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:	1.1.7
Depends:	DiceKriging, emoa
Imports:	Rcpp (≥ 0.12.15), methods, rgenoud, pbivnorm, pso, randtoolbox, KrigInv, MASS, DiceDesign, ks, rgl
LinkingTo:	Rcpp
Suggests:	knitr, DiceOptim
Published:	2022-06-24
Author:	Mickael Binois, Victor Picheny
Maintainer:	Mickael Binois <mickael.binois at inria.fr>
BugReports:	https://github.com/mbinois/GPareto/issues
License:	GPL-3
URL:	https://github.com/mbinois/GPareto
NeedsCompilation:	yes
Citation:	GPareto citation info
Materials:	README NEWS
In views:	Optimization
CRAN checks:	GPareto results

Documentation:

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

Downloads:

Package source:	GPareto_1.1.7.tar.gz
Windows binaries:	r-devel: GPareto_1.1.7.zip, r-release: GPareto_1.1.7.zip, r-oldrel: GPareto_1.1.7.zip
macOS binaries:	r-release (arm64): GPareto_1.1.7.tgz, r-oldrel (arm64): GPareto_1.1.7.tgz, r-release (x86_64): GPareto_1.1.7.tgz, r-oldrel (x86_64): GPareto_1.1.7.tgz
Old sources:	GPareto archive

Reverse dependencies:

Reverse imports:	GPGame, moko
Reverse suggests:	DiceOptim

Linking:

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