CPFs: Conditional Pareto fronts obtained from Gaussian processes...

In eaf: Plots of the Empirical Attainment Function

Conditional Pareto fronts obtained from Gaussian processes simulations.

Description

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

\insertRef

BinGinRou2015gaupareaf

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")

eaf documentation built on Sept. 11, 2024, 8:45 p.m.