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 2026-03-20 06:19:27
#> 2: finished -4.7273068 1.55118543 -55.969945 2026-03-20 06:19:27
#> 3: finished -8.3142357 -0.84808989 -101.014174 2026-03-20 06:19:27
#> 4: finished 7.4495551 2.57767647 -50.808126 2026-03-20 06:19:27
#> 5: finished 7.5065666 1.81294693 -43.486734 2026-03-20 06:19:27
#> 6: finished -9.8478251 -2.69770124 -130.462345 2026-03-20 06:19:27
#> 7: finished 0.3870992 2.09953629 -18.606719 2026-03-20 06:19:27
#> 8: finished 9.1887166 1.95032306 -66.183344 2026-03-20 06:19:27
#> 9: finished -6.7320590 -4.50562770 -68.515769 2026-03-20 06:19:27
#> 10: finished 0.9955564 -3.63495767 8.587922 2026-03-20 06:19:27
#> 11: finished 1.9339031 3.60780233 -33.667420 2026-03-20 06:19:27
#> 12: finished -3.6334861 -0.37274046 -28.638658 2026-03-20 06:19:27
#> 13: finished -1.3406191 -2.02690225 -2.106655 2026-03-20 06:19:27
#> 14: finished -5.8805572 -2.81415540 -52.137720 2026-03-20 06:19:27
#> 15: finished 6.5360563 4.77872294 -71.084338 2026-03-20 06:19:27
#> 16: finished -4.5197170 -2.02682984 -33.453771 2026-03-20 06:19:27
#> 17: finished 5.3688411 1.11764111 -18.304059 2026-03-20 06:19:27
#> 18: finished 2.5665792 -1.21875214 6.506144 2026-03-20 06:19:27
#> 19: finished -5.3691761 -0.01495275 -53.215263 2026-03-20 06:19:27
#> 20: finished -6.3526726 1.61537696 -81.068844 2026-03-20 06:19:27
#> state x1 x2 y timestamp_xs
#> <char> <num> <num> <num> <POSc>
#> worker_id timestamp_ys
#> <char> <POSc>
#> 1: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 2: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 3: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 4: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 5: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 6: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 7: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 8: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 9: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 10: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 11: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 12: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 13: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 14: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 15: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 16: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 17: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 18: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 19: narrow_xinjiangovenator 2026-03-20 06:19:27
#> 20: narrow_xinjiangovenator 2026-03-20 06:19:27
#> worker_id timestamp_ys
#> <char> <POSc>
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>
#> 1: 3048202a-e429-4c3a-b764-9748ca8434ca 7.0005899 0.64849173
#> 2: dd44418d-3f8e-4a52-9e59-ff18d42777a1 -4.7273068 1.55118543
#> 3: 3edda94f-c91e-4e70-9586-23cd37b9678f -8.3142357 -0.84808989
#> 4: 676e7f79-00fe-4b69-8dd2-6f1f0f5c0bda 7.4495551 2.57767647
#> 5: df2e9f5d-4f1f-414c-a52d-f63546f9940a 7.5065666 1.81294693
#> 6: a88d28c5-ef06-444e-8fe1-d8cf6eecb298 -9.8478251 -2.69770124
#> 7: ce0f4e2a-f5c7-4feb-ad4c-e42f47c06b62 0.3870992 2.09953629
#> 8: 1baf7e61-d787-4ed5-ad3e-7984c926d0a3 9.1887166 1.95032306
#> 9: 27f863e7-576c-470f-8fe9-abff8c24bcba -6.7320590 -4.50562770
#> 10: ef6d85e0-67d7-4d36-a99a-cfc00daac0d5 0.9955564 -3.63495767
#> 11: 157abefe-4ba4-46a3-897b-67b831673a95 1.9339031 3.60780233
#> 12: ec1423ae-f155-468f-accd-d67eeca58838 -3.6334861 -0.37274046
#> 13: 18a7cb0b-c39b-4d88-a940-6217bb2341d7 -1.3406191 -2.02690225
#> 14: 8f50e45b-75d0-48bb-9d3d-ed9c3525983e -5.8805572 -2.81415540
#> 15: ec404f6a-f95b-4a10-91d6-ea44dd43530d 6.5360563 4.77872294
#> 16: 341edea7-f3ab-4857-a044-b830fdfefabc -4.5197170 -2.02682984
#> 17: e6ef4cc3-c3c2-46ac-8478-6d1829efc31d 5.3688411 1.11764111
#> 18: a0f5f365-0d25-44cf-85f8-956e931c1317 2.5665792 -1.21875214
#> 19: 8d307420-40f6-466c-8353-d41ce00702ef -5.3691761 -0.01495275
#> 20: 4cf9a632-bc83-4e53-ba82-af39aff8e5cf -6.3526726 1.61537696
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>