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URN: urn:nbn:de:0030-drops-21159
URL: http://drops.dagstuhl.de/opus/volltexte/2009/2115/
Bartz-Beielstein, Thomas

Sequential Parameter Optimization

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Abstract

We provide a comprehensive, effective and very efficient methodology for the design and experimental analysis of algorithms. We rely on modern statistical techniques for tuning and understanding algorithms from an experimental perspective. Therefore, we make use of the sequential parameter optimization (SPO) method that has been successfully applied as a tuning procedure to numerous heuristics for practical and theoretical optimization problems. Two case studies, which illustrate the applicability of SPO to algorithm tuning and model selection, are presented.

BibTeX - Entry

@InProceedings{bartzbeielstein:DSP:2009:2115,
  author =	{Thomas Bartz-Beielstein},
  title =	{Sequential Parameter Optimization},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty },
  year =	{2009},
  editor =	{J{\"u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  number =	{09181},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2009/2115},
  annote =	{Keywords: Optimization, evolutionary algorithms, design of experiments}
}

Keywords: Optimization, evolutionary algorithms, design of experiments
Seminar: 09181 - Sampling-based Optimization in the Presence of Uncertainty
Issue date: 2009
Date of publication: 30.07.2009

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