2 Search Results for "Petrie, Karen"

Document
DOI: 10.4230/OASIcs.ICCSW.2013.128
Using Self-learning and Automatic Tuning to Improve the Performance of Sexual Genetic Algorithms for Constraint Satisfaction Problems

Authors: Hu Xu, Karen Petrie, and Iain Murray

Published in: OASIcs, Volume 35, 2013 Imperial College Computing Student Workshop

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.
Cite as

Hu Xu, Karen Petrie, and Iain Murray. Using Self-learning and Automatic Tuning to Improve the Performance of Sexual Genetic Algorithms for Constraint Satisfaction Problems. In 2013 Imperial College Computing Student Workshop. Open Access Series in Informatics (OASIcs), Volume 35, pp. 128-135, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InProceedings{xu_et_al:OASIcs.ICCSW.2013.128,
  author =	{Xu, Hu and Petrie, Karen and Murray, Iain},
  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 =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-63-7},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{35},
  editor =	{Jones, Andrew V. and Ng, Nicholas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2013.128},
  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}
}
Document
DOI: 10.4230/OASIcs.ICCSW.2012.156
Self-Learning Genetic Algorithm For Constrains Satisfaction Problems

Authors: Hu Xu and Karen Petrie

Published in: OASIcs, Volume 28, 2012 Imperial College Computing Student Workshop

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.
Cite as

Hu Xu and Karen Petrie. Self-Learning Genetic Algorithm For Constrains Satisfaction Problems. In 2012 Imperial College Computing Student Workshop. Open Access Series in Informatics (OASIcs), Volume 28, pp. 156-162, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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