R: Optimization via Covariance Matrix Adaptation Evolution...

mlr_optimizers_cmaes {bbotk} | R Documentation

## Optimization via Covariance Matrix Adaptation Evolution Strategy

### Description

`OptimizerCmaes` class that implements CMA-ES. Calls `adagio::pureCMAES()` from package adagio. The algorithm is typically applied to search space dimensions between three and fifty. Lower search space dimensions might crash.

### Dictionary

This Optimizer can be instantiated via the dictionary mlr_optimizers or with the associated sugar function `opt()`:

    mlr_optimizers$get("cmaes")
    opt("cmaes")

### Parameters

`sigma`

  `numeric(1)`

`start_values`

  `character(1)`
  Create `random` start values or based on `center` of search space? In the latter case, it is the center of the parameters before a trafo is applied.

For the meaning of the control parameters, see `adagio::pureCMAES()`. Note that we have removed all control parameters which refer to the termination of the algorithm and where our terminators allow to obtain the same behavior.

### Progress Bars

`$optimize()` supports progress bars via the package progressr combined with a Terminator. Simply wrap the function in `progressr::with_progress()` to enable them. We recommend to use package progress as backend; enable with `progressr::handlers("progress")`.

### Super class

`bbotk::Optimizer` -> `OptimizerCmaes`

### Methods

#### Public methods

- `OptimizerCmaes$new()`

- `OptimizerCmaes$clone()`

Inherited methods

- `bbotk::Optimizer$format()`

- `bbotk::Optimizer$help()`

- `bbotk::Optimizer$optimize()`

- `bbotk::Optimizer$print()`

#### Method `new()`

Creates a new instance of this R6 class.

##### Usage

    OptimizerCmaes$new()

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    OptimizerCmaes$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### Examples

    if (requireNamespace("adagio")) {
      search_space = domain = ps(
        x1 = p_dbl(-10, 10),
        x2 = p_dbl(-5, 5)
      )

      codomain = ps(y = p_dbl(tags = "maximize"))

      objective_function = function(xs) {
        c(y = -(xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
      }

      objective = ObjectiveRFun$new(
        fun = objective_function,
        domain = domain,
        codomain = codomain)

      instance = OptimInstanceSingleCrit$new(
        objective = objective,
        search_space = search_space,
        terminator = trm("evals", n_evals = 10))

      optimizer = opt("cmaes")

      # modifies the instance by reference
      optimizer$optimize(instance)

      # returns best scoring evaluation
      instance$result

      # allows access of data.table of full path of all evaluations
      as.data.table(instance$archive$data)
    }

[Package bbotk version 0.5.3 Index]