GPareto: Gaussian Processes for Pareto Front Estimation and Optimization

Gaussian process regression models, a.k.a. Kriging models, are applied to global multi-objective optimization of black-box functions. Multi-objective Expected Improvement and Step-wise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.

Version: 1.1.8
Depends: DiceKriging, emoa
Imports: Rcpp (≥ 0.12.15), methods, rgenoud, pbivnorm, pso, randtoolbox, KrigInv, MASS, DiceDesign, ks, rgl
LinkingTo: Rcpp
Suggests: knitr, DiceOptim
Published: 2024-01-26
DOI: 10.32614/CRAN.package.GPareto
Author: Mickael Binois, Victor Picheny
Maintainer: Mickael Binois <mickael.binois at>
License: GPL-3
NeedsCompilation: yes
Citation: GPareto citation info
Materials: README NEWS
In views: Optimization
CRAN checks: GPareto results


Reference manual: GPareto.pdf
Vignettes: a guide to the GPareto package


Package source: GPareto_1.1.8.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): GPareto_1.1.8.tgz, r-oldrel (arm64): GPareto_1.1.8.tgz, r-release (x86_64): GPareto_1.1.8.tgz, r-oldrel (x86_64): GPareto_1.1.8.tgz
Old sources: GPareto archive

Reverse dependencies:

Reverse imports: GPGame
Reverse suggests: biopixR, DiceOptim


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