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 2026-04-08 06:03:02
#> 2: finished -4.7273068 1.55118543 -55.969945 2026-04-08 06:03:02
#> 3: finished -8.3142357 -0.84808989 -101.014174 2026-04-08 06:03:02
#> 4: finished 7.4495551 2.57767647 -50.808126 2026-04-08 06:03:02
#> 5: finished 7.5065666 1.81294693 -43.486734 2026-04-08 06:03:02
#> 6: finished -9.8478251 -2.69770124 -130.462345 2026-04-08 06:03:03
#> 7: finished 0.3870992 2.09953629 -18.606719 2026-04-08 06:03:03
#> 8: finished 9.1887166 1.95032306 -66.183344 2026-04-08 06:03:03
#> 9: finished -6.7320590 -4.50562770 -68.515769 2026-04-08 06:03:03
#> 10: finished 0.9955564 -3.63495767 8.587922 2026-04-08 06:03:03
#> 11: finished 1.9339031 3.60780233 -33.667420 2026-04-08 06:03:03
#> 12: finished -3.6334861 -0.37274046 -28.638658 2026-04-08 06:03:03
#> 13: finished -1.3406191 -2.02690225 -2.106655 2026-04-08 06:03:03
#> 14: finished -5.8805572 -2.81415540 -52.137720 2026-04-08 06:03:03
#> 15: finished 6.5360563 4.77872294 -71.084338 2026-04-08 06:03:03
#> 16: finished -4.5197170 -2.02682984 -33.453771 2026-04-08 06:03:03
#> 17: finished 5.3688411 1.11764111 -18.304059 2026-04-08 06:03:03
#> 18: finished 2.5665792 -1.21875214 6.506144 2026-04-08 06:03:03
#> 19: finished -5.3691761 -0.01495275 -53.215263 2026-04-08 06:03:03
#> 20: finished -6.3526726 1.61537696 -81.068844 2026-04-08 06:03:03
#> state x1 x2 y timestamp_xs
#> <char> <num> <num> <num> <POSc>
#> worker_id timestamp_ys
#> <char> <POSc>
#> 1: narrow_xinjiangovenator 2026-04-08 06:03:02
#> 2: narrow_xinjiangovenator 2026-04-08 06:03:02
#> 3: narrow_xinjiangovenator 2026-04-08 06:03:02
#> 4: narrow_xinjiangovenator 2026-04-08 06:03:02
#> 5: narrow_xinjiangovenator 2026-04-08 06:03:02
#> 6: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 7: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 8: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 9: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 10: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 11: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 12: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 13: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 14: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 15: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 16: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 17: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 18: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 19: narrow_xinjiangovenator 2026-04-08 06:03:03
#> 20: narrow_xinjiangovenator 2026-04-08 06:03:03
#> worker_id timestamp_ys
#> <char> <POSc>
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>
#> 1: 50bb4649-9f0e-4c85-83d3-ebcea9d215d5 7.0005899 0.64849173
#> 2: f460648a-0f65-49f1-98b5-6d7555e11ffc -4.7273068 1.55118543
#> 3: 0465af23-8e12-4d9e-a2e9-008aa8dcc791 -8.3142357 -0.84808989
#> 4: 84dc2f0d-cb57-4a8e-a11e-a43eae3098ac 7.4495551 2.57767647
#> 5: 3e537739-a1b9-43b1-a53c-7e6ac3a8e070 7.5065666 1.81294693
#> 6: 71c81b5d-b366-427b-9a01-3b84b3bbbd45 -9.8478251 -2.69770124
#> 7: 5e549589-acc3-42f6-9bfa-e574a9791ffe 0.3870992 2.09953629
#> 8: 3130bd37-dd4b-4c3b-b58c-151c06b1aa75 9.1887166 1.95032306
#> 9: df54c5d7-c9bb-45a2-9a7e-3a656b0463c2 -6.7320590 -4.50562770
#> 10: 1d3c2f32-4be0-428f-8071-061d9a0101c8 0.9955564 -3.63495767
#> 11: 6fb43b03-92dc-4411-a89f-2e023dfad3a7 1.9339031 3.60780233
#> 12: db7386e2-95db-4543-9a28-521e510bc3ba -3.6334861 -0.37274046
#> 13: 2d336421-4e56-40ce-86d6-af785456d4d5 -1.3406191 -2.02690225
#> 14: e32b49f0-906b-4460-a4da-09e7b6f9d1eb -5.8805572 -2.81415540
#> 15: 0ad82879-6fcf-4866-ae07-892c4d0aef22 6.5360563 4.77872294
#> 16: bef45a0f-b13c-4c1e-aecd-3c6fd1497fdf -4.5197170 -2.02682984
#> 17: 51e9b7e7-b526-48b1-add2-fa30a682e25c 5.3688411 1.11764111
#> 18: acba45e5-fa60-497a-b7e0-344b68456b72 2.5665792 -1.21875214
#> 19: 92bfddff-4d82-4595-b1c5-693e7af44110 -5.3691761 -0.01495275
#> 20: 3c297215-8a43-487a-b6ea-639c0abf5771 -6.3526726 1.61537696
#> keys x_domain_x1 x_domain_x2
#> <char> <num> <num>