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 2026-04-22 10:04:36
#> 2: finished -4.7273068 1.55118543 -55.969945 2026-04-22 10:04:36
#> 3: finished -8.3142357 -0.84808989 -101.014174 2026-04-22 10:04:36
#> 4: finished 7.4495551 2.57767647 -50.808126 2026-04-22 10:04:36
#> 5: finished 7.5065666 1.81294693 -43.486734 2026-04-22 10:04:36
#> 6: finished -9.8478251 -2.69770124 -130.462345 2026-04-22 10:04:36
#> 7: finished 0.3870992 2.09953629 -18.606719 2026-04-22 10:04:36
#> 8: finished 9.1887166 1.95032306 -66.183344 2026-04-22 10:04:36
#> 9: finished -6.7320590 -4.50562770 -68.515769 2026-04-22 10:04:36
#> 10: finished 0.9955564 -3.63495767 8.587922 2026-04-22 10:04:36
#> 11: finished 1.9339031 3.60780233 -33.667420 2026-04-22 10:04:36
#> 12: finished -3.6334861 -0.37274046 -28.638658 2026-04-22 10:04:36
#> 13: finished -1.3406191 -2.02690225 -2.106655 2026-04-22 10:04:36
#> 14: finished -5.8805572 -2.81415540 -52.137720 2026-04-22 10:04:36
#> 15: finished 6.5360563 4.77872294 -71.084338 2026-04-22 10:04:36
#> 16: finished -4.5197170 -2.02682984 -33.453771 2026-04-22 10:04:36
#> 17: finished 5.3688411 1.11764111 -18.304059 2026-04-22 10:04:36
#> 18: finished 2.5665792 -1.21875214 6.506144 2026-04-22 10:04:36
#> 19: finished -5.3691761 -0.01495275 -53.215263 2026-04-22 10:04:36
#> 20: finished -6.3526726 1.61537696 -81.068844 2026-04-22 10:04:36
#> state x1 x2 y timestamp_xs
#> <char> <num> <num> <num> <POSc>
#> worker_id timestamp_ys
#> <char> <POSc>
#> 1: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 2: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 3: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 4: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 5: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 6: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 7: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 8: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 9: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 10: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 11: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 12: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 13: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 14: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 15: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 16: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 17: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 18: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 19: narrow_xinjiangovenator 2026-04-22 10:04:36
#> 20: narrow_xinjiangovenator 2026-04-22 10:04:36
#> worker_id timestamp_ys
#> <char> <POSc>
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>
#> 1: e54ca42d-557c-43e6-910b-24af880f72af 7.0005899 0.64849173
#> 2: 2d80ad50-bde7-4d54-b8dd-ff420948a87f -4.7273068 1.55118543
#> 3: f81e6a54-15d0-4fdf-b490-1b44e5f0e1a8 -8.3142357 -0.84808989
#> 4: f2a67787-32df-41a5-9916-20a393e6bb6e 7.4495551 2.57767647
#> 5: 831f9840-383c-46b9-b7c4-959ada83c8ec 7.5065666 1.81294693
#> 6: c89d3466-08f1-4cbe-9a2b-2cffb2abc150 -9.8478251 -2.69770124
#> 7: 158ecc4b-cb82-4bc9-9008-c9b21a9eb5c4 0.3870992 2.09953629
#> 8: 902797e1-d24b-4a3a-9539-ca42cf8971b4 9.1887166 1.95032306
#> 9: f13264bf-9880-4e1c-9025-25274287ac59 -6.7320590 -4.50562770
#> 10: aa08a15c-f6b2-4be0-a7b2-649fcb56cb97 0.9955564 -3.63495767
#> 11: 70d8fa42-2a04-4b9f-a33e-81f81af83e29 1.9339031 3.60780233
#> 12: dc24b0a3-5640-4f81-9b86-5cc9e625022d -3.6334861 -0.37274046
#> 13: 7d9ccad6-acd5-4697-aa4b-dbc3f4eca130 -1.3406191 -2.02690225
#> 14: a665f36e-47d4-487b-8f8f-df74fd5c5d20 -5.8805572 -2.81415540
#> 15: c5fff5cd-4e71-40b7-96eb-b0f4932bc22b 6.5360563 4.77872294
#> 16: e22cb770-2260-4b25-9b06-7e3e1e645d26 -4.5197170 -2.02682984
#> 17: 61eab7d1-d620-4dab-addd-ad2b1f04f07d 5.3688411 1.11764111
#> 18: 8badcc56-42b6-4f82-9f1d-0b6834b5cccc 2.5665792 -1.21875214
#> 19: b1dda4e2-1fbf-4a90-a5a2-64b358e44d59 -5.3691761 -0.01495275
#> 20: 86d50818-a003-4d7f-b571-85e4838ab597 -6.3526726 1.61537696
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>