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Asynchronous Optimization via Design Points

Source: R/OptimizerAsyncDesignPoints.R
mlr_optimizers_async_design_points.Rd

OptimizerAsyncDesignPoints class that implements optimization w.r.t. fixed design points. We simply search over a set of points fully specified by the ser.

Dictionary

This Optimizer can be instantiated via the dictionary mlr_optimizers or with the associated sugar function opt():

mlr_optimizers$get("async_design_points")
opt("async_design_points")

Parameters

design

data.table::data.table
Design points to try in search, one per row.

Super classes

bbotk::Optimizer -> bbotk::OptimizerAsync -> OptimizerAsyncDesignPoints

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerAsyncDesignPoints$new()

Method optimize()

Starts the asynchronous optimization.

Usage

OptimizerAsyncDesignPoints$optimize(inst)

Arguments

inst

(OptimInstance).

Returns

data.table::data.table.

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerAsyncDesignPoints$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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 = "remote")
mirai::daemons(1)

# initialize instance
instance = oi_async(
  objective = objective,
  terminator = trm("evals", n_evals = 20)
)

# load optimizer
design = data.table::data.table(x1 = c(0, 1), x2 = c(0, 1))
optimizer = opt("async_design_points", design = design)

# 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   pid
#>      <char> <num> <num> <num>              <POSc> <int>
#> 1: finished     0     0    -3 2025-10-24 13:34:20  9582
#> 2: finished     1     1    -7 2025-10-24 13:34:20  9582
#>                     worker_id        timestamp_ys
#>                        <char>              <POSc>
#> 1: emotionless_astrangiacoral 2025-10-24 13:34:21
#> 2: emotionless_astrangiacoral 2025-10-24 13:34:21
#>                                    keys x_domain_x1 x_domain_x2
#>                                  <char>       <num>       <num>
#> 1: 24f8f240-e5c7-43f5-a674-552dacc980ec           0           0
#> 2: d92b6b2b-f1a3-4cc5-a686-4f7da59ad959           1           1