An Enhanced Features Extractor for a Portfolio of Constraint Solvers
This work addresses the need for improved solver selection in constraint solving portfolios, though it appears incremental as it builds on existing portfolio techniques.
The paper tackled the problem of selecting the best solver from a portfolio for constraint satisfaction/optimization problems by introducing a framework that extracts a comprehensive set of features from problem specifications in multiple modeling languages, resulting in performance that is effective and competitive with state-of-the-art techniques.
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. We also report some empirical results showing that the performances that can be obtained using these features are effective and competitive with state of the art CSP portfolio techniques.