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Automated Parameter Tuning Framework for Heterogeneous and Large Instances: Case Study in Quadratic Assignment Problem

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Learning and Intelligent Optimization (LION 2013)

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Abstract

This paper is concerned with automated tuning of parameters of algorithms to handle heterogeneous and large instances. We propose an automated parameter tuning framework with the capability to provide instance-specific parameter configurations. We report preliminary results on the Quadratic Assignment Problem (QAP) and show that our framework provides a significant improvement on solutions qualities with much smaller tuning computational time.

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Acknowledgments

We thank Saifullah bin Hussin, Thomas Stützle, Mauro Birattari, Matteo Gagliolo for valuable discussion on scaling large instances, and Aldy Gunawan for allowing us to use his DoE codes.

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Authors and Affiliations

  1. School of Information Systems, Singapore Management University, Singapore, Singapore

    Lindawati, Zhi Yuan, Hoong Chuin Lau & Feida Zhu

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

    Zhi Yuan

Authors
  1. Lindawati
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  2. Zhi Yuan
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  3. Hoong Chuin Lau
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  4. Feida Zhu
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Corresponding author

Correspondence to Feida Zhu .

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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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Lindawati, Yuan, Z., Lau, H.C., Zhu, F. (2013). Automated Parameter Tuning Framework for Heterogeneous and Large Instances: Case Study in Quadratic Assignment Problem. 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_45

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  • DOI: https://doi.org/10.1007/978-3-642-44973-4_45

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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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