| mlr_terminators_evals | R Documentation |
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.
This Terminator can be instantiated via the
dictionary mlr_terminators or with the associated
sugar function trm():
mlr_terminators$get("evals")
trm("evals")
n_evalsinteger(1)
See formula above. Default is 100.
kinteger(1)
See formula above. Default is 0.
bbotk::Terminator -> TerminatorEvals
new()Creates a new instance of this R6 class.
TerminatorEvals$new()
is_terminated()Is TRUE iff the termination criterion is positive, and FALSE
otherwise.
TerminatorEvals$is_terminated(archive)
archive(Archive).
logical(1).
clone()The objects of this class are cloneable with this method.
TerminatorEvals$clone(deep = FALSE)
deepWhether to make a deep clone.
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
TerminatorEvals$new()
# 5 evaluations in total
trm("evals", n_evals = 5)
# 3 * [dimension of search space] evaluations in total
trm("evals", n_evals = 0, k = 3)
# (3 * [dimension of search space] + 1) evaluations in total
trm("evals", n_evals = 1, k = 3)
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