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Structure-Preserving Instance Generation

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Book cover Learning and Intelligent Optimization (LION 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10079))

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Abstract

Real-world instances are critical for the development of state-of-the-art algorithms, algorithm configuration techniques, and selection approaches. However, very few true industrial instances exist for most problems, which poses a problem both to algorithm designers and methods for algorithm selection. The lack of enough real data leads to an inability for algorithm designers to show the effectiveness of their techniques, and for algorithm selection it is difficult or even impossible to train a portfolio with so few training examples. This paper introduces a novel instance generator that creates instances that have the same structural properties as industrial instances. We generate instances through a large neighborhood search-like method that combines components of instances together to form new ones. We test our approach on the MaxSAT and SAT problems, and then demonstrate that portfolios trained on these generated instances perform just as well or even better than those trained on the real instances.

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Notes

  1. 1.

    An extended version of this work provides a more extensive literature review; see: https://bitbucket.org/eusorpb/spig/.

  2. 2.

    We do note use local search probing features in this work.

  3. 3.

    The MaxSAT 2013 dataset contains 55 instances, but we remove instances over 110 MB after performing unit propagation, as SpIG cannot fit them in RAM.

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Acknowledgements

We thank the Paderborn Center for Parallel Computing for the use of the OCuLUS cluster for the experiments in this paper. Barry O’Sullivan was supported in part by Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.

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Authors and Affiliations

  1. IBM T.J. Watson Research Center, New York, USA

    Yuri Malitsky

  2. Decision Support and Operations Research Lab, University of Paderborn, Paderborn, Germany

    Marius Merschformann & Kevin Tierney

  3. Insight Centre for Data Analytics, University College Cork, Cork, Ireland

    Barry O’Sullivan

Authors
  1. Yuri Malitsky
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  2. Marius Merschformann
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  3. Barry O’Sullivan
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  4. Kevin Tierney
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Corresponding author

Correspondence to Kevin Tierney .

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Editors and Affiliations

  1. Univ of Naples Federico II, Naples, Italy

    Paola Festa

  2. Thomas J. Wastson Research Center, Yorktown Heights, New York, USA

    Meinolf Sellmann

  3. Eindhoven University of Technology, Eindhoven, The Netherlands

    Joaquin Vanschoren

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Malitsky, Y., Merschformann, M., O’Sullivan, B., Tierney, K. (2016). Structure-Preserving Instance Generation. In: Festa, P., Sellmann, M., Vanschoren, J. (eds) Learning and Intelligent Optimization. LION 2016. Lecture Notes in Computer Science(), vol 10079. Springer, Cham. https://doi.org/10.1007/978-3-319-50349-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-50349-3_9

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  • Online ISBN: 978-3-319-50349-3

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