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Optimization via Random Search

Source: R/OptimizerBatchRandomSearch.R
mlr_optimizers_random_search.Rd

OptimizerBatchRandomSearch class that implements a simple Random Search.

In order to support general termination criteria and parallelization, we evaluate points in a batch-fashion of size batch_size. Larger batches mean we can parallelize more, smaller batches imply a more fine-grained checking of termination criteria.

Source

Bergstra J, Bengio Y (2012). “Random Search for Hyper-Parameter Optimization.” Journal of Machine Learning Research, 13(10), 281–305. https://jmlr.csail.mit.edu/papers/v13/bergstra12a.html.

Dictionary

This Optimizer can be instantiated via the dictionary mlr_optimizers or with the associated sugar function opt():

mlr_optimizers$get("random_search")
opt("random_search")

Parameters

batch_size

integer(1)
Maximum number of points to try in a batch.

Progress Bars

$optimize() supports progress bars via the package progressr combined with a Terminator. Simply wrap the function in progressr::with_progress() to enable them. We recommend to use package progress as backend; enable with progressr::handlers("progress").

Super classes

bbotk::Optimizer -> bbotk::OptimizerBatch -> OptimizerBatchRandomSearch

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerBatchRandomSearch$new()

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerBatchRandomSearch$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# define the objective function
fun = function(xs) {
  list(y = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
}

# set domain
domain = ps(
  x1 = p_dbl(-10, 10),
  x2 = p_dbl(-5, 5)
)

# set codomain
codomain = ps(
  y = p_dbl(tags = "maximize")
)

# create objective
objective = ObjectiveRFun$new(
  fun = fun,
  domain = domain,
  codomain = codomain,
  properties = "deterministic"
)

# initialize instance
instance = oi(
  objective = objective,
  terminator = trm("evals", n_evals = 20)
)

# load optimizer
optimizer = opt("random_search", batch_size = 10)

# trigger optimization
optimizer$optimize(instance)
#>           x1        x2  x_domain        y
#>        <num>     <num>    <list>    <num>
#> 1: 0.7310295 -4.649834 <list[2]> 5.667762

# all evaluated configurations
instance$archive
#> 
#> ── <ArchiveBatch> - Data Table Storage ─────────────────────────────────────────
#>        x1    x2     y           timestamp batch_nr x_domain_x1 x_domain_x2
#>     <num> <num> <num>              <POSc>    <int>       <num>       <num>
#>  1:  -8.2  -0.1  -103 2025-10-24 13:29:12        1        -8.2        -0.1
#>  2:  -5.0  -2.4   -40 2025-10-24 13:29:12        1        -5.0        -2.4
#>  3:  -2.2  -0.8   -13 2025-10-24 13:29:12        1        -2.2        -0.8
#>  4:  -8.1  -2.0   -92 2025-10-24 13:29:12        1        -8.1        -2.0
#>  5:  -0.9  -4.7    -2 2025-10-24 13:29:12        1        -0.9        -4.7
#>  6:   7.5  -2.6   -20 2025-10-24 13:29:12        1         7.5        -2.6
#>  7:  -3.9   2.4   -54 2025-10-24 13:29:12        1        -3.9         2.4
#>  8:  -9.8   2.1  -154 2025-10-24 13:29:12        1        -9.8         2.1
#>  9:   8.8   2.9   -71 2025-10-24 13:29:12        1         8.8         2.9
#> 10:   8.7  -1.3   -38 2025-10-24 13:29:12        1         8.7        -1.3
#> 11:   2.5  -1.0     6 2025-10-24 13:29:13        2         2.5        -1.0
#> 12:   7.3   2.1   -44 2025-10-24 13:29:13        2         7.3         2.1
#> 13:   4.0   3.1   -31 2025-10-24 13:29:13        2         4.0         3.1
#> 14:   3.1   2.2   -18 2025-10-24 13:29:13        2         3.1         2.2
#> 15:   0.7  -4.6     6 2025-10-24 13:29:13        2         0.7        -4.6
#> 16:  -2.3  -3.2    -8 2025-10-24 13:29:13        2        -2.3        -3.2
#> 17:   8.9   4.4   -93 2025-10-24 13:29:13        2         8.9         4.4
#> 18:   7.5   4.7   -80 2025-10-24 13:29:13        2         7.5         4.7
#> 19:  -2.9  -2.6   -14 2025-10-24 13:29:13        2        -2.9        -2.6
#> 20:   4.7   1.6   -18 2025-10-24 13:29:13        2         4.7         1.6
#>        x1    x2     y           timestamp batch_nr x_domain_x1 x_domain_x2

# best performing configuration
instance$result
#>           x1        x2  x_domain        y
#>        <num>     <num>    <list>    <num>
#> 1: 0.7310295 -4.649834 <list[2]> 5.667762