mlr_optimizers_async_design_points: Asynchronous Optimization via Design Points

In bbotk: Black-Box Optimization Toolkit

Asynchronous Optimization via Design Points

Description

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

• OptimizerAsyncDesignPoints$new()

• OptimizerAsyncDesignPoints$optimize()

• OptimizerAsyncDesignPoints$clone()

Inherited methods

• bbotk::Optimizer$format()
• bbotk::Optimizer$help()
• bbotk::Optimizer$print()

---

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)
}

bbotk documentation built on Nov. 5, 2025, 6:23 p.m.