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.

Depends: DiceKriging, emoa
Imports: Rcpp (≥ 0.12.15), methods, rgenoud, pbivnorm, pso, randtoolbox, KrigInv, MASS, DiceDesign, ks, rgl
LinkingTo: Rcpp
Suggests: knitr
Published: 2020-04-01
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_1.1.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: GPareto_1.1.4.1.tgz, r-oldrel: GPareto_1.1.4.1.tgz
Old sources: GPareto archive

Reverse dependencies:

Reverse imports: GPGame, moko
Reverse suggests: DiceOptim


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