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International Conference on Learning and Intelligent Optimization

LION 2014: Learning and Intelligent Optimization pp 41–46Cite as

Algorithm Configuration in the Cloud: A Feasibility Study

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8426))

Abstract

Configuring algorithms automatically to achieve high performance is becoming increasingly relevant and important in many areas of academia and industry. Algorithm configuration methods take a parameterized target algorithm, a performance metric and a set of example data, and aim to find a parameter configuration that performs as well as possible on a given data set.

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References

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Acknowledgements

The authors were supported by an Amazon Web Services research grant, European Union FP7 grant 284715 (ICON), a DFG Emmy Noether Grant, and Compute Canada.

Author information

Authors and Affiliations

  1. University of Nebraska-Lincoln, Lincoln, USA

    Daniel Geschwender

  2. University of Freiburg, Freiburg im Breisgau, Germany

    Frank Hutter

  3. INSIGHT Centre for Data Analytics, Cork, Ireland

    Lars Kotthoff & Yuri Malitsky

  4. University of British Columbia, Vancouver, Canada

    Holger H. Hoos & Kevin Leyton-Brown

Authors
  1. Daniel Geschwender
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  2. Frank Hutter
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  3. Lars Kotthoff
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  4. Yuri Malitsky
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  5. Holger H. Hoos
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  6. Kevin Leyton-Brown
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Corresponding author

Correspondence to Daniel Geschwender .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  2. AT&T Labs Research, Middletown, New Jersey, USA

    Mauricio G.C. Resende

  3. University of Florida, Gainesville, Florida, USA

    Chrysafis Vogiatzis

  4. Department of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Jose L. Walteros

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© 2014 Springer International Publishing Switzerland

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Geschwender, D., Hutter, F., Kotthoff, L., Malitsky, Y., Hoos, H.H., Leyton-Brown, K. (2014). Algorithm Configuration in the Cloud: A Feasibility Study. In: Pardalos, P., Resende, M., Vogiatzis, C., Walteros, J. (eds) Learning and Intelligent Optimization. LION 2014. Lecture Notes in Computer Science(), vol 8426. Springer, Cham. https://doi.org/10.1007/978-3-319-09584-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-09584-4_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09583-7

  • Online ISBN: 978-3-319-09584-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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