An enhanced features extractor for a portfolio of constraint solvers | Zendy

Roberto Amadini | Zendy; Maurizio Gabbrielli | Zendy; Jacopo Mauro | Zendy

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An enhanced features extractor for a portfolio of constraint solvers

Author(s) -

Roberto Amadini,

Maurizio Gabbrielli,

Jacopo Mauro

Publication year - 2014

Publication title -

hal (le centre pour la communication scientifique directe)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/2554850.2555114

Subject(s) - solver , computer science , exploit , extractor , constraint satisfaction problem , set (abstract data type) , constraint (computer aided design) , constraint satisfaction , portfolio , selection (genetic algorithm) , artificial intelligence , machine learning , mathematical optimization , programming language , mathematics , geometry , computer security , process engineering , probabilistic logic , financial economics , engineering , economics

International audienceRecent 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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