mlr_fselectors_genetic_search: Feature Selection with Genetic Search

In mlr3fselect: Feature Selection for 'mlr3'

Feature Selection with Genetic Search

Description

Feature selection using the Genetic Algorithm from the package genalg.

Dictionary

This FSelector can be instantiated with the associated sugar function fs():

fs("genetic_search")

Control Parameters

For the meaning of the control parameters, see genalg::rbga.bin(). genalg::rbga.bin() internally terminates after iters iteration. We set ìters = 100000 to allow the termination via our terminators. If more iterations are needed, set ìters to a higher value in the parameter set.

Super classes

mlr3fselect::FSelector -> mlr3fselect::FSelectorBatch -> FSelectorBatchGeneticSearch

Methods

Public methods

• FSelectorBatchGeneticSearch$new()

• FSelectorBatchGeneticSearch$clone()

Inherited methods

• mlr3fselect::FSelector$format()
• mlr3fselect::FSelector$help()
• mlr3fselect::FSelector$print()
• mlr3fselect::FSelectorBatch$optimize()

---

Method new()

Creates a new instance of this R6 class.

Usage

FSelectorBatchGeneticSearch$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FSelectorBatchGeneticSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other FSelector: FSelector, mlr_fselectors, mlr_fselectors_design_points, mlr_fselectors_exhaustive_search, mlr_fselectors_random_search, mlr_fselectors_rfe, mlr_fselectors_rfecv, mlr_fselectors_sequential, mlr_fselectors_shadow_variable_search

Examples

# Feature Selection


# retrieve task and load learner
task = tsk("penguins")
learner = lrn("classif.rpart")

# run feature selection on the Palmer Penguins data set
instance = fselect(
  fselector = fs("genetic_search"),
  task = task,
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 10
)

# best performing feature set
instance$result

# all evaluated feature sets
as.data.table(instance$archive)

# subset the task and fit the final model
task$select(instance$result_feature_set)
learner$train(task)

mlr3fselect documentation built on Aug. 8, 2025, 7:46 p.m.