Nonlinear fitness–space–structure adaptation and principal component analysis in genetic algorithms: an application to x-ray reflectivity analysis

Jouni Tiilikainen, J-M Tilli, Vesa Bosund, Marco Mattila, Teppo Hakkarainen, Veli-Matti Airaksinen, Harri Lipsanen

    Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

    Abstrakti

    Two novel genetic algorithms implementing principal component analysis
    and an adaptive nonlinear fitness–space–structure technique are presented
    and compared with conventional algorithms in x-ray reflectivity analysis.
    Principal component analysis based on Hessian or interparameter covariance
    matrices is used to rotate a coordinate frame. The nonlinear adaptation
    applies nonlinear estimates to reshape the probability distribution of the trial
    parameters. The simulated x-ray reflectivity of a realistic model of a periodic
    nanolaminate structure was used as a test case for the fitting algorithms.
    The novel methods had significantly faster convergence and less stagnation
    than conventional non-adaptive genetic algorithms. The covariance
    approach needs no additional curve calculations compared with conventional
    methods, and it had better convergence properties than the computationally
    expensive Hessian approach. These new algorithms can also be applied to
    other fitting problems where tight interparameter dependence is present.
    AlkuperäiskieliEnglanti
    Sivut215-218
    Sivumäärä4
    JulkaisuJournal of Physics D: Applied Physics
    Vuosikerta40
    Numero1
    DOI - pysyväislinkit
    TilaJulkaistu - 15 joulukuuta 2006
    OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

    Tutkimusalat

    • curve fitting
    • genetic algorthm
    • nanolaminate
    • x-ray reflectivity

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