Optimisation and fitness modelling of bio-control in mushroom farming using a Markov Network EDA

Alexander Brownlee, Yanghui Wu, John McCall, Paul Godley, David Cairns, Julie Cowie

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

    5 Citations (Scopus)

    Abstract

    We explore the application of an Estimation of Distribution Algorithm which uses a Markov Network to the problem of bio-control in mushroom farming. This falls into the category of .bang-bang control. problems and was previously used as an application for genetic algorithms with modified crossover operators. The EDA yields a small improvement in the solutions that are evolved. Moreover, the probabilistic models constructed closely match identifiable features in the underlying dynamics of the problem. We conclude that this is a useful by-product of the probabilistic modelling which can be further exploited.
    Original languageEnglish
    Title of host publicationGECCO '08
    Subtitle of host publicationproceedings of the 10th annual conference on Genetic and evolutionary computation
    EditorsMaarten Keijzer
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery
    Pages465-466
    Number of pages2
    ISBN (Print)9781605581309
    DOIs
    Publication statusPublished - 2008
    EventGenetic and Evolutionary Computation Conference 2008 - Atlanta, Georgia, United States
    Duration: 12 Jul 200816 Jul 2008
    http://www.sigevo.org/gecco-2008/

    Conference

    ConferenceGenetic and Evolutionary Computation Conference 2008
    Abbreviated titleGECCO 2008
    CountryUnited States
    CityAtlanta, Georgia
    Period12/07/0816/07/08
    Internet address

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  • Cite this

    Brownlee, A., Wu, Y., McCall, J., Godley, P., Cairns, D., & Cowie, J. (2008). Optimisation and fitness modelling of bio-control in mushroom farming using a Markov Network EDA. In M. Keijzer (Ed.), GECCO '08 : proceedings of the 10th annual conference on Genetic and evolutionary computation (pp. 465-466). Association for Computing Machinery. https://doi.org/10.1145/1389095.1389180