CRAN/E | GPareto

GPareto

Gaussian Processes for Pareto Front Estimation and Optimization

Installation

About

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.

Citation GPareto citation info
github.com/mbinois/GPareto
Bug report File report

Key Metrics

Version 1.1.8
Published 2024-01-26 91 days ago
Needs compilation? yes
License GPL-3
CRAN checks GPareto results

Downloads

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Maintainer

Maintainer

Mickael Binois

mickael.binois@inria.fr

Authors

Mickael Binois
Victor Picheny

Material

README
NEWS
Reference manual
Package source

In Views

Optimization

Vignettes

a guide to the GPareto package

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

GPareto archive

Depends

DiceKriging
emoa

Imports

Rcpp ≥ 0.12.15
methods
rgenoud
pbivnorm
pso
randtoolbox
KrigInv
MASS
DiceDesign
ks
rgl

Suggests

knitr
DiceOptim

LinkingTo

Rcpp

Reverse Imports

GPGame
moko

Reverse Suggests

DiceOptim