Nonlinear dynamics modelling for controller evolution

Julian Togelius, Renzo De Nardi, Hugo Marques, Richard Newcombe, Simon M. Lucas, Owen Holland

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


    The problem of how to acquire a model of a physical robot,which is fit for evolution of controllers that can subsequently be used to control that robot, is considered in the context of racing a radio-controlled toy car around a randomised track. Several modelling techniques are compared, and the specific properties of the acquired models that influence the quality of the evolved controller are discussed. As we aim tominimise the amount of domain knowledge used, we furtherinvestigate the relation between the assumptions about the modelled system made by particular modelling techniques and the suitability of the acquired models as bases for controller evolution. We find that none of the models acquired is good enough on its own, and that a key to evolving robustbehaviour is to evaluate controllers simultaneously on multiple models during evolution. Examples of successfully evolved racing control for the physical car are analysed.

    Original languageEnglish (US)
    Title of host publicationProceedings of GECCO 2007
    Subtitle of host publicationGenetic and Evolutionary Computation Conference
    Number of pages10
    StatePublished - 2007
    Event9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007 - London, United Kingdom
    Duration: Jul 7 2007Jul 11 2007

    Publication series

    NameProceedings of GECCO 2007: Genetic and Evolutionary Computation Conference


    Other9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007
    Country/TerritoryUnited Kingdom


    • Evolutionary robotics
    • Forward models
    • Games
    • Neural networks
    • System identification

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Theoretical Computer Science


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