ASlib: A benchmark library for algorithm selection

The task of algorithm selection involves choosing an algorithm from a set of algorithms on a per-instance basis in order to exploit the varying performance of algorithms over a set of instances. The algorithm selection problem is attracting increasing attention from researchers and practitioners in AI. Years of fruitful applications in a number of domains have resulted in a large amount of data, but the community lacks a standard format or repository for this data. This situation makes it difficult to share and compare different approaches effectively, as is done in other, more established fields. It also unnecessarily hinders new researchers who want to work in this area. To address this problem, we introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature. Our format has been designed to be able to express a wide variety of different scenarios. To demonstrate the breadth and power of our platform, we describe a study that builds and evaluates algorithm selection models through a common interface. The results display the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.

Authors

• Bernd Bischl
• Pascal Kerschke
• Lars Kotthoff University of Wyoming, Laramie, WY, USA
• Marius Lindauer Institute of Artificial Intelligence L3S Research Center Leibniz University Hannover
• Yuri Malitsky
• Alexandre Fréchette
• Holger Hoos
• Frank Hutter
• Kevin Leyton-Brown University of British Columbia
• Kevin Tierney Decision and Operation Technologies Group, Bielefeld University, Germany
• Joaquin Vanschoren

Keywords

• Algorithm selection
• Machine learning
• Empirical performance estimation