Abstract
Modern solvers for hard computational problems often expose parameters that permit customization for high performance on specific instance types. Since it is tedious and time-consuming to manually optimize such highly parameterized algorithms, recent work in the AI literature has developed automated approaches for this algorithm configuration problem [1, 3, 10, 11, 13, 16].
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Acknowledgments
We gratefully acknowledge all authors of algorithms and instance distributions for making their work available (they are cited on the webpage, acknowledged in README files, and will be cited in a future longer version of this paper). We thank Kevin Tierney and Yuri Malitsky for modifying GGA [1] to support AClib’s format; Lin Xu for generating several instance distributions and writing most feature extraction code for SAT and TSP; Adrian Balint and Sam Bayless for contributing SAT benchmark distributions; Mauro Vallati for exposing many new parameters in LPG; the developers of Fast Downward for helping define its configuration space; and Steve Ramage for helping diagnose and fix problems with several wrappers and runsolver. M. Lindauer acknowledges support by DFG project SCHA 550/8-3, and M. López-Ibáñez acknowledges support from a “Crédit Bref Séjour à l’étranger” from the Belgian F.R.S.-FNRS.
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Hutter, F. et al. (2014). AClib: A Benchmark Library for Algorithm Configuration. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_4
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