The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
hv_contributions() ignores dominated points by default.
Set ignore_dominated=FALSE to restore the previous
behavior. The 3D case uses the HVC3D algorithm.any_dominated().generate_ndset() to generate random
nondominated sets with different shapes.is_nondominated(), any_dominated() and
pareto_rank() now handle single-objective inputs correctly
(#27) (#29).is_nondominated() and filter_dominated()
are faster for dimensions larger than 3.is_nondominated() and filter_dominated()
are now stable in 2D and 3D with keep_weakly=FALSE, that
is, only the first of duplicated points is marked as nondominated.hv_approx().hv_contributions() is much faster for 2D
inputs.DTLZLinearShape.8d.front.60pts.10 and
ran.10pts.9d.10.hypervolume() now uses the HV3D+ algorithm for the
3D case and the HV4D+ algorithm for the 4D case. For dimensions larger
than 4, the recursive algorithm uses HV4D+ as the base case, which is
significantly faster.
read_datasets() is significantly faster for large
files.
is_nondominated() and
filter_dominated() are faster for 3D inputs.
vorobT() and vorobDev() to
vorob_t() and vorob_dev() to be consistent
with other function names.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.