View Item 
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        •   Utrecht University Student Theses Repository Home
        • UU Theses Repository
        • Theses
        • View Item
        JavaScript is disabled for your browser. Some features of this site may not work without it.

        Browse

        All of UU Student Theses RepositoryBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

        Tree-GP: A Scalable Bayesian Global Numerical Optimization algorithm

        Thumbnail
        View/Open
        paper.pdf (4.408Mb)
        Publication date
        2015
        Author
        Veenendaal, G. van
        Metadata
        Show full item record
        Summary
        This paper presents the Tree-GP algorithm: a scalable Bayesian global numerical optimization algorithm. The algorithm focuses on optimizing evaluation functions that are very expensive to evaluate. It models the search space using a mixture model of Gaussian process regression models. This model is then used to find new evaluation points, using our new CMPVR acquisition criteria function that combines both the mean and variance of the predictions made by the model. Conventional Gaussian process based Bayesian optimization algorithms often do not scale well in the total amount of function evaluations. Tree-GP resolves this issue by using a mixture model of Gaussian process regression models stored in a vantage-point tree. This makes the algorithm almost linear in the total amount of function evaluations.
        URI
        https://studenttheses.uu.nl/handle/20.500.12932/19408
        Collections
        • Theses
        Utrecht university logo