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dc.contributor.authorHoriguchi, Akira
dc.contributor.authorSantner, Thomas J.
dc.contributor.authorSun, Ying
dc.contributor.authorPratola, Matthew T.
dc.date.accessioned2021-01-14T10:32:22Z
dc.date.available2021-01-14T10:32:22Z
dc.date.issued2021-01-04
dc.identifier.urihttp://hdl.handle.net/10754/666900
dc.description.abstractTechniques 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.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2101.02558
dc.rightsArchived with thanks to arXiv
dc.titleUsing BART for Multiobjective Optimization of Noisy Multiple Objectives
dc.typePreprint
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentEnvironmental Statistics Group
dc.contributor.departmentStatistics Program
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Statistics The Ohio State University Cockins Hall 1958 Neil Ave. Columbus, OH 43210.
dc.identifier.arxivid2101.02558
kaust.personSun, Ying
refterms.dateFOA2021-01-14T10:33:04Z


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