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.

References in zbMATH (referenced in 11 articles )

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  1. Theresa Eimer, André Biedenkapp, Maximilian Reimer, Steven Adriaensen, Frank Hutter, Marius Lindauer: DACBench: A Benchmark Library for Dynamic Algorithm Configuration (2021) arXiv
  2. Bowly, Simon; Smith-Miles, Kate; Baatar, Davaatseren; Mittelmann, Hans: Generation techniques for linear programming instances with controllable properties (2020)
  3. Hottung, André; Tanaka, Shunji; Tierney, Kevin: Deep learning assisted heuristic tree search for the container pre-marshalling problem (2020)
  4. Carvalho, Iago A.: On the statistical evaluation of algorithmic’s computational experimentation with infeasible solutions (2019)
  5. Lindauer, Marius; van Rijn, Jan N.; Kotthoff, Lars: The algorithm selection competitions 2015 and 2017 (2019)
  6. Eggensperger, Katharina; Lindauer, Marius; Hoos, Holger H.; Hutter, Frank; Leyton-Brown, Kevin: Efficient benchmarking of algorithm configurators via model-based surrogates (2018)
  7. Tanaka, Shunji; Tierney, Kevin: Solving real-world sized container pre-marshalling problems with an iterative deepening branch-and-bound algorithm (2018)
  8. Mısır, Mustafa; Sebag, Michèle: \textscAlors: an algorithm recommender system (2017)
  9. Bischl, Bernd; Kerschke, Pascal; Kotthoff, Lars; Lindauer, Marius; Malitsky, Yuri; Fréchette, Alexandre; Hoos, Holger; Hutter, Frank; Leyton-Brown, Kevin; Tierney, Kevin; Vanschoren, Joaquin: ASlib: a benchmark library for algorithm selection (2016)
  10. Hottung, André; Tierney, Kevin: A biased random-key genetic algorithm for the container pre-marshalling problem (2016)
  11. Amadini, Roberto; Gabbrielli, Maurizio; Mauro, Jacopo: Why CP portfolio solvers are (under)utilized? issues and challenges (2015)