Conditional Pareto fronts obtained from Gaussian processes simulations.

The data has the only goal of providing an example of use of vorobT() and vorobDev(). It has been obtained by fitting two Gaussian processes on 20 observations of a bi-objective problem, before generating conditional simulation of both GPs at different locations and extracting non-dominated values of coupled simulations.

Usage

CPFs

Format

A data frame with 2967 observations on the following 3 variables.

f1

first objective values.

f2

second objective values.

set

indices of corresponding conditional Pareto fronts.

Source

M Binois, D Ginsbourger, O Roustant (2015). “Quantifying uncertainty on Pareto fronts with Gaussian process conditional simulations.” European Journal of Operational Research, 243(2), 386--394. doi:10.1016/j.ejor.2014.07.032 .

Examples

data(CPFs)

res <- vorobT(CPFs, reference = c(2, 200))
eafplot(CPFs[,1:2], sets = CPFs[,3], percentiles = c(0, 20, 40, 60, 80, 100),
       col = gray(seq(0.8, 0.1, length.out = 6)^2), type = "area",
       legend.pos = "bottomleft", extra.points = res$VE, extra.col = "cyan")