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

$$B = \mathrm{n_evals} + k * D$$ 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():

### Method is_terminated()

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

#### 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