Abstract
Many advanced solving algorithms for constraint programming problems are highly configurable. The research area of algorithm configuration investigates ways of automatically configuring these solvers in the best manner possible. In this paper, we specifically focus on algorithm configuration in which the objective is to decrease the time it takes the solver to find an optimal solution. In this setting, adaptive capping is a popular technique which reduces the overall runtime of the search for good configurations by adaptively setting the solver’s timeout to the best runtime found so far. Additionally, sequential model-based optimization (SMBO)—in which one iteratively learns a surrogate model that can predict the runtime of unseen configurations—has proven to be a successful paradigm. Unfortunately, adaptive capping and SMBO have thus far remained incompatible, as in adaptive capping, one cannot observe the true runtime of runs that time out, precluding the typical use of SMBO. To marry adaptive capping and SMBO, we instead use SMBO to model the probability that a configuration will improve on the best runtime achieved so far, for which we propose several decomposed models. These models also allow defining prior probabilities for each hyperparameter. The experimental results show that our DeCaprio method speeds up hyperparameter search compared to random search and the seminal adaptive capping approach of ParamILS.
Ignace Bleukx and Senne Berden—These authors contributed equally.
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Notes
- 1.
DEcomposable adaptive CApping with PRIOrs.
- 2.
From Håkan Kjellerstrand’s collection: http://www.hakank.org/cpmpy/.
- 3.
The number of threads was limited to 1 for every solver call.
- 4.
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Acknowledgments
This research was partly funded by the Flemish Government (AI Research Program), the Research Foundation - Flanders (FWO) projects G0G3220N and S007318N and the European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (Grant No 101002802, CHAT-Opt).
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Bleukx, I., Berden, S., Coenen, L., Decleyre, N., Guns, T. (2022). Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions. In: Schaus, P. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2022. Lecture Notes in Computer Science, vol 13292. Springer, Cham. https://doi.org/10.1007/978-3-031-08011-1_6
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