Asynchronous Optimization via Random Search
Source:R/OptimizerAsyncRandomSearch.R
mlr_optimizers_async_random_search.RdOptimizerAsyncRandomSearch class that implements a simple Random Search.
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():
Super classes
bbotk::Optimizer -> bbotk::OptimizerAsync -> OptimizerAsyncRandomSearch
Examples
# example only runs if a Redis server is available
if (mlr3misc::require_namespaces(c("rush", "redux", "mirai"), quietly = TRUE) &&
redux::redis_available()) {
# 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$new(
fun = fun,
domain = domain,
codomain = codomain,
properties = "deterministic"
)
# start workers
rush::rush_plan(worker_type = "mirai")
mirai::daemons(1)
# initialize instance
instance = oi_async(
objective = objective,
terminator = trm("evals", n_evals = 20)
)
# load optimizer
optimizer = opt("async_random_search")
# trigger optimization
optimizer$optimize(instance)
# all evaluated configurations
instance$archive
# best performing configuration
instance$archive$best()
# covert to data.table
as.data.table(instance$archive)
}
#> state x1 x2 y timestamp_xs
#> <char> <num> <num> <num> <POSc>
#> 1: finished 7.0005899 0.64849173 -28.317391 2026-04-10 10:49:13
#> 2: finished -4.7273068 1.55118543 -55.969945 2026-04-10 10:49:13
#> 3: finished -8.3142357 -0.84808989 -101.014174 2026-04-10 10:49:13
#> 4: finished 7.4495551 2.57767647 -50.808126 2026-04-10 10:49:13
#> 5: finished 7.5065666 1.81294693 -43.486734 2026-04-10 10:49:13
#> 6: finished -9.8478251 -2.69770124 -130.462345 2026-04-10 10:49:14
#> 7: finished 0.3870992 2.09953629 -18.606719 2026-04-10 10:49:14
#> 8: finished 9.1887166 1.95032306 -66.183344 2026-04-10 10:49:14
#> 9: finished -6.7320590 -4.50562770 -68.515769 2026-04-10 10:49:14
#> 10: finished 0.9955564 -3.63495767 8.587922 2026-04-10 10:49:14
#> 11: finished 1.9339031 3.60780233 -33.667420 2026-04-10 10:49:14
#> 12: finished -3.6334861 -0.37274046 -28.638658 2026-04-10 10:49:14
#> 13: finished -1.3406191 -2.02690225 -2.106655 2026-04-10 10:49:14
#> 14: finished -5.8805572 -2.81415540 -52.137720 2026-04-10 10:49:14
#> 15: finished 6.5360563 4.77872294 -71.084338 2026-04-10 10:49:14
#> 16: finished -4.5197170 -2.02682984 -33.453771 2026-04-10 10:49:14
#> 17: finished 5.3688411 1.11764111 -18.304059 2026-04-10 10:49:14
#> 18: finished 2.5665792 -1.21875214 6.506144 2026-04-10 10:49:14
#> 19: finished -5.3691761 -0.01495275 -53.215263 2026-04-10 10:49:14
#> 20: finished -6.3526726 1.61537696 -81.068844 2026-04-10 10:49:14
#> state x1 x2 y timestamp_xs
#> <char> <num> <num> <num> <POSc>
#> worker_id timestamp_ys
#> <char> <POSc>
#> 1: narrow_xinjiangovenator 2026-04-10 10:49:13
#> 2: narrow_xinjiangovenator 2026-04-10 10:49:13
#> 3: narrow_xinjiangovenator 2026-04-10 10:49:13
#> 4: narrow_xinjiangovenator 2026-04-10 10:49:13
#> 5: narrow_xinjiangovenator 2026-04-10 10:49:13
#> 6: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 7: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 8: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 9: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 10: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 11: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 12: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 13: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 14: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 15: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 16: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 17: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 18: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 19: narrow_xinjiangovenator 2026-04-10 10:49:14
#> 20: narrow_xinjiangovenator 2026-04-10 10:49:14
#> worker_id timestamp_ys
#> <char> <POSc>
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>
#> 1: e01b8ae4-ec81-42c4-9e95-b3de528d75b7 7.0005899 0.64849173
#> 2: 86f43932-75d1-47ec-a897-b0720e7bafa5 -4.7273068 1.55118543
#> 3: f029dc7a-be34-47ae-bc6c-b40b01c17a45 -8.3142357 -0.84808989
#> 4: 3e776777-dfe5-4081-a9ee-461019447361 7.4495551 2.57767647
#> 5: 8ae44da3-18f3-4ef7-a957-a3022c4b64b6 7.5065666 1.81294693
#> 6: 4ade519d-5e9c-48a4-bfbe-d0f17becaa2d -9.8478251 -2.69770124
#> 7: 6ac952be-7176-4610-8483-1424bc21e01c 0.3870992 2.09953629
#> 8: a0900fb4-ea09-40b1-a02b-8d6b905caabb 9.1887166 1.95032306
#> 9: 9fc47297-0c69-4db6-a48a-69dc18f86170 -6.7320590 -4.50562770
#> 10: 2e84124b-d79c-4dc4-a987-fd4d455ba85d 0.9955564 -3.63495767
#> 11: 4df27278-ba11-45ef-8836-9663ec34a6d2 1.9339031 3.60780233
#> 12: c18d7c53-7e7c-4b64-b3ca-36fc862910df -3.6334861 -0.37274046
#> 13: 6da44322-db87-41e7-a3ff-d1ece6420130 -1.3406191 -2.02690225
#> 14: 92c6f4d0-4594-4474-986f-b075b05d9e69 -5.8805572 -2.81415540
#> 15: ed04c057-2b8e-4548-849e-590703323f6f 6.5360563 4.77872294
#> 16: 946719bd-084c-4d2b-9cdf-3c508b0c02a1 -4.5197170 -2.02682984
#> 17: bef51a87-2cee-43d3-b9bb-70e12b8b306c 5.3688411 1.11764111
#> 18: 7171bebf-2f6c-4101-b5da-64fe8a536c9b 2.5665792 -1.21875214
#> 19: 6f4bba48-da67-4201-b89c-f1a31a69db97 -5.3691761 -0.01495275
#> 20: 6f3be329-8f42-4cec-8194-9f987c14927c -6.3526726 1.61537696
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>