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- W2274513178 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." @default.
- W2274513178 created "2016-06-24" @default.
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- W2274513178 date "2013-01-01" @default.
- W2274513178 modified "2023-09-23" @default.
- W2274513178 title "Using Self-learning and Automatic Tuning to Improve the Performance of Sexual Genetic Algorithms for Constraint Satisfaction Problems" @default.
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- W2274513178 doi "https://doi.org/10.4230/oasics.iccsw.2013.128" @default.
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