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Frugal Algorithm Selection (Short Paper)

Authors Erdem Kuş , Özgür Akgün , Nguyen Dang , Ian Miguel

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Author Details

Erdem Kuş
  • School of Computer Science, University of St Andrews, UK
Özgür Akgün
  • School of Computer Science, University of St Andrews, UK
Nguyen Dang
  • School of Computer Science, University of St Andrews, UK
Ian Miguel
  • School of Computer Science, University of St Andrews, UK

Funding

The experiments made use of Cirrus, a UK National Tier-2 HPC Service at EPCC (http://www.cirrus.ac.uk) funded by the University of Edinburgh and EPSRC (EP/P020267/1).
  • Miguel, Ian: Ian Miguel is funded by EPSRC grant EP/V027182/1.

Cite AsGet BibTex

Erdem Kuş, Özgür Akgün, Nguyen Dang, and Ian Miguel. Frugal Algorithm Selection (Short Paper). In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 38:1-38:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.CP.2024.38

Abstract

When solving decision and optimisation problems, many competing algorithms (model and solver choices) have complementary strengths. Typically, there is no single algorithm that works well for all instances of a problem. Automated algorithm selection has been shown to work very well for choosing a suitable algorithm for a given instance. However, the cost of training can be prohibitively large due to running candidate algorithms on a representative set of training instances. In this work, we explore reducing this cost by choosing a subset of the training instances on which to train. We approach this problem in three ways: using active learning to decide based on prediction uncertainty, augmenting the algorithm predictors with a timeout predictor, and collecting training data using a progressively increasing timeout. We evaluate combinations of these approaches on six datasets from ASLib and present the reduction in labelling cost achieved by each option.

Subject Classification

ACM Subject Classification
  • Theory of computation → Active learning
  • Theory of computation → Constraint and logic programming
Keywords
  • Algorithm Selection
  • Active Learning

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Supplementary Materials

References

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