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 2026-09-03 12:10:58
#> 2: finished -8.33732121 3.178398 -135.032815 2026-09-03 12:10:58
#> 3: finished -7.78396197 2.105379 -111.790803 2026-09-03 12:10:58
#> 4: finished 5.92632791 -4.231512 -6.932672 2026-09-03 12:10:58
#> 5: finished 0.98995613 2.678818 -23.269168 2026-09-03 12:10:58
#> 6: finished -3.50214144 1.111543 -37.178342 2026-09-03 12:10:58
#> 7: finished 2.77985531 4.933030 -53.541141 2026-09-03 12:10:58
#> 8: finished 9.86020590 0.052479 -61.100465 2026-09-03 12:10:59
#> 9: finished -4.77089430 4.984108 -99.590989 2026-09-03 12:10:59
#> 10: finished 8.62827623 3.264048 -73.172348 2026-09-03 12:10:59
#> 11: finished 5.20608164 -2.035465 -1.209287 2026-09-03 12:10:59
#> 12: finished -0.97588025 1.013553 -14.964468 2026-09-03 12:10:59
#> 13: finished -9.42569878 0.916582 -135.886207 2026-09-03 12:10:59
#> 14: finished 0.07085662 3.202987 -32.198639 2026-09-03 12:10:59
#> 15: finished -6.61173459 1.705343 -86.302224 2026-09-03 12:10:59
#> 16: finished 8.34573561 3.714461 -75.352353 2026-09-03 12:10:59
#> 17: finished -8.96971468 -3.263809 -110.404235 2026-09-03 12:10:59
#> 18: finished 1.89838435 2.698046 -22.478057 2026-09-03 12:10:59
#> 19: finished -2.71425870 1.813992 -35.398750 2026-09-03 12:10:59
#> 20: finished 1.30114551 -4.177370 8.125403 2026-09-03 12:10:59
#> state x1 x2 y timestamp_xs
#> <char> <num> <num> <num> <POSc>
#> worker_id timestamp_ys
#> <char> <POSc>
#> 1: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:58
#> 2: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:58
#> 3: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:58
#> 4: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:58
#> 5: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:58
#> 6: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:58
#> 7: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:58
#> 8: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 9: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 10: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 11: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 12: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 13: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 14: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 15: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 16: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 17: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 18: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 19: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> 20: narrow_xinjiangovenator_a08b59e3 2026-09-03 12:10:59
#> worker_id timestamp_ys
#> <char> <POSc>
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>
#> 1: 50b80314-af06-4391-8505-3a35832aefc1 -2.11085866 3.149893
#> 2: 82e17126-e9f0-49a3-b63f-a9b24290cd61 -8.33732121 3.178398
#> 3: 4a1064eb-f49b-4de4-bcdf-16c806e38e91 -7.78396197 2.105379
#> 4: 8f46609b-ac32-40c9-a9c2-5267305f8e0c 5.92632791 -4.231512
#> 5: 52debbf7-4022-4761-82be-21a03168a06c 0.98995613 2.678818
#> 6: 56c568d9-7f09-40fb-b18f-269896bb952f -3.50214144 1.111543
#> 7: 274c2785-5dd3-43d7-b186-6fb77ee7ddf3 2.77985531 4.933030
#> 8: 79e9dc4d-7cbd-45c4-9982-e4eced5ffd14 9.86020590 0.052479
#> 9: a80e8e53-0e05-43f6-95ff-beb951070560 -4.77089430 4.984108
#> 10: fff53769-177b-4fad-a651-1cfc2f631274 8.62827623 3.264048
#> 11: f8fff645-15e5-4cfe-9dab-c60cffa87864 5.20608164 -2.035465
#> 12: 0117969d-87c3-4ec8-b4ad-f01c5057559d -0.97588025 1.013553
#> 13: 4e9e508c-e605-429d-b9ff-1cb7cec5b22b -9.42569878 0.916582
#> 14: 131d8c83-2e42-4592-82b6-d22ebcd2423a 0.07085662 3.202987
#> 15: c831f8f5-bb52-4285-b9ac-dd6c36a7dd12 -6.61173459 1.705343
#> 16: c067d36c-c385-4cbe-a3f7-f76264e56f40 8.34573561 3.714461
#> 17: d13b37e6-5452-44f8-a00e-e35dd800c3ce -8.96971468 -3.263809
#> 18: d8bb01e3-3793-4d27-abc8-3477c49fac48 1.89838435 2.698046
#> 19: 5f80f577-ecff-4a60-8e9d-2f4433665faa -2.71425870 1.813992
#> 20: 8de9d953-3516-43d8-b007-d4341c1ab866 1.30114551 -4.177370
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>