SNNAP: Solver-based nearest neighbor for algorithm portfolios

Abstract

The success of portfolio algorithms in competitions in the area of combinatorial problem solving, as well as in practice, has motivated interest in the development of new approaches to determine the best solver for the problem at hand. Yet, although there are a number of ways in which this decision can be made, it always relies on a rich set of features to identify and distinguish the structure of the problem instances. In this paper, we show how one of the more successful portfolio approaches, ISAC, can be augmented by taking into account the past performance of solvers as part of the feature vector. Testing on a variety of SAT datasets, we show how our new formulation continuously outperforms an unmodified/standard version of ISAC. © 2013 Springer-Verlag.