Scalor that returns a the number of (weakly, epsilon-) dominated or dominating individuals for each individuum.

Configuration Parameters

  • 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.

Supported Operand Types

Supported Param classes are: ParamLgl, ParamInt, ParamDbl, ParamFct

Dictionary

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("domcount")
scls("domcount")  # takes vector IDs, returns list of Scalors

# long form:
dict_scalors$get("domcount")

Super classes

miesmuschel::MiesOperator -> miesmuschel::Scalor -> ScalorDomcount

Methods

Inherited methods


Method new()

Initialize the ScalorNondom object.

Usage


Method clone()

The objects of this class are cloneable with this method.

Usage

ScalorDomcount$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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