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Using self-learning and automatic tuning to improve the performance of sexual genetic algorithms for constraint satisfaction problems

Using self-learning and automatic tuning to improve the performance of sexual genetic algorithms for constraint satisfaction problems

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Original languageEnglish
Title of host publication2013 Imperial College Computing Student Workshop (ICCSW'13)
EditorsAndrew V. Jones, Nicholas Ng
Place of PublicationSaarbrücken
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH
Pages128-135
Number of pages8
ISBN (Print)9783939897637
DOIs
StatePublished - 2013
Event2013 Imperial College Computing Student Workshop - London, United Kingdom

Publication series

NameOASICS
Volume35
ISSN (Print)2190-6807

Workshop

Workshop2013 Imperial College Computing Student Workshop
Abbreviated titleICCSW'13
CountryUnited Kingdom
CityLondon
Period26/09/1327/09/13
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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.

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