Describes a black-box objective function that maps an arbitrary domain to a numerical codomain.

`Objective`

objects can have the following properties: `"noisy"`

,
`"deterministic"`

, `"single-crit"`

and `"multi-crit"`

.

`id`

(

`character(1)`

)).`properties`

(

`character()`

).`domain`

(paradox::ParamSet)

Specifies domain of function, hence its input parameters, their types and ranges.`codomain`

(paradox::ParamSet)

Specifies codomain of function, hence its feasible values.`constants`

(paradox::ParamSet).

Changeable constants or parameters that are not subject to tuning can be stored and accessed here.`check_values`

`xdim`

(

`integer(1)`

)

Dimension of domain.`ydim`

(

`integer(1)`

)

Dimension of codomain.

`new()`

Creates a new instance of this R6 class.

Objective$new( id = "f", properties = character(), domain, codomain = ParamSet$new(list(ParamDbl$new("y", tags = "minimize"))), constants = ParamSet$new(), check_values = TRUE )

`id`

(

`character(1)`

).`properties`

(

`character()`

).`domain`

(paradox::ParamSet)

Specifies domain of function. The paradox::ParamSet should describe all possible input parameters of the objective function. This includes their`id`

, their types and the possible range.`codomain`

(paradox::ParamSet)

Specifies codomain of function. Most importantly the tags of each output "Parameter" define whether it should be minimized or maximized. The default is to minimize each component.`constants`

(paradox::ParamSet)

Changeable constants or parameters that are not subject to tuning can be stored and accessed here.`check_values`

(

`logical(1)`

)

Should points before the evaluation and the results be checked for validity?

`format()`

Helper for print outputs.

Objective$format()

`print()`

Print method.

Objective$print()

`eval()`

Evaluates a single input value on the objective function. If
`check_values = TRUE`

, the validity of the point as well as the validity
of the result is checked.

Objective$eval(xs)

`xs`

(

`list()`

)

A list that contains a single x value, e.g.`list(x1 = 1, x2 = 2)`

.

`list()`

that contains the result of the evaluation, e.g. `list(y = 1)`

.
The list can also contain additional *named* entries that will be stored in the
archive if called through the OptimInstance.
These extra entries are referred to as *extras*.

`eval_many()`

Evaluates multiple input values on the objective function. If
`check_values = TRUE`

, the validity of the points as well as the validity
of the results are checked. *bbotk* does not take care of
parallelization. If the function should make use of parallel computing,
it has to be implemented by deriving from this class and overwriting this
function.

Objective$eval_many(xss)

`xss`

(

`list()`

)

A list of lists that contains multiple x values, e.g.`list(list(x1 = 1, x2 = 2), list(x1 = 3, x2 = 4))`

.

data.table::data.table()] that contains one y-column for
single-criteria functions and multiple y-columns for multi-criteria functions,
e.g. `data.table(y = 1:2)`

or `data.table(y1 = 1:2, y2 = 3:4)`

.
It may also contain additional columns that will be stored in the archive if
called through the OptimInstance.
These extra columns are referred to as *extras*.

`eval_dt()`

Evaluates multiple input values on the objective function

Objective$eval_dt(xdt)

`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.

data.table::data.table()] that contains one y-column for
single-criteria functions and multiple y-columns for multi-criteria
functions, e.g. `data.table(y = 1:2)`

or `data.table(y1 = 1:2, y2 = 3:4)`

.

`clone()`

The objects of this class are cloneable with this method.

Objective$clone(deep = FALSE)

`deep`

Whether to make a deep clone.