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Single Criterion Optimization Instance for Batch Optimization

Source: R/OptimInstanceBatchSingleCrit.R
OptimInstanceBatchSingleCrit.Rd

The OptimInstanceBatchSingleCrit specifies an optimization problem for an OptimizerBatch. The function oi() creates an OptimInstanceBatchSingleCrit.

Super classes

bbotk::OptimInstance -> bbotk::OptimInstanceBatch -> OptimInstanceBatchSingleCrit

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

OptimInstanceBatchSingleCrit$new(
  objective,
  search_space = NULL,
  terminator,
  check_values = TRUE,
  callbacks = NULL,
  archive = NULL
)

Arguments

objective

(Objective)
Objective function.

search_space

(paradox::ParamSet)
Specifies the search space for the Optimizer. The paradox::ParamSet describes either a subset of the domain of the Objective or it describes a set of parameters together with a trafo function that transforms values from the search space to values of the domain. Depending on the context, this value defaults to the domain of the objective.

terminator

Terminator
Termination criterion.

check_values

(logical(1))
Should points before the evaluation and the results be checked for validity?

callbacks

(list of mlr3misc::Callback)
List of callbacks.

archive

(Archive).

Method assign_result()

The Optimizer object writes the best found point and estimated performance value here. For internal use.

Usage

OptimInstanceBatchSingleCrit$assign_result(xdt, y, extra = NULL, ...)

Arguments

xdt

(data.table::data.table())
Set of untransformed points / points from the search space. One point per row, e.g. data.table(x1 = c(1, 3), x2 = c(2, 4)). Column names have to match ids of the search_space. However, xdt can contain additional columns.

y

(numeric(1))
Optimal outcome.

extra

(data.table::data.table())
Additional information.

...

(any)
ignored.

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimInstanceBatchSingleCrit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# 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"
)

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