Optimization via Grid Search — mlr_optimizers_grid

OptimizerBatchGridSearch class that implements grid search. The grid is constructed as a Cartesian product over discretized values per parameter, see paradox::generate_design_grid(). The points of the grid are evaluated in a random order.

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.

Dictionary

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

mlr_optimizers$get("grid_search")
opt("grid_search")

Parameters

resolution: integer(1)
Resolution of the grid, see paradox::generate_design_grid().
param_resolutions: named integer()
Resolution per parameter, named by parameter ID, see paradox::generate_design_grid().
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

Optimizer -> OptimizerBatch -> OptimizerBatchGridSearch

Methods

Inherited methods

`OptimizerBatchGridSearch$new()`

Creates a new instance of this R6 class.

Usage

OptimizerBatchGridSearch$new()

`OptimizerBatchGridSearch$clone()`

The objects of this class are cloneable with this method.

Usage

OptimizerBatchGridSearch$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("grid_search", resolution = 10)

# trigger optimization
optimizer$optimize(instance)
#>          x1        x2  x_domain        y
#>       <num>     <num>    <list>    <num>
#> 1: 1.111111 -1.666667 <list[2]> 7.432099

# 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:     3   0.6    -4 2026-09-03 12:11:05        1           3         0.6
#>  2:     1   1.7   -13 2026-09-03 12:11:05        2           1         1.7
#>  3:     6  -5.0    -7 2026-09-03 12:11:05        3           6        -5.0
#>  4:    10  -0.6   -60 2026-09-03 12:11:05        4          10        -0.6
#>  5:    -8  -0.6   -92 2026-09-03 12:11:05        5          -8        -0.6
#>  6:    -8  -1.7   -87 2026-09-03 12:11:05        6          -8        -1.7
#>  7:    -8  -3.9   -86 2026-09-03 12:11:05        7          -8        -3.9
#>  8:    -8   1.7  -107 2026-09-03 12:11:05        8          -8         1.7
#>  9:     1   2.8   -24 2026-09-03 12:11:05        9           1         2.8
#> 10:    -1   1.7   -21 2026-09-03 12:11:05       10          -1         1.7
#> 11:    -8   2.8  -119 2026-09-03 12:11:05       11          -8         2.8
#> 12:    -8   0.6   -98 2026-09-03 12:11:05       12          -8         0.6
#> 13:     1  -1.7     7 2026-09-03 12:11:05       13           1        -1.7
#> 14:   -10   0.6  -147 2026-09-03 12:11:05       14         -10         0.6
#> 15:     8   1.7   -45 2026-09-03 12:11:05       15           8         1.7
#> 16:    -8   5.0  -150 2026-09-03 12:11:05       16          -8         5.0
#> 17:     3  -0.6     2 2026-09-03 12:11:06       17           3        -0.6
#> 18:     3   5.0   -56 2026-09-03 12:11:06       18           3         5.0
#> 19:     8   0.6   -36 2026-09-03 12:11:06       19           8         0.6
#> 20:     8  -5.0   -27 2026-09-03 12:11:06       20           8        -5.0
#>        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: 1.111111 -1.666667 <list[2]> 7.432099