Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Mickaël Binois, Victor Picheny
Title: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis
Abstract: The GPareto package for R provides multi-objective optimization algorithms for expensive black-box functions and an ensemble of dedicated uncertainty quantification methods. Popular methods such as efficient global optimization in the mono-objective case rely on Gaussian processes or kriging to build surrogate models. Driven by the prediction uncertainty given by these models, several infill criteria have also been proposed in a multi-objective setup to select new points sequentially and efficiently cope with severely limited evaluation budgets. They are implemented in the package, in addition with Pareto front estimation and uncertainty quantification visualization in the design and objective spaces. Finally, it attempts to fill the gap between expert use of the corresponding methods and user-friendliness, where many efforts have been put on providing graphical postprocessing, standard tuning and interactivity.

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Paper: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis     Download PDF (Downloads: 66)
Supplements:
GPareto_1.1.3.tar.gz: R source package Download (Downloads: 7; 1MB)
v89i08.R: R replication code Download (Downloads: 5; 35KB)
benchmark1.RData: Supplementary data (R binary format) Download (Downloads: 5; 42KB)
benchmark2.RData: Supplementary data (R binary format) Download (Downloads: 6; 113KB)
SPOT_1.1.0.tar.gz: R source package Download (Downloads: 5; 1MB)

DOI: 10.18637/jss.v089.i08

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.