Publication Details

AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)

authored by
Marius Lindauer, Frank Hutter, Holger H. Hoos, Torsten Schaub

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, such as SAT, CSP, ASP, MAXSAT and QBF. 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. In this extended abstract of our 2015 JAIR article of the same title, we summarize AUTOFOLIO, which uses an algorithm configuration procedure to automatically select an AS approach and optimize its parameters for a given AS scenario. AUTOFOLIO allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods and to automatically construct high-performance algorithm selectors. We demonstrate that AUTOFOLIO was able to produce new state-of-the-art algorithm selectors for 7 well-studied AS scenarios and matches state-oftheart performance statistically on all other scenarios. Compared to the best single algorithm for each AS scenario, AUTOFOLIO achieved average speedup factors between 1:3 and 15:4.

External Organisation(s)
University of Freiburg
University of British Columbia
University of Potsdam
Conference abstract
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Publication date
Publication status
Peer reviewed
ASJC Scopus subject areas
Artificial Intelligence
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