E.g., for calculation of dominated hypervolume.
approximateRefPoints(df, obj.cols = c("f1", "f2"), offset = 0, as.df = FALSE)[list | data.frame]
[data.frame]
Data frame with the required structure, i.e. the data frame must contain a problem column "prob" as well as objective column(s).
[character(>= 2)]
Column names of the objective functions.
Default is c("f1", "f2"), i.e., the bi-objective case is assumed.
[numeric(1)]
Offset added to reference points.
Default is 0.
[logical(1)]
Should a data.frame be returned?
Default is FALSE. In this case a named list is returned.
Other EMOA performance assessment tools:
approximateNadirPoint(),
approximateRefSets(),
computeDominanceRanking(),
emoaIndEps(),
makeEMOAIndicator(),
niceCellFormater(),
normalize(),
plotDistribution(),
plotFront(),
plotScatter2d(),
plotScatter3d(),
toLatex()