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.
https://hal.inria.fr/hal-01089183
Contributeur : Jacopo Mauro
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Soumis le : lundi 1 décembre 2014 - 12:02:37
Dernière modification le : samedi 27 janvier 2018 - 01:31:32
Document(s) archivé(s) le : lundi 2 mars 2015 - 13:30:47
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, 2014, 〈10.1145/2554850.2555114〉. 〈hal-01089183〉
