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Using Racing to Automatically Configure Algorithms for Scaling Performance

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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

Automated algorithm configuration has been proven to be an effective approach for achieving improved performance of solvers for many computationally hard problems. Following our previous work, we consider the challenging situation where the kind of problem instances for which we desire optimised performance are too difficult to be used during the configuration process. In this work, we propose a novel combination of racing techniques with existing algorithm configurators to meet this challenge. We demonstrate that the resulting algorithm configuration protocol achieves better results than previous approaches and in many cases closely matches the bound on performance obtained using an oracle selector. An extended version of this paper can be found at www.cs.ubc.ca/labs/beta/Projects/Config4Scaling.

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Authors and Affiliations

  1. University of British Columbia, 2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada

    James Styles & Holger H. Hoos

Authors
  1. James Styles
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  2. Holger H. Hoos
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Corresponding author

Correspondence to James Styles .

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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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Styles, J., Hoos, H.H. (2013). Using Racing to Automatically Configure Algorithms for Scaling Performance. 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_41

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  • DOI: https://doi.org/10.1007/978-3-642-44973-4_41

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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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