Scalor that returns a the number of (weakly, epsilon-) dominated or dominating individuals for each individuum.
output :: character(1)
What to count: individuals that are being dominated by the point under consideration("count_dominated"),
or individuals that do not dominate the point under consideration ("count_not_dominating").
In both cases, a larger output means the individual is "better", in some way, according to the fitness values.
Initialized with "count_not_dominating".
epsilon :: numeric
Epsilon-value for non-dominance, as used by rank_nondominated. Initialized to 0.
jitter :: logical(1)
Whether to add random jitter to points, with magnitude sqrt(.Machine$double.eps) relative to fitness values.
This is used to effectively break ties.
scale_output :: logical(1)
Whether to scale output by the total numberof individuals, giving output between 0 and 1 (inclusive) when TRUE
or integer outputs ranging from 0 and nrow(fitnesses) (inclusive) when FALSE. Initialized to TRUE.
This Scalor can be created with the short access form scl()
(scls() to get a list), or through the the dictionary
dict_scalors in the following way:
miesmuschel::MiesOperator -> miesmuschel::Scalor -> ScalorDomcount
p = ps(x = p_dbl(-5, 5))
data = data.frame(x = rep(0, 5))
sd = scl("domcount")
sd$prime(p)
(fitnesses = matrix(c(1, 5, 2, 3, 0, 3, 1, 0, 10, 8), ncol = 2))
#> [,1] [,2]
#> [1,] 1 3
#> [2,] 5 1
#> [3,] 2 0
#> [4,] 3 10
#> [5,] 0 8
# to see the fitness matrix, use:
## plot(fitnesses, pch = as.character(1:5))
# note that for both 2 and 4, all points do not dominate them
# their value is therefore 1
sd$operate(data, fitnesses)
#> [1] 0.8 1.0 0.6 1.0 0.8
sd$param_set$values$scale_output = FALSE
sd$operate(data, fitnesses)
#> [1] 4 5 3 5 4
sd$param_set$values$output = "count_dominated"
# point 4 dominates three other points, point 2 only one other point.
sd$operate(data, fitnesses)
#> [1] 0 1 0 3 0