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.

### 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

`par`

  `numeric()`

`sigma`

  `numeric(1)`

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.

### Super class

`bbotk::Optimizer` -> `OptimizerCmaes`

### Methods

#### Public methods

- `OptimizerCmaes$new()`

- `OptimizerCmaes$clone()`

Inherited methods

- `bbotk::Optimizer$format()`

- `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

    library(paradox)
    library(data.table)

    domain = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1)))

    search_space = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1)))

    codomain = ParamSet$new(list(ParamDbl$new("y", tags = "minimize")))

    objective_function = function(xs) {
      list(y = as.numeric(xs)^2)
    }

    objective = ObjectiveRFun$new(fun = objective_function,
                                  domain = domain,
                                  codomain = codomain)
    terminator = trm("evals", n_evals = 2)
    instance = OptimInstanceSingleCrit$new(objective = objective,
                                 search_space = search_space,
                                 terminator = terminator)

    optimizer = opt("cmaes", par = 1)

    # 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
    instance$archive$data()

[Package bbotk version 0.2.2 Index]