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():
Parameters
resolutioninteger(1)
Resolution of the grid, seeparadox::generate_design_grid().param_resolutionsnamed
integer()
Resolution per parameter, named by parameter ID, seeparadox::generate_design_grid().batch_sizeinteger(1)
Maximum number of points to try in a batch.
Progress Bars
$optimize() supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress() to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress").
Super classes
bbotk::Optimizer -> bbotk::OptimizerBatch -> OptimizerBatchGridSearch
Examples
# define the objective function
fun = function(xs) {
list(y = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
}
# set domain
domain = ps(
x1 = p_dbl(-10, 10),
x2 = p_dbl(-5, 5)
)
# set codomain
codomain = ps(
y = p_dbl(tags = "maximize")
)
# create objective
objective = ObjectiveRFun$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$optimize(instance)
#> x1 x2 x_domain y
#> <num> <num> <list> <num>
#> 1: 3.333333 -2.777778 <list[2]> 8.17284
# all evaluated configurations
instance$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: -1 -5.0 -3.7 2025-10-24 13:34:31 1 -1 -5.0
#> 2: 10 -3.9 -54.8 2025-10-24 13:34:31 2 10 -3.9
#> 3: -6 1.7 -68.9 2025-10-24 13:34:31 3 -6 1.7
#> 4: -8 -5.0 -89.6 2025-10-24 13:34:31 4 -8 -5.0
#> 5: -10 -0.6 -140.0 2025-10-24 13:34:31 5 -10 -0.6
#> 6: -6 -0.6 -53.1 2025-10-24 13:34:31 6 -6 -0.6
#> 7: 3 -1.7 6.4 2025-10-24 13:34:32 7 3 -1.7
#> 8: -6 0.6 -59.7 2025-10-24 13:34:32 8 -6 0.6
#> 9: -1 -3.9 -0.5 2025-10-24 13:34:32 9 -1 -3.9
#> 10: -6 -1.7 -48.9 2025-10-24 13:34:32 10 -6 -1.7
#> 11: -8 -3.9 -86.4 2025-10-24 13:34:32 11 -8 -3.9
#> 12: 3 -2.8 8.2 2025-10-24 13:34:32 12 3 -2.8
#> 13: -10 0.6 -146.6 2025-10-24 13:34:32 13 -10 0.6
#> 14: 3 -5.0 4.2 2025-10-24 13:34:32 14 3 -5.0
#> 15: 8 -1.7 -25.2 2025-10-24 13:34:32 15 8 -1.7
#> 16: 1 -5.0 5.2 2025-10-24 13:34:32 16 1 -5.0
#> 17: -3 -2.8 -18.5 2025-10-24 13:34:32 17 -3 -2.8
#> 18: 6 -3.9 -3.4 2025-10-24 13:34:32 18 6 -3.9
#> 19: -8 3.9 -133.1 2025-10-24 13:34:32 19 -8 3.9
#> 20: -8 2.8 -119.0 2025-10-24 13:34:32 20 -8 2.8
#> 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: 3.333333 -2.777778 <list[2]> 8.17284