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GPareto

GPareto: Gaussian Processes for Pareto Front Estimation and Optimization

This R package provides tools for multi-objective optimization of expensive black-box functions along with estimation of Pareto fronts.

Installation

For the stable version: http://cran.r-project.org/package=GPareto

This the development version, contributions are welcomed.

Note: roxygen is used for the documentation

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Install

install.packages('GPareto')

Monthly Downloads

1,071

Version

1.1.8

License

GPL-3

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Last Published

January 26th, 2024

Functions in GPareto (1.1.8)

ParetoSetDensity

Estimation of Pareto set density
GPareto-package

Package GPareto
crit_EHI

Expected Hypervolume Improvement with m objectives
crit_SMS

Analytical expression of the SMS-EGO criterion with m>1 objectives
crit_SUR

Analytical expression of the SUR criterion for two or three objectives.
ZDT1

Test functions of x
crit_EMI

Expected Maximin Improvement with m objectives
CPF

Conditional Pareto Front simulations
GParetoptim

Sequential multi-objective Expected Improvement maximization and model re-estimation, with a number of iterations fixed in advance by the user
checkPredict

Prevention of numerical instability for a new observation
plotGPareto

Plot multi-objective optimization results and post-processing
plotParetoEmp

Pareto front visualization
fastfun-class

Class for fast to compute objective.
nonDomSet

Non-dominated points with respect to a reference
crit_optimizer

Maximization of multiobjective infill criterion
fastfun

Fast-to-evaluate function wrapper
integration_design_optim

Function to build integration points (for the SUR criterion)
easyGParetoptim

EGO algorithm for multiobjective optimization
getDesign

Get design corresponding to an objective target
crit_qEHI

Batch Expected Hypervolume Improvement with m objectives
predict_kms

Predict function for list of km models.
plotSymDifRNP

Symmetrical difference of RNP sets
plot_uncertainty

Plot uncertainty
plotSymDevFun

Display the Symmetric Deviation Function
plotParetoGrid

Visualisation of Pareto front and set