SNNAP: Solver-based Nearest Neighbor for Algorithm Portfolios

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. In this thesis, however, it is firstly shown how not all the features in the problem have the same relevancy. Then it is presented how one of the most successful portfolio approaches, ISAC, can be augmented by taking into account the past performance of solvers