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 2026-07-20 08:44:16
#> 2: finished -8.33732121 3.178398 -135.032815 2026-07-20 08:44:16
#> 3: finished -7.78396197 2.105379 -111.790803 2026-07-20 08:44:16
#> 4: finished 5.92632791 -4.231512 -6.932672 2026-07-20 08:44:16
#> 5: finished 0.98995613 2.678818 -23.269168 2026-07-20 08:44:16
#> 6: finished -3.50214144 1.111543 -37.178342 2026-07-20 08:44:16
#> 7: finished 2.77985531 4.933030 -53.541141 2026-07-20 08:44:16
#> 8: finished 9.86020590 0.052479 -61.100465 2026-07-20 08:44:16
#> 9: finished -4.77089430 4.984108 -99.590989 2026-07-20 08:44:16
#> 10: finished 8.62827623 3.264048 -73.172348 2026-07-20 08:44:16
#> 11: finished 5.20608164 -2.035465 -1.209287 2026-07-20 08:44:17
#> 12: finished -0.97588025 1.013553 -14.964468 2026-07-20 08:44:17
#> 13: finished -9.42569878 0.916582 -135.886207 2026-07-20 08:44:17
#> 14: finished 0.07085662 3.202987 -32.198639 2026-07-20 08:44:17
#> 15: finished -6.61173459 1.705343 -86.302224 2026-07-20 08:44:17
#> 16: finished 8.34573561 3.714461 -75.352353 2026-07-20 08:44:17
#> 17: finished -8.96971468 -3.263809 -110.404235 2026-07-20 08:44:17
#> 18: finished 1.89838435 2.698046 -22.478057 2026-07-20 08:44:17
#> 19: finished -2.71425870 1.813992 -35.398750 2026-07-20 08:44:17
#> 20: finished 1.30114551 -4.177370 8.125403 2026-07-20 08:44:17
#> state x1 x2 y timestamp_xs
#> <char> <num> <num> <num> <POSc>
#> worker_id timestamp_ys
#> <char> <POSc>
#> 1: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 2: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 3: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 4: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 5: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 6: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 7: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 8: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 9: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 10: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:16
#> 11: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> 12: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> 13: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> 14: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> 15: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> 16: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> 17: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> 18: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> 19: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> 20: narrow_xinjiangovenator_196879f8 2026-07-20 08:44:17
#> worker_id timestamp_ys
#> <char> <POSc>
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>
#> 1: 671a1ec7-9ac5-4ebb-b541-8e6ffd39c97f -2.11085866 3.149893
#> 2: 62be4951-1b9e-42d0-a0a8-a6de58dc0dcf -8.33732121 3.178398
#> 3: e35c8ff1-4eee-4635-8c29-ddfb415fad5b -7.78396197 2.105379
#> 4: 4281eb70-b718-451f-96d8-a32c5bf584f1 5.92632791 -4.231512
#> 5: 3aeee405-4478-4ac8-b8ce-78b7dea400d5 0.98995613 2.678818
#> 6: 3e559127-14be-4eb8-8be4-3dedf785c8cd -3.50214144 1.111543
#> 7: 56d9eda1-5848-4751-8a75-c58cc535b746 2.77985531 4.933030
#> 8: 1d9d03c2-29d2-435f-b5ab-43f6d8d2e13a 9.86020590 0.052479
#> 9: e7cab063-9ff8-497c-b7bb-7217afe43693 -4.77089430 4.984108
#> 10: f4e17dd1-f07d-4972-b74f-c43ae858e4e2 8.62827623 3.264048
#> 11: 1d2e84ef-fbb0-424a-8538-179d7793ac36 5.20608164 -2.035465
#> 12: f88e1745-763b-464e-abc7-b966899c64ca -0.97588025 1.013553
#> 13: 55e45eb6-2bdc-46c0-b540-a5cca37dd5c7 -9.42569878 0.916582
#> 14: 160293a3-be2b-49b6-bc9e-2d37c14d2677 0.07085662 3.202987
#> 15: 148b1f29-066d-4531-83b8-4dc00baf6f28 -6.61173459 1.705343
#> 16: 1f3edfd9-48f0-4e6c-bfcd-2e3c36389fa2 8.34573561 3.714461
#> 17: b26c0364-e295-4702-891b-dbecfb6fe9f7 -8.96971468 -3.263809
#> 18: 891a4a69-23b3-4fc8-907e-8531d425fa02 1.89838435 2.698046
#> 19: 45bab9d0-4e56-4f22-84ee-fc8c80b600c5 -2.71425870 1.813992
#> 20: dda9e700-eeed-47ad-999c-cd58734f28ca 1.30114551 -4.177370
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>