R: Black-Box Optimization

bb_optimize {bbotk} | R Documentation

## Black-Box Optimization

### Description

This function optimizes a function or Objective with a given method.

### Usage

    bb_optimize(
      x,
      method = "random_search",
      max_evals = 1000,
      max_time = NULL,
      ...
    )

    ## S3 method for class ''function''
    bb_optimize(
      x,
      method = "random_search",
      max_evals = 1000,
      max_time = NULL,
      lower = NULL,
      upper = NULL,
      maximize = FALSE,
      ...
    )

    ## S3 method for class 'Objective'
    bb_optimize(
      x,
      method = "random_search",
      max_evals = 1000,
      max_time = NULL,
      search_space = NULL,
      ...
    )

### Arguments

`x` | (`function` | Objective).
`method` | (`character(1)` | Optimizer) Key to retrieve optimizer from mlr_optimizers dictionary or Optimizer.
`max_evals` | (`integer(1)`) Number of allowed evaluations.
`max_time` | (`integer(1)`) Maximum allowed time in seconds.
`...` | (named `list()`) Named arguments passed to objective function. Ignored if Objective is optimized.
`lower` | (`numeric()`) Lower bounds on the parameters. If named, names are used to create the domain.
`upper` | (`numeric()`) Upper bounds on the parameters.
`maximize` | (`logical()`) Logical vector used to create the codomain e.g. c(TRUE, FALSE) -> ps(y1 = p_dbl(tags = "maximize"), y2 = pd_dbl(tags = "minimize")). If named, names are used to create the codomain.
`search_space` | (paradox::ParamSet).

### Value

`list` of

- `"par"` - Best found parameters

- `"value"` - Optimal outcome

- `"instance"` - OptimInstanceSingleCrit | OptimInstanceMultiCrit

### Note

If both `max_evals` and `max_time` are `NULL`, TerminatorNone is used. This is useful if the Optimizer can terminate itself. If both are given, TerminatorCombo is created and the optimization stops if the time or evaluation budget is exhausted.

### Examples

    # function and bounds
    fun = function(xs) {
      -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10
    }

    bb_optimize(fun, lower = c(-10, -5), upper = c(10, 5), max_evals = 10)

    # function and constant
    fun = function(xs, c) {
      -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + c
    }

    bb_optimize(fun, lower = c(-10, -5), upper = c(10, 5), max_evals = 10, c = 1)

    # objective
    fun = function(xs) {
      c(z = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
    }

    # define domain and codomain using a `ParamSet` from paradox
    domain = ps(x1 = p_dbl(-10, 10), x2 = p_dbl(-5, 5))
    codomain = ps(z = p_dbl(tags = "minimize"))
    objective = ObjectiveRFun$new(fun, domain, codomain)

    bb_optimize(objective, method = "random_search", max_evals = 10)

[Package bbotk version 0.5.3 Index]