makeMultiObjectiveFunction function

Generator for multi-objective target functions.

makeMultiObjectiveFunction(
  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,
  ref.point = NULL
)

Arguments

• name: [character(1)]

  Function name. Used for the title of plots for example.

• id: [character(1) | NULL]

  Optional short function identifier. If provided, this should be a short name without whitespaces and now special characters beside the underscore. Default is NULL, which means no ID at all.

• description: [character(1) | NULL]

  Optional function description.

• fn: [function]

  Objective function.

• has.simple.signature: [logical(1)]

  Set this to TRUE if the objective 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 objective 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 if the corresponding objectives shall be minimized or maximized. Default is the vector with all components set to TRUE.

• vectorized: [logical(1)]

  Can the objective function 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 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.

• ref.point: [numeric]

  Optional reference point in the objective space, e.g., for hypervolume computation.

Returns

[function] Target function with additional stuff attached as attributes.

Examples

fn = makeMultiObjectiveFunction(
  name = "My test function",
  fn = function(x) c(sum(x^2), exp(x)),
  n.objectives = 2L,
  par.set = makeNumericParamSet("x", len = 1L, lower = -5L, upper = 5L)
)
print(fn)