makeObjectiveFunction: Internal generator for function of smoof type.
In jakobbossek/smoof: Single and Multi-Objective Optimization Test Functions

makeObjectiveFunction

R Documentation

Internal generator for function of smoof type.

Description

Internal generator for function of smoof type.

Usage

makeObjectiveFunction(
  name = NULL,
  id = NULL,
  description = NULL,
  fn,
  has.simple.signature = TRUE,
  par.set,
  n.objectives = NULL,
  noisy = FALSE,
  fn.mean = NULL,
  minimize = NULL,
  vectorized = FALSE,
  constraint.fn = NULL
)

Arguments

`name`	[`character(1)`] Optional function name used e.g. in plots.
`id`	[`character(1)`] Optional identifier for the function
`description`	[`character(1)` \| `NULL`] Optional function description.
`fn`	[`function`] Target function.
`has.simple.signature`	[`logical(1)`] Set this to `TRUE` if the target function expects a vector as input and `FALSE` if it expects a named list of values. The latter is needed if the function depends on mixed parameters. Default is `TRUE`.
`par.set`	[`ParamSet`] Parameter set describing different aspects of the target function parameters, i. e., names, lower and/or upper bounds, types and so on. See `makeParamSet` for further information.
`n.objectives`	[`integer(1)`] Number of objectives of the multi-objective function.
`noisy`	[`logical(1)`] Is the function noisy? Defaults to `FALSE`.
`fn.mean`	[`function`] Optional true mean function in case of a noisy objective function. This functions should have the same mean as `fn`.
`minimize`	[`logical`] Logical vector of length `n.objectives` indicating which objectives shall be minimized/maximized. The default is `TRUE` `n.objectives` times.
`vectorized`	[`logical(1)`] Can the handle “vector” input, i. e., does it accept matrix of parameters? Default is `FALSE`.
`constraint.fn`	[`function \| NULL`] Function which returns a logical vector indicating which indicates whether certain conditions are met or not. Default is `NULL`, which means, that there are no constraints (beside possible) box constraints defined via the `par.set` argument.