Genetic algorithm using independent component analysis in x-ray reflectivity curve fitting of periodic layer structures

Jouni Tiilikainen, Vesa Bosund, Juha-Matti Tilli, Jaakko Sormunen, Marco Mattila, Teppo Hakkarainen, Harri Lipsanen

    Research output: Contribution to journalArticleScientificpeer-review

    13 Citations (Scopus)

    Abstract

    A novel genetic algorithm (GA) utilizing independent component analysis
    (ICA) was developed for x-ray reflectivity (XRR) curve fitting. EFICA was
    used to reduce mutual information, or interparameter dependences, during
    the combinatorial phase. The performance of the new algorithm was studied
    by fitting trial XRR curves to target curves which were computed using
    realistic multilayer models. The median convergence properties of
    conventional GA, GA using principal component analysis and the novel GA
    were compared. GA using ICA was found to outperform the other methods
    with problems having 41 parameters or more to be fitted without additional
    XRR curve calculations. The computational complexity of the conventional
    methods was linear but the novel method had a quadratic computational
    complexity due to the applied ICA method which sets a practical limit for
    the dimensionality of the problem to be solved. However, the novel
    algorithm had the best capability to extend the fitting analysis based on
    Parratt’s formalism to multiperiodic layer structures.
    Original languageEnglish
    Pages (from-to)6000-6004
    Number of pages5
    JournalJournal of Physics D: Applied Physics
    Volume40
    Issue number19
    DOIs
    Publication statusPublished - 21 Sep 2007
    MoE publication typeA1 Journal article-refereed

    Keywords

    • curve fitting
    • genetic algorithm
    • independent component analysis
    • x-ray reflectivity

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