Multi Criteria Optimization Instance for Batch Optimization

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

Super classes

result_x_domain: (list())
(transformed) x part of the result in the domain space of the objective.
result_y: (numeric(1))
Optimal outcome.

Inherited methods

Creates a new instance of this R6 class.

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

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).

The Optimizer object writes the best found points and estimated performance values here (probably the Pareto set / front). For internal use.

OptimInstanceBatchMultiCrit$assign_result(xdt, ydt, extra = NULL, ...)

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.
ydt: (data.table::data.table())
Optimal outcome.
extra: (data.table::data.table())
Additional information.
...: (any)
ignored.

The objects of this class are cloneable with this method.

OptimInstanceBatchMultiCrit$clone(deep = FALSE)