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
Optimizer -> OptimizerAsync -> OptimizerAsyncRandomSearch
Methods
Inherited methods
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 -2.11085866 3.149893 -44.720348 2026-09-18 09:12:16
#> 2: finished -8.33732121 3.178398 -135.032815 2026-09-18 09:12:16
#> 3: finished -7.78396197 2.105379 -111.790803 2026-09-18 09:12:16
#> 4: finished 5.92632791 -4.231512 -6.932672 2026-09-18 09:12:16
#> 5: finished 0.98995613 2.678818 -23.269168 2026-09-18 09:12:16
#> 6: finished -3.50214144 1.111543 -37.178342 2026-09-18 09:12:16
#> 7: finished 2.77985531 4.933030 -53.541141 2026-09-18 09:12:16
#> 8: finished 9.86020590 0.052479 -61.100465 2026-09-18 09:12:16
#> 9: finished -4.77089430 4.984108 -99.590989 2026-09-18 09:12:16
#> 10: finished 8.62827623 3.264048 -73.172348 2026-09-18 09:12:16
#> 11: finished 5.20608164 -2.035465 -1.209287 2026-09-18 09:12:16
#> 12: finished -0.97588025 1.013553 -14.964468 2026-09-18 09:12:16
#> 13: finished -9.42569878 0.916582 -135.886207 2026-09-18 09:12:16
#> 14: finished 0.07085662 3.202987 -32.198639 2026-09-18 09:12:16
#> 15: finished -6.61173459 1.705343 -86.302224 2026-09-18 09:12:16
#> 16: finished 8.34573561 3.714461 -75.352353 2026-09-18 09:12:16
#> 17: finished -8.96971468 -3.263809 -110.404235 2026-09-18 09:12:16
#> 18: finished 1.89838435 2.698046 -22.478057 2026-09-18 09:12:16
#> 19: finished -2.71425870 1.813992 -35.398750 2026-09-18 09:12:16
#> 20: finished 1.30114551 -4.177370 8.125403 2026-09-18 09:12:17
#> state x1 x2 y timestamp_xs
#> <char> <num> <num> <num> <POSc>
#> worker_id timestamp_ys
#> <char> <POSc>
#> 1: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 2: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 3: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 4: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 5: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 6: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 7: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 8: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 9: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 10: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 11: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 12: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 13: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 14: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 15: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 16: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 17: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 18: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 19: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:16
#> 20: narrow_xinjiangovenator_67b61609 2026-09-18 09:12:17
#> worker_id timestamp_ys
#> <char> <POSc>
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>
#> 1: 18d30539-30bb-4a75-b0f6-0377550e5f39 -2.11085866 3.149893
#> 2: 54701205-3bf2-49ca-ab74-5d640f85f662 -8.33732121 3.178398
#> 3: 8d67fbf4-a5bc-48f9-b444-a19de3b5128a -7.78396197 2.105379
#> 4: e60bc8eb-131b-44f8-9d55-438bdf31deae 5.92632791 -4.231512
#> 5: cbb9193b-e973-4b75-b275-3b5cc6f4abde 0.98995613 2.678818
#> 6: e1182e82-a8bb-4236-a6d9-27e1262c5e3f -3.50214144 1.111543
#> 7: b0fd33ac-1414-42ec-abc8-de4435622822 2.77985531 4.933030
#> 8: 09c17159-ae13-4c85-a1f5-e36ee579aa14 9.86020590 0.052479
#> 9: 378cc5e0-d272-44de-8213-d3732a345628 -4.77089430 4.984108
#> 10: ee3baff2-a00a-4010-9e49-9d0667549554 8.62827623 3.264048
#> 11: 595f7640-f8b6-4031-a6a4-cd0639de30dd 5.20608164 -2.035465
#> 12: a6c8839a-d2f3-4878-8205-85c185b40abb -0.97588025 1.013553
#> 13: 421a783d-72f0-49a9-ae74-fa97e6ef22dc -9.42569878 0.916582
#> 14: 0da413bd-719b-44d8-8a50-98c71ae85082 0.07085662 3.202987
#> 15: b5ad86ad-50b6-452e-8a90-78eed0aa686f -6.61173459 1.705343
#> 16: 0f8248c0-7f9f-4694-b48e-c69d2d418e90 8.34573561 3.714461
#> 17: ff84fe82-c68a-4846-aaf5-447708b88f0d -8.96971468 -3.263809
#> 18: 6efdc0cb-16ed-4a52-9d4a-1cb4290e4ed3 1.89838435 2.698046
#> 19: 77f8d4ba-c8d6-436c-8a43-b58f8df3e5c7 -2.71425870 1.813992
#> 20: a7d7b75d-6926-4768-aa82-041e8faa47c7 1.30114551 -4.177370
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>