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 2026-07-14 15:10:25
#> 2: finished -8.33732121 3.178398 -135.032815 2026-07-14 15:10:25
#> 3: finished -7.78396197 2.105379 -111.790803 2026-07-14 15:10:25
#> 4: finished 5.92632791 -4.231512 -6.932672 2026-07-14 15:10:25
#> 5: finished 0.98995613 2.678818 -23.269168 2026-07-14 15:10:25
#> 6: finished -3.50214144 1.111543 -37.178342 2026-07-14 15:10:25
#> 7: finished 2.77985531 4.933030 -53.541141 2026-07-14 15:10:25
#> 8: finished 9.86020590 0.052479 -61.100465 2026-07-14 15:10:25
#> 9: finished -4.77089430 4.984108 -99.590989 2026-07-14 15:10:25
#> 10: finished 8.62827623 3.264048 -73.172348 2026-07-14 15:10:25
#> 11: finished 5.20608164 -2.035465 -1.209287 2026-07-14 15:10:25
#> 12: finished -0.97588025 1.013553 -14.964468 2026-07-14 15:10:25
#> 13: finished -9.42569878 0.916582 -135.886207 2026-07-14 15:10:25
#> 14: finished 0.07085662 3.202987 -32.198639 2026-07-14 15:10:25
#> 15: finished -6.61173459 1.705343 -86.302224 2026-07-14 15:10:25
#> 16: finished 8.34573561 3.714461 -75.352353 2026-07-14 15:10:25
#> 17: finished -8.96971468 -3.263809 -110.404235 2026-07-14 15:10:25
#> 18: finished 1.89838435 2.698046 -22.478057 2026-07-14 15:10:25
#> 19: finished -2.71425870 1.813992 -35.398750 2026-07-14 15:10:25
#> 20: finished 1.30114551 -4.177370 8.125403 2026-07-14 15:10:25
#> state x1 x2 y timestamp_xs
#> <char> <num> <num> <num> <POSc>
#> worker_id timestamp_ys
#> <char> <POSc>
#> 1: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 2: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 3: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 4: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 5: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 6: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 7: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 8: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 9: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 10: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 11: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 12: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 13: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 14: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 15: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 16: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 17: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 18: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 19: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> 20: narrow_xinjiangovenator_9b6ac502 2026-07-14 15:10:25
#> worker_id timestamp_ys
#> <char> <POSc>
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>
#> 1: 9e317dad-36e2-4152-9be4-57a67bfb3d29 -2.11085866 3.149893
#> 2: 5fe470fe-94a6-40db-b23c-59210b73d47e -8.33732121 3.178398
#> 3: d477835a-f66b-4153-880a-fedfc7800f64 -7.78396197 2.105379
#> 4: 3cd51f86-ce00-45f9-9266-57d3e18bf881 5.92632791 -4.231512
#> 5: c91d234e-ffe9-43fc-9a6d-4199826b786d 0.98995613 2.678818
#> 6: bbfef1bb-42fc-4e36-8795-0bf6c4276592 -3.50214144 1.111543
#> 7: 71aba732-ebb4-48d4-9332-d4fa7815efc6 2.77985531 4.933030
#> 8: deafe7f7-ae09-4d6c-8342-ed4f88edead1 9.86020590 0.052479
#> 9: 1c6bbcca-0d68-4574-bc59-2a7772af4f58 -4.77089430 4.984108
#> 10: 4cd6b791-847d-4199-9293-c5c1a0b60179 8.62827623 3.264048
#> 11: 6a78d884-f443-4649-a119-603d69f89346 5.20608164 -2.035465
#> 12: 0621301d-616d-4a88-afdf-539a9481ed59 -0.97588025 1.013553
#> 13: d746c849-99db-4a55-b93f-fba2f566183d -9.42569878 0.916582
#> 14: 02f9fcee-4a43-4add-b486-ae936b9b24fc 0.07085662 3.202987
#> 15: 63dbecfb-0344-4ad3-bf7d-4e068f24ba6f -6.61173459 1.705343
#> 16: d3eb4620-4cfc-4d3c-992e-de84f28405a7 8.34573561 3.714461
#> 17: 90b46bd7-f0fc-4e79-8a0f-65502ad8590b -8.96971468 -3.263809
#> 18: 81b3d6a8-f4d7-493f-bb75-210688990fbe 1.89838435 2.698046
#> 19: b5c64e85-56c1-4c24-90dc-09b90b352d68 -2.71425870 1.813992
#> 20: 0ccf9d7f-b105-4dea-9572-07c318e64bd3 1.30114551 -4.177370
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>