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An enhanced features extractor for a portfolio of constraint solvers

Roberto Amadini 1, 2 Maurizio Gabbrielli 1, 2 Jacopo Mauro 2, 1
2 FOCUS - Foundations of Component-based Ubiquitous Systems
CRISAM - Inria Sophia Antipolis - Méditerranée , DISI - Dipartimento di Informatica - Scienza e Ingegneria [Bologna]
Abstract : Recent research has shown that a single arbitrarily efficient solver can be significantly outperformed by a portfolio of possibly slower on-average solvers. The solver selection is usually done by means of (un)supervised learning techniques which exploit features extracted from the problem specifica-tion. In this paper we present an useful and flexible framework that is able to extract an extensive set of features from a Constraint (Satisfaction/Optimization) Problem defined in possibly different modeling languages: MiniZinc, FlatZinc or XCSP.
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Submitted on : Monday, December 1, 2014 - 12:02:37 PM
Last modification on : Friday, October 30, 2020 - 12:04:03 PM
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Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro. An enhanced features extractor for a portfolio of constraint solvers. SAC 2014, Mar 2014, Gyeongju, South Korea. pp.1357 - 1359, ⟨10.1145/2554850.2555114⟩. ⟨hal-01089183⟩



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