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Automatic Tuning of Algorithms Through Sensitivity Minimization

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Machine Learning, Optimization, and Big Data (MOD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9432))

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Abstract

Parameters tuning is a crucial step in global optimization. In this work, we present a novel method, the Sensitive Algorithmic Tuning, which finds near-optimal parameter configurations through sensitivity minimization. The experimental results highlight the effectiveness and robustness of this novel approach.

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Acknowledgments

The authors would like to acknowledge Professor Angelo Marcello Anile for the useful discussions on the seminal idea of automatic algorithms tuning. Professor Anile was a continuous source of inspiration during our research work.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, Catania, Italy

    Piero Conca & Giuseppe Nicosia

  2. Ludwig Institute for Cancer Research, University of Oxford, Oxford, UK

    Giovanni Stracquadanio

Authors
  1. Piero Conca
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  2. Giovanni Stracquadanio
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  3. Giuseppe Nicosia
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Corresponding author

Correspondence to Piero Conca .

Editor information

Editors and Affiliations

  1. University of Florida, Gainsville, Florida, USA

    Panos Pardalos

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

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Conca, P., Stracquadanio, G., Nicosia, G. (2015). Automatic Tuning of Algorithms Through Sensitivity Minimization. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-27926-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27925-1

  • Online ISBN: 978-3-319-27926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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