When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.09181.5
URN: urn:nbn:de:0030-drops-21159
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Bartz-Beielstein, Thomas

Sequential Parameter Optimization

09181.BartzBeielsteinThomas.Paper.2115.pdf (2 MB)


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

  author =	{Bartz-Beielstein, Thomas},
  title =	{{Sequential Parameter Optimization}},
  booktitle =	{Sampling-based Optimization in the Presence of Uncertainty},
  pages =	{1--32},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9181},
  editor =	{J\"{u}rgen Branke and Barry L. Nelson and Warren Buckler Powell and Thomas J. Santner},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-21159},
  doi =		{10.4230/DagSemProc.09181.5},
  annote =	{Keywords: Optimization, evolutionary algorithms, design of experiments}

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

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