License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ICCSW.2013.128
URN: urn:nbn:de:0030-drops-42811
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2013/4281/
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Xu, Hu ; Petrie, Karen ; Murray, Iain

Using Self-learning and Automatic Tuning to Improve the Performance of Sexual Genetic Algorithms for Constraint Satisfaction Problems

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Abstract

Currently the parameters in a constraint solver are often selected by hand by experts in the field; these parameters might include the level of preprocessing to be used and the variable ordering heuristic. The efficient and automatic choice of a preprocessing level for a constraint solver is a step towards making constraint programming a more widely accessible technology. Self-learning sexual genetic algorithms are a new approach combining a self-learning mechanism with sexual genetic algorithms in order to suggest or predict a suitable solver configuration for large scale problems by learning from the same class of small scale problems. In this paper, Self-learning Sexual genetic algorithms are applied to create an automatic solver configuration mechanism for solving various constraint problems. The starting population of self-learning sexual genetic algorithms will be trained through experience on small instances. The experiments in this paper are a proof-of-concept for the idea of combining sexual genetic algorithms with a self-learning strategy to aid in parameter selection for constraint programming.

BibTeX - Entry

@InProceedings{xu_et_al:OASIcs:2013:4281,
  author =	{Hu Xu and Karen Petrie and Iain Murray},
  title =	{{Using Self-learning and Automatic Tuning to Improve the Performance of Sexual Genetic Algorithms for Constraint Satisfaction Problems}},
  booktitle =	{2013 Imperial College Computing Student Workshop},
  pages =	{128--135},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-63-7},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{35},
  editor =	{Andrew V. Jones and Nicholas Ng},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2013/4281},
  URN =		{urn:nbn:de:0030-drops-42811},
  doi =		{10.4230/OASIcs.ICCSW.2013.128},
  annote =	{Keywords: Self-learning Genetic Algorithm, Sexual Genetic algorithm, Constraint Programming, Parameter Tuning}
}

Keywords: Self-learning Genetic Algorithm, Sexual Genetic algorithm, Constraint Programming, Parameter Tuning
Collection: 2013 Imperial College Computing Student Workshop
Issue Date: 2013
Date of publication: 14.10.2013

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