Optimization via Random Search

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

• OptimizerBatchRandomSearch$new()

• OptimizerBatchRandomSearch$clone()

Inherited methods

• bbotk::Optimizer$format()
• bbotk::Optimizer$help()
• bbotk::Optimizer$print()
• bbotk::OptimizerBatch$optimize()

---

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:  -8.1 -2.03   -92 2026-02-28 07:04:23        1        -8.1       -2.03
#>  2:  -0.9 -4.73    -2 2026-02-28 07:04:23        1        -0.9       -4.73
#>  3:   7.5 -2.59   -20 2026-02-28 07:04:23        1         7.5       -2.59
#>  4:  -3.9  2.45   -54 2026-02-28 07:04:23        1        -3.9        2.45
#>  5:  -9.8  2.10  -154 2026-02-28 07:04:23        1        -9.8        2.10
#>  6:   8.8  2.86   -71 2026-02-28 07:04:23        1         8.8        2.86
#>  7:   8.7 -1.29   -38 2026-02-28 07:04:23        1         8.7       -1.29
#>  8:  -0.2  1.24   -13 2026-02-28 07:04:23        1        -0.2        1.24
#>  9:  -4.8  3.63   -80 2026-02-28 07:04:23        1        -4.8        3.63
#> 10:  -1.5  2.02   -28 2026-02-28 07:04:23        1        -1.5        2.02
#> 11:   3.1  2.15   -18 2026-02-28 07:04:23        2         3.1        2.15
#> 12:   0.7 -4.65     6 2026-02-28 07:04:23        2         0.7       -4.65
#> 13:  -2.3 -3.23    -8 2026-02-28 07:04:23        2        -2.3       -3.23
#> 14:   8.9  4.38   -93 2026-02-28 07:04:23        2         8.9        4.38
#> 15:   7.5  4.72   -80 2026-02-28 07:04:23        2         7.5        4.72
#> 16:  -2.9 -2.57   -14 2026-02-28 07:04:23        2        -2.9       -2.57
#> 17:   4.7  1.59   -18 2026-02-28 07:04:23        2         4.7        1.59
#> 18:  -1.9  0.06   -15 2026-02-28 07:04:23        2        -1.9        0.06
#> 19:   4.3 -1.26     2 2026-02-28 07:04:23        2         4.3       -1.26
#> 20:   6.1 -0.77   -12 2026-02-28 07:04:23        2         6.1       -0.77
#>        x1    x2     y           timestamp batch_nr x_domain_x1 x_domain_x2
#>     <num> <num> <num>              <POSc>    <int>       <num>       <num>

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