Particle swarm optimisation for learning Bayesian networks

J. Cowie, L. Oteniya, R. Coles

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    This paper discusses the potential of Particle Swarm Optimisation (PSO) for inducing Bayesian Networks (BNs). Specifically, we detail two methods which adopt the search and score approach to BN learning. The two algorithms are similar in that they both use PSO as the search algorithm, and the K2 metric to score the resulting network. The difference lies in the way networks are constructed. The CONstruct And Repair (CONAR) algorithm generates structures, validates, and repairs if required, and the REstricted STructure (REST) algorithm, only permits valid structures to be developed. Initial experiments indicate that these approaches produce promising results when compared to other BN learning strategies.

    Original languageEnglish
    Title of host publicationProceedings of the World Congress on Engineering, WCE 2007, London, UK, 2-4 July, 2007
    Subtitle of host publicationLecture notes in engineering and computer science, Volume 1
    EditorsS. I. Ao, L. Gelman
    Place of PublicationHong Kong
    PublisherInternational Association of Engineers
    Number of pages6
    ISBN (Print)9789889867157
    Publication statusPublished - 2007
    EventWorld Congress on Engineering 2007 - South Kensington Campus, Imperial College London, London, United Kingdom
    Duration: 2 Jul 20074 Jul 2007


    ConferenceWorld Congress on Engineering 2007
    Abbreviated titleWCE 2007
    Country/TerritoryUnited Kingdom
    Internet address


    Dive into the research topics of 'Particle swarm optimisation for learning Bayesian networks'. Together they form a unique fingerprint.

    Cite this