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Terminator that stops after a number of evaluations

Source: R/TerminatorEvals.R
mlr_terminators_evals.Rd

Class to terminate the optimization depending on the number of evaluations. An evaluation is defined by one resampling of a parameter value. The total number of evaluations \(B\) is defined as

$$ B = \mathtt{n\_evals} + \mathtt{k} * D $$ where \(D\) is the dimension of the search space.

Dictionary

This Terminator can be instantiated via the dictionary mlr_terminators or with the associated sugar function trm():

mlr_terminators$get("evals")
trm("evals")

Parameters

n_evals

integer(1)
See formula above. Default is 100.

k

integer(1)
See formula above. Default is 0.

See also

Other Terminator: Terminator, mlr_terminators, mlr_terminators_clock_time, mlr_terminators_combo, mlr_terminators_none, mlr_terminators_perf_reached, mlr_terminators_run_time, mlr_terminators_stagnation, mlr_terminators_stagnation_batch, mlr_terminators_stagnation_hypervolume

Super class

bbotk::Terminator -> TerminatorEvals

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

TerminatorEvals$new()

Method is_terminated()

Is TRUE iff the termination criterion is positive, and FALSE otherwise.

Usage

TerminatorEvals$is_terminated(archive)

Arguments

archive

(Archive).

Returns

logical(1).

Method clone()

The objects of this class are cloneable with this method.

Usage

TerminatorEvals$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

TerminatorEvals$new()
#> <TerminatorEvals>: Number of Evaluation
#> * Parameters: n_evals=100, k=0

# 5 evaluations in total
trm("evals", n_evals = 5)
#> <TerminatorEvals>: Number of Evaluation
#> * Parameters: n_evals=5, k=0

# 3 * [dimension of search space] evaluations in total
trm("evals", n_evals = 0, k = 3)
#> <TerminatorEvals>: Number of Evaluation
#> * Parameters: n_evals=0, k=3

# (3 * [dimension of search space] + 1) evaluations in total
trm("evals", n_evals = 1, k = 3)
#> <TerminatorEvals>: Number of Evaluation
#> * Parameters: n_evals=1, k=3