Self-learning genetic algorithm for constrains satisfaction problems

    Research output: Chapter in Book/Report/Conference proceedingChapter

    1 Citation (Scopus)


    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.
    Original languageEnglish
    Title of host publication2012 Imperial College Computing Student Workshop
    Subtitle of host publicationICCSW 2012
    EditorsAndrew V. Jones
    Place of PublicationLeibnitz, Austria
    PublisherDagstuhl Publications
    Number of pages7
    ISBN (Print)9783939897484
    Publication statusPublished - 2012
    Event2nd Imperial College Computing Student Workshop, ICCSW 2012 - London, United Kingdom
    Duration: 27 Sept 201228 Sept 2012

    Publication series

    NameOpen Access Series in Informatics (OASIcs)
    ISSN (Print)2190-6807


    Conference2nd Imperial College Computing Student Workshop, ICCSW 2012
    Country/TerritoryUnited Kingdom
    Internet address


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