CPFs {moocore} | R Documentation |
Conditional Pareto fronts obtained from Gaussian processes simulations.
Description
The data has the only goal of providing an example of use of vorob_t()
and
vorob_dev()
. 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
MickaĆ«l Binois, David Ginsbourger, Olivier 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)
vorob_t(CPFs, reference = c(2, 200))
[Package moocore version 0.1.8 Index]