Evolution of neural networks for helicopter control: Why modularity matters

Renzo De Nardi, Julian Togelius, Owen E. Holland, Simon M. Lucas

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

    Abstract

    The problem of the automatic development of controllers for vehicles for which the exact characteristics are not known is considered in the context of miniature helicopter flocking. A methodology is proposed in which neural network based controllers are evolved in a simulation using a dynamic model qualitatively similar to the physical helicopter. Several network architectures and evolutionary sequences are investigated, and two approaches are found that can evolve very competitive controllers. The division of the neural network into modules and of the task into incremental steps seems to be a precondition for success, and we analyse why this might be so.

    Original languageEnglish (US)
    Title of host publication2006 IEEE Congress on Evolutionary Computation, CEC 2006
    Pages1799-1806
    Number of pages8
    StatePublished - 2006
    Event2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
    Duration: Jul 16 2006Jul 21 2006

    Publication series

    Name2006 IEEE Congress on Evolutionary Computation, CEC 2006

    Other

    Other2006 IEEE Congress on Evolutionary Computation, CEC 2006
    CountryCanada
    CityVancouver, BC
    Period7/16/067/21/06

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Software
    • Theoretical Computer Science

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  • Cite this

    De Nardi, R., Togelius, J., Holland, O. E., & Lucas, S. M. (2006). Evolution of neural networks for helicopter control: Why modularity matters. In 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (pp. 1799-1806). [1688525] (2006 IEEE Congress on Evolutionary Computation, CEC 2006).