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URN: urn:nbn:de:0030-drops-37808
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Self-Learning Genetic Algorithm For Constrains Satisfaction Problems

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Abstract

The efficient choice of a preprocessing level can reduce the search time of a constraint solver to find a solution to a constraint problem. Currently the parameters in constraint solver are often picked by hand by experts in the field. Genetic algorithms are a robust machine learning technology for problem optimization such as function optimization. Self-learning Genetic Algorithm are a strategy which suggests or predicts the suitable preprocessing method for large scale problems by learning from the same class of small scale problems. In this paper Self-learning Genetic Algorithms are used to create an automatic preprocessing selection mechanism for solving various constraint problems. The experiments in the paper are a proof of concept for the idea of combining genetic algorithm self-learning ability with constraint programming to aid in the parameter selection issue.

BibTeX - Entry

@InProceedings{xu_et_al:OASIcs:2012:3780,
  author =	{Hu Xu and Karen Petrie},
  title =	{{Self-Learning Genetic Algorithm For Constrains Satisfaction Problems}},
  booktitle =	{2012 Imperial College Computing Student Workshop},
  pages =	{156--162},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-48-4},
  ISSN =	{2190-6807},
  year =	{2012},
  volume =	{28},
  editor =	{Andrew V. Jones},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2012/3780},
  URN =		{urn:nbn:de:0030-drops-37808},
  doi =		{http://dx.doi.org/10.4230/OASIcs.ICCSW.2012.156},
  annote =	{Keywords: Self-learning Genetic Algorithm, Constraint Programming, Parameter Tuning}
}

Keywords: Self-learning Genetic Algorithm, Constraint Programming, Parameter Tuning
Seminar: 2012 Imperial College Computing Student Workshop
Issue date: 2012
Date of publication: 2012


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