dict_selectors_random: Random Selector

In miesmuschel: Mixed Integer Evolution Strategies

Random Selector

Description

Random selector that disregards fitness and individual values and selects individuals randomly. Depending on the configuration parameter replace, it samples with or without replacement.

Configuration Parameters

• sample_unique :: character(1)
  Whether to sample individuals globally unique ("global"), unique within groups ("groups"), or not unique at all ("no", sample with replacement). This is done with best effort; if group_size (when sample_unique is "groups") or n_select (when sample_unique is "global") is greater than nrow(values), then individuals are selected with as few repeats as possible. Initialized to "groups".

Supported Operand Types

Supported Domain classes are: p_lgl ('ParamLgl'), p_int ('ParamInt'), p_dbl ('ParamDbl'), p_fct ('ParamFct')

Dictionary

This Selector can be created with the short access form sel() (sels() to get a list), or through the the dictionary dict_selectors in the following way:

# preferred:
sel("random")
sels("random")  # takes vector IDs, returns list of Selectors

# long form:
dict_selectors$get("random")

Super classes

miesmuschel::MiesOperator -> miesmuschel::Selector -> SelectorRandom

Methods

Public methods

• SelectorRandom$new()

• SelectorRandom$clone()

Inherited methods

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

---

Method new()

Initialize the SelectorRandom object.

Usage

SelectorRandom$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

SelectorRandom$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other selectors: Selector, SelectorScalar, dict_selectors_best, dict_selectors_maybe, dict_selectors_null, dict_selectors_proxy, dict_selectors_sequential, dict_selectors_tournament

Examples

set.seed(1)
sr = sel("random")
p = ps(x = p_dbl(-5, 5))
# dummy data; note that SelectorRandom does not depend on data content
data = data.frame(x = rep(0, 5))
fitnesses = c(1, 5, 2, 3, 0)

sr$prime(p)

sr$operate(data, fitnesses, 2)
sr$operate(data, fitnesses, 2)
sr$operate(data, fitnesses, 2)

sr$operate(data, fitnesses, 4)
sr$operate(data, fitnesses, 4)
sr$operate(data, fitnesses, 4)

miesmuschel documentation built on April 4, 2025, 5:19 a.m.