2026-07-20T05:32:22Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:3780 2024-03-06T10:29:10Z ddc:004 open_access

Self-Learning Genetic Algorithm For Constrains Satisfaction Problems Xu, Hu Petrie, Karen Self-learning Genetic Algorithm Constraint Programming Parameter Tuning 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Hu Xu and Karen Petrie 2012 Is Part Of OASIcs, Volume 28, 2012 Imperial College Computing Student Workshop InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.ICCSW.2012.156 urn:nbn:de:0030-drops-37808 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2012.156 eng https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode