AS-ASL: Algorithm Selection with Auto-sklearn
Proceedings of the Open Algorithm Selection Challenge, PMLR 79:19-22, 2017.
In this paper, we describe our algorithm selection with Auto-sklearn (as-asl) software as it was entered in the 2017 Open Algorithm Selection Challenge. as-asl first selects informative sets of features and then uses those to predict distributions of algorithm runtimes. A classifier uses those predictions, as well as the informative features, to select an algorithm for each problem instance. Our source code is publicly available with the permissive MIT license.