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: 1.1.0
Depends: DiceKriging (≥ 1.5.3), emoa, methods
Imports: Rcpp (≥ 0.11.1), rgenoud, pbivnorm, pso, randtoolbox, KrigInv, MASS, DiceDesign (≥ 1.4), ks
LinkingTo: Rcpp
Suggests: knitr
Published: 2017-06-29
Author: Mickael Binois, Victor Picheny
Maintainer: Mickael Binois <mickael.binois at>
License: GPL-3
NeedsCompilation: yes
Materials: NEWS
CRAN checks: GPareto results


Reference manual: GPareto.pdf
Vignettes: a guide to the GPareto package
Package source: GPareto_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: GPareto_1.1.0.tgz
OS X Mavericks binaries: r-oldrel: GPareto_1.1.0.tgz
Old sources: GPareto archive

Reverse dependencies:

Reverse imports: GPGame, moko
Reverse suggests: DiceOptim


Please use the canonical form to link to this page.