makeSingleObjectiveFunction: Generator for single-objective target functions.
In smoof: Single and Multi-Objective Optimization Test Functions

Description Usage Arguments Value Examples

View source: R/makeSingleObjectiveFunction.R

Generator for single-objective target functions.

makeSingleObjectiveFunction(name = NULL, id = NULL,
  description = NULL, fn, has.simple.signature = TRUE,
  vectorized = FALSE, par.set, noisy = FALSE, fn.mean = NULL,
  minimize = TRUE, constraint.fn = NULL, tags = character(0),
  global.opt.params = NULL, global.opt.value = NULL,
  local.opt.params = NULL, local.opt.values = NULL)

`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`.
`vectorized`	[`logical(1)`] Can the objective function handle “vector” input, i.~e., does it accept matrix of parameters? Default is `FALSE`.
`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.
`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(1)`] Set this to `TRUE` if the function should be minimized and to `FALSE` otherwise. The default is `TRUE`.
`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.
`tags`	[`character`] Optional character vector of tags or keywords which characterize the function, e.~g. “unimodal”, “separable”. See `getAvailableTags` for a character vector of allowed tags.
`global.opt.params`	[`list` \| `numeric` \| `data.frame` \| `matrix` \| `NULL`] Default is `NULL` which means unknown. Passing a `numeric` vector will be the most frequent case (numeric only functions). In this case there is only a single global optimum. If there are multiple global optima, passing a numeric `matrix` is the best choice. Passing a `list` or a `data.frame` is necessary if your function is mixed, e.g., it expects both numeric and discrete parameters. Internally, however, each representation is casted to a `data.frame` for reasons of consistency.
`global.opt.value`	[`numeric(1)` \| `NULL`] Global optimum value if known. Default is `NULL`, which means unknown. If only the `global.opt.params` are passed, the value is computed automatically.
`local.opt.params`	[`list` \| `numeric` \| `data.frame` \| `matrix` \| `NULL`] Default is `NULL`, which means the function has no local optima or they are unknown. For details see the description of `global.opt.params`.
`local.opt.values`	[`numeric` \| `NULL`] Value(s) of local optima. Default is `NULL`, which means unknown. If only the `local.opt.params` are passed, the values are computed automatically.

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

library(ggplot2)

fn = makeSingleObjectiveFunction(
  name = "Sphere Function",
  fn = function(x) sum(x^2),
  par.set = makeNumericParamSet("x", len = 1L, lower = -5L, upper = 5L),
  global.opt.params = list(x = 0)
)
print(fn)
print(autoplot(fn))

fn.num2 = makeSingleObjectiveFunction(
  name = "Numeric 2D",
  fn = function(x) sum(x^2),
  par.set = makeParamSet(
    makeNumericParam("x1", lower = -5, upper = 5),
    makeNumericParam("x2", lower = -10, upper = 20)
  )
)
print(fn.num2)
print(autoplot(fn.num2))

fn.mixed = makeSingleObjectiveFunction(
  name = "Mixed 2D",
  fn = function(x) x$num1^2 + as.integer(as.character(x$disc1) == "a"),
  has.simple.signature = FALSE,
  par.set = makeParamSet(
    makeNumericParam("num1", lower = -5, upper = 5),
    makeDiscreteParam("disc1", values = c("a", "b"))
  ),
  global.opt.params = list(num1 = 0, disc1 = "b")
)
print(fn.mixed)
print(autoplot(fn.mixed))

Loading required package: ParamHelpers
Loading required package: BBmisc
Loading required package: checkmate
Single-objective function
Name: Sphere Function
Description: no description
Tags: 
Noisy: FALSE
Minimize: TRUE
Constraints: TRUE
Number of parameters: 1
           Type len Def  Constr Req Tunable Trafo
x numericvector   1   - -5 to 5   -    TRUE     -
Global optimum objective value of 0.0000 at
  x
1 0
Single-objective function
Name: Numeric 2D
Description: no description
Tags: 
Noisy: FALSE
Minimize: TRUE
Constraints: TRUE
Number of parameters: 2
      Type len Def    Constr Req Tunable Trafo
x1 numeric   -   -   -5 to 5   -    TRUE     -
x2 numeric   -   - -10 to 20   -    TRUE     -
Single-objective function
Name: Mixed 2D
Description: no description
Tags: 
Noisy: FALSE
Minimize: TRUE
Constraints: TRUE
Number of parameters: 2
          Type len Def  Constr Req Tunable Trafo
num1   numeric   -   - -5 to 5   -    TRUE     -
disc1 discrete   -   -     a,b   -    TRUE     -
Global optimum objective value of 0.0000 at
  num1 disc1
1    0     b