Abstract

We consider a generic binary CSP solver parameterized by high-level design choices, i.e., backtracking mechanisms, constraint propagation levels, and variable ordering heuristics. We experimentally compare 24 different configurations of this generic solver on a benchmark of around a thousand instances. This allows us to understand the complementarity of the different search mechanisms, with an emphasis on Backtracking with Tree Decomposition (BTD). Then, we use a per-instance algorithm selector to automatically select a good solver for each new instance to be solved. We introduce a new strategy for selecting the solvers of the portfolio, which aims at maximizing the number of instances for which the portfolio contains a good solver, independently from a time limit.

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Author information

Authors and Affiliations

  1. LIRIS, UMR5205, Université de Lyon, France

    Loïc Blet, Samba Ndojh Ndiaye & Christine Solnon

  2. Université Lyon 1, 69622, France

    Samba Ndojh Ndiaye

  3. INSA-Lyon, 69621, France

    Loïc Blet & Christine Solnon

Authors
  1. Loïc Blet
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  2. Samba Ndojh Ndiaye
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  3. Christine Solnon
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Editors and Affiliations

  1. Insight Centre for Data Analytics, School of Computer Science and IT, University College Cork, Western Road, Cork, Ireland

    Barry O’Sullivan

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Blet, L., Ndiaye, S.N., Solnon, C. (2014). Experimental Comparison of BTD and Intelligent Backtracking: Towards an Automatic Per-instance Algorithm Selector. In: O’Sullivan, B. (eds) Principles and Practice of Constraint Programming. CP 2014. Lecture Notes in Computer Science, vol 8656. Springer, Cham. https://doi.org/10.1007/978-3-319-10428-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-10428-7_16

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10427-0

  • Online ISBN: 978-3-319-10428-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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