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Tuning Algorithms for Tackling Large Instances: An Experimental Protocol

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Learning and Intelligent Optimization (LION 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7997))

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Abstract

Tuning stochastic local search algorithms for tackling large instances is difficult due to the large amount of CPU-time that testing algorithm configurations requires on such large instances. We define an experimental protocol that allows tuning an algorithm on small tuning instances and extrapolating from the obtained configurations a parameter setting that is suited for tackling large instances. The key element of our experimental protocol is that both the algorithm parameters that need to be scaled to large instances and the stopping time that is employed for the tuning instances are treated as free parameters. The scaling law of parameter values, and the computation time limits on the small instances are then derived through the minimization of a loss function. As a proof of concept, we tune an iterated local search algorithm and a robust tabu search algorithm for the quadratic assignment problem.

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Acknowledgments

This work was supported by the META-X project, an Action de Recherche Concertée funded by the Scientific Research Directorate of the French Community of Belgium. Franco Mascia, Mauro Birattari, and Thomas Stützle acknowledge support from the Belgian F.R.S.-FNRS. The authors also acknowledge support from the FRFC project “Méthodes de recherche hybrids pour la résolution de problèmes complexes”. This research and its results have also received funding from the COMEX project within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office.

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Authors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

    Franco Mascia, Mauro Birattari & Thomas Stützle

Authors
  1. Franco Mascia
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  2. Mauro Birattari
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  3. Thomas Stützle
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Corresponding author

Correspondence to Franco Mascia .

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Editors and Affiliations

  1. Università Catania Dipto. Matematica e Informatica, Catania, Italy

    Giuseppe Nicosia

  2. Industrial & Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos Pardalos

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Mascia, F., Birattari, M., Stützle, T. (2013). Tuning Algorithms for Tackling Large Instances: An Experimental Protocol. In: Nicosia, G., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 2013. Lecture Notes in Computer Science(), vol 7997. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-44973-4_44

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  • DOI: https://doi.org/10.1007/978-3-642-44973-4_44

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-44972-7

  • Online ISBN: 978-3-642-44973-4

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