Self-Learning Genetic Algorithm For Constrains Satisfaction Problems

Authors Hu Xu, Karen Petrie

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Hu Xu
Karen Petrie

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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)


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.
  • Self-learning Genetic Algorithm
  • Constraint Programming
  • Parameter Tuning


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