dict_scalors_nondom {miesmuschel}

Nondominated Sorting Scalor

Description

Scalor that returns a the rank of the pareto-front in nondominated sorting as scale. Higher ranks indocate higher fitnesses and therefore "better" individuals.

Configuration Parameters

• epsilon

• nadir

• jitter

• scale_output

• tiebreak

Supported Operand Types

Supported Domain classes are: p_lgl ('ParamLgl'), p_int ('ParamInt'), p_dbl ('ParamDbl'), p_fct ('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("nondom")
scls("nondom")  # takes vector IDs, returns list of Scalors

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

Super classes

miesmuschel::MiesOperator -> miesmuschel::Scalor -> ScalorNondom

Methods

Public methods

• ScalorNondom$new()

• ScalorNondom$clone()

Inherited methods

• miesmuschel::MiesOperator$help()
• miesmuschel::MiesOperator$operate()
• miesmuschel::MiesOperator$prime()
• miesmuschel::MiesOperator$print()
• miesmuschel::MiesOperator$repr()

Method new()

Initialize the ScalorNondom object.

Usage

ScalorNondom$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

ScalorNondom$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other scalors: Scalor, dict_scalors_aggregate, dict_scalors_domcount, dict_scalors_fixedprojection, dict_scalors_hypervolume, dict_scalors_one, dict_scalors_proxy, dict_scalors_single

Examples

so = scl("nondom")
p = ps(x = p_dbl(-5, 5))
# dummy data; note that ScalorNondom 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)

so$prime(p)

so$operate(data, fitnesses)