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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

search_space = domain = ps(x = p_dbl(lower = -1, upper = 1))

codomain = ps(y = p_dbl(tags = "minimize"))

objective_function = function(xs) {
  list(y = as.numeric(xs)^2)
}

objective = ObjectiveRFun$new(
 fun = objective_function,
 domain = domain,
 codomain = codomain)

instance = OptimInstanceBatchSingleCrit$new(
 objective = objective,
 search_space = search_space,
 terminator = trm("evals", n_evals = 10))


optimizer = opt("random_search")

# modifies the instance by reference
optimizer$optimize(instance)
#>             x  x_domain            y
#>         <num>    <list>        <num>
#> 1: 0.02069745 <list[1]> 0.0004283846

# returns best scoring evaluation
instance$result
#>             x  x_domain            y
#>         <num>    <list>        <num>
#> 1: 0.02069745 <list[1]> 0.0004283846

# allows access of data.table of full path of all evaluations
as.data.table(instance$archive$data)
#>               x            y  x_domain           timestamp batch_nr
#>           <num>        <num>    <list>              <POSc>    <int>
#>  1: -0.38728307 0.1499881785 <list[1]> 2025-10-14 15:41:36        1
#>  2:  0.72684146 0.5282985025 <list[1]> 2025-10-14 15:41:36        2
#>  3: -0.39775191 0.1582065835 <list[1]> 2025-10-14 15:41:36        3
#>  4: -0.39600806 0.1568223849 <list[1]> 2025-10-14 15:41:36        4
#>  5:  0.02069745 0.0004283846 <list[1]> 2025-10-14 15:41:36        5
#>  6:  0.23661496 0.0559866376 <list[1]> 2025-10-14 15:41:36        6
#>  7: -0.75617397 0.5717990711 <list[1]> 2025-10-14 15:41:36        7
#>  8:  0.86804544 0.7535028826 <list[1]> 2025-10-14 15:41:36        8
#>  9: -0.24157158 0.0583568303 <list[1]> 2025-10-14 15:41:36        9
#> 10: -0.51128462 0.2614119665 <list[1]> 2025-10-14 15:41:36       10