Rank by Nondominated Front and Crowding Distance or Hypervolume Contribution

Rank individuals by nondominating sorted front first and by hypervolume contribution or crowding distance second.

Ties are broken randomly by adding random noise of relative magnitude .Machine$double.eps * 2^10 to points.

overallRankMO(fitness, sorting = "crowding", ref.point)

Arguments

fitness	[matrix] fitness matrix, one column per individual.
sorting	[character(1)] one of "domhv" or "crowding" (default).
ref.point	[numeric] reference point for hypervolume, must be given if sorting is "domhv".

Value

[integer] vector of ranks with length ncol(fitness), lower ranks are associated with individuals that tend to dominate more points and that tend to have larger crowding distance or hypervolume contribution.