GPareto: Gaussian Processes for Pareto Front Estimation and Optimization

Gaussian process regression models, a.k.a. kriging models, are applied to global multiobjective optimization of black-box functions. Multiobjective Expected Improvement and Stepwise Uncertainty Reduction sequential infill criteria are available. A quantification of uncertainty on Pareto fronts is provided using conditional simulations.

Version: 1.0.1
Depends: DiceKriging (≥ 1.5.3), emoa, methods
Imports: stats, grDevices, graphics, Rcpp (≥ 0.11.1), rgenoud, pbivnorm, pso, randtoolbox, KrigInv, MASS
LinkingTo: Rcpp
Suggests: DiceDesign (≥ 1.4)
Published: 2015-07-03
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
Package source: GPareto_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X Snow Leopard binaries: r-release: GPareto_1.0.1.tgz, r-oldrel: GPareto_1.0.0.tgz
OS X Mavericks binaries: r-release: GPareto_1.0.1.tgz
Old sources: GPareto archive