Optimisation of cancer chemotherapy schedules using directed intervention crossover approaches

Paul Godley, Julie Cowie, David Cairns, John McCall, Catherine Howie

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


    This paper describes two directed intervention crossover approaches that are applied to the problem of deriving optimal cancer chemotherapy treatment schedules. Unlike traditional uniform crossover (UC), both the calculated expanding bin (CalEB) method and targeted intervention with stochastic selection (TInSSel) approaches actively choose an intervention level and spread based on the fitness of the parents selected for crossover. Our results indicate that these approaches lead to significant improvements over UC when applied to cancer chemotherapy scheduling.

    Original languageEnglish
    Title of host publication IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence)
    Place of PublicationNew York
    PublisherIEEE Computer Society
    Number of pages6
    ISBN (Print)9781424418220
    Publication statusPublished - 2008
    Event2008 IEEE Congress on Evolutionary Computation - Hong Kong, China
    Duration: 1 Jun 20086 Jun 2008


    Conference2008 IEEE Congress on Evolutionary Computation
    Abbreviated titleCEC 2008
    CityHong Kong
    OtherHeld as part of WCCI 2008 - the joint event of 2008 International Joint Conference on Neural Networks (IJCNN 2008), 2008 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2008), and 2008 IEEE Congress on Evolutionary Computation (CEC 2008).
    Internet address


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