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.
@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.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} }
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