  Recent research in areas such as SAT solving and Integer Linear Programming
has shown that the performances of a single arbitrarily efficient solver can be
significantly outperformed by a portfolio of possibly slower on-average
solvers. We report an empirical evaluation and comparison of portfolio
approaches applied to Constraint Satisfaction Problems (CSPs). We compared
models developed on top of off-the-shelf machine learning algorithms with
respect to approaches used in the SAT field and adapted for CSPs, considering
different portfolio sizes and using as evaluation metrics the number of solved
problems and the time taken to solve them. Results indicate that the best SAT
approaches have top performances also in the CSP field and are slightly more
competitive than simple models built on top of classification algorithms.
