| dict_scalors_hypervolume | R Documentation |
Scalor that returns the hypervolume of each individual, relative to nadir and as a contribution over baseline.
The returned scalar value is the measure of all points that have fitnesses that are
greater than the respective value in nadir in all dimensions, and
smaller than the respective value in the given point in all dimensions, and
greater than all points in baseline in at least one dimension.
baseline should probably be a paradox::ContextPV and generate fitness values from the Archive
in the context using mies_get_fitnesses.
scale_output :: logical(1)
Whether to scale output to lie between 0 and 1.
nadir :: numeric
Nadir of fitness values relative to which hypervolume ution is calculated.
baseline :: matrix
Fitness-matrix with one column per objective, giving a population over which the hypervolume improvement should be calculated.
Supported Domain classes are: p_lgl ('ParamLgl'), p_int ('ParamInt'), p_dbl ('ParamDbl'), p_fct ('ParamFct')
This Scalor can be created with the short access form scl()
(scls() to get a list), or through the the dictionary
dict_scalors in the following way:
# preferred:
scl("hypervolume")
scls("hypervolume") # takes vector IDs, returns list of Scalors
# long form:
dict_scalors$get("hypervolume")
miesmuschel::MiesOperator -> miesmuschel::Scalor -> ScalorHypervolume
new()Initialize the ScalorHypervolume object.
ScalorHypervolume$new()
clone()The objects of this class are cloneable with this method.
ScalorHypervolume$clone(deep = FALSE)
deepWhether to make a deep clone.
Other scalors:
Scalor,
dict_scalors_aggregate,
dict_scalors_domcount,
dict_scalors_fixedprojection,
dict_scalors_nondom,
dict_scalors_one,
dict_scalors_proxy,
dict_scalors_single
sv = scl("hypervolume")
p = ps(x = p_dbl(-5, 5))
# dummy data; note that ScalorHV does not depend on data content
data = data.frame(x = rep(0, 5))
fitnesses = matrix(c(1, 5, 2, 3, 0, 3, 1, 0, 10, 8), ncol = 2)
sv$param_set$values$baseline = matrix(c(1, 1), ncol = 2)
sv$param_set$values$nadir = c(0, -1)
sv$prime(p)
sv$operate(data, fitnesses)
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