An enhanced features extractor for a portfolio of constraint solvers
R Amadini, M Gabbrielli, J Mauro
Proceedings of the 29th Annual ACM Symposium on Applied Computing - SAC '14 | Association for Computing Machinery (ACM) | Published : 2014
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 specification. 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. Copyright 2014 ACM.