Learning Algorithm Portfolios for Parallel Execution

摘要

Portfolio-based solvers are both effective and robust, but their promise for parallel execution with constraint satisfaction solvers has received relatively little attention. This paper proposes an approach that constructs algorithm portfolios intended for parallel execution based on a combination of case-based reasoning, a greedy algorithm, and three heuristics. Empirical results show that this method is efficient, and can significantly improve performance with only a few additional processors. On problems from solver competitions, the resultant algorithm portfolios perform nearly as well as an oracle.