AutoFolio uses algorithm configuration to optimize the performance of algorithm selection systems by determining the best selection approach and its hyperparameters.
Algorithm selection (AS) techniques — which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently — have substantially improved the state-of-the-art in solving many prominent AI problems, notably in ASP, CSP, MAXSAT, QBF and SAT. Although, several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. This holds specifically for the machine learning techniques that form the core of current AS procedures and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems by using automated algorithm configuration. Specifically, we demonstrate that we can use algorithm configurators to automatically configure the flexible algorithm framework of claspfolio 2 which implements a large variety of different AS approaches and is highly parameterized.
- Releases – Download
- Autofolio for ICON Algorithm Selection Challenge
- Repository for Autofolio 2.0 — much easier to use; but never feature-completed
- M. Lindauer, H. Hoos, F. Hutter and T. Schaub. AutoFolio: An Automatically Configured Algorithm Selector. Journal of Artificial Intelligence 53 (2015): 745-778