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An enhanced features extractor for a portfolio of constraint solvers
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.
Cited literature [16 references]
https://hal.inria.fr/hal-01089183
Contributor : Jacopo Mauro <>
Submitted on : Monday, December 1, 2014 - 12:02:37 PM
Last modification on : Friday, October 30, 2020 - 12:04:03 PM
Long-term archiving on: : Monday, March 2, 2015 - 1:30:47 PM
Contributor : Jacopo Mauro <>
Submitted on : Monday, December 1, 2014 - 12:02:37 PM
Last modification on : Friday, October 30, 2020 - 12:04:03 PM
Long-term archiving on: : Monday, March 2, 2015 - 1:30:47 PM
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sac_2014.pdf
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