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    Using BART for Multiobjective Optimization of Noisy Multiple Objectives

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    Type
    Preprint
    Authors
    Horiguchi, Akira
    Santner, Thomas J.
    Sun, Ying cc
    Pratola, Matthew T.
    KAUST Department
    Statistics Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2021-01-04
    Permanent link to this record
    http://hdl.handle.net/10754/666900
    
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    Abstract
    Techniques to reduce the energy burden of an Industry 4.0 ecosystem often require solving a multiobjective optimization problem. However, collecting experimental data can often be either expensive or time-consuming. In such cases, statistical methods can be helpful. This article proposes Pareto Front (PF) and Pareto Set (PS) estimation methods using Bayesian Additive Regression Trees (BART), which is a non-parametric model whose assumptions are typically less restrictive than popular alternatives, such as Gaussian Processes. The performance of our BART-based method is compared to a GP-based method using analytic test functions, demonstrating convincing advantages. Finally, our BART-based methodology is applied to a motivating Industry 4.0 engineering problem.
    Publisher
    arXiv
    arXiv
    2101.02558
    Additional Links
    https://arxiv.org/pdf/2101.02558
    Collections
    Preprints; Statistics Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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