AutoFolio: An Automatically Configured Algorithm Selector

authored by

Marius Thomas Lindauer, Holger Hoos, Frank Hutter, Torsten Schaub

Abstract

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. 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 simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single, highly-parameterized algorithm framework. Our approach, dubbed AutoFolio, allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods. We demonstrate AutoFolio can significantly improve the performance of claspfolio 2 on 8 out of the 13 scenarios from the Algorithm Selection Library, leads to new state-of-the-art algorithm selectors for 7 of these scenarios, and matches state-of-the-art performance (statistically) on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieves average speedup factors between 1.3 and 15.4.

External Organisation(s)

University of Freiburg
University of British Columbia
University of Potsdam
INRIA Institut National de Recherche en Informatique et en Automatique

Type

Article

Journal

Journal of Artificial Intelligence Research

Volume

53

Pages

745-778

No. of pages

34

ISSN

1076-9757

Publication date

15.08.2015

Publication status

Published

ASJC Scopus subject areas

Artificial Intelligence

Electronic version(s)

https://doi.org/10.1613/jair.4726 (Access: Unknown)