R: Optimization via Generalized Simulated Annealing

mlr_optimizers_gensa {bbotk} | R Documentation

## Optimization via Generalized Simulated Annealing

### Description

`OptimizerGenSA` class that implements generalized simulated annealing. Calls `GenSA::GenSA()` from package GenSA.

### Dictionary

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

    mlr_optimizers$get("gensa")
    opt("gensa")

### Parameters

`smooth`

  `logical(1)`

`temperature`

  `numeric(1)`

`acceptance.param`

  `numeric(1)`

`verbose`

  `logical(1)`

`trace.mat`

  `logical(1)`

For the meaning of the control parameters, see `GenSA::GenSA()`. 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` -> `OptimizerGenSA`

### Methods

#### Public methods

- `OptimizerGenSA$new()`

- `OptimizerGenSA$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

    OptimizerGenSA$new()

#### Method `clone()`

The objects of this class are cloneable with this method.

##### Usage

    OptimizerGenSA$clone(deep = FALSE)

##### Arguments

`deep`

  Whether to make a deep clone.

### Source

Tsallis C, Stariolo DA (1996). “Generalized simulated annealing.” Physica A: Statistical Mechanics and its Applications, 233(1-2), 395–406. doi: 10.1016/s0378-4371(96)00271-3.

Xiang Y, Gubian S, Suomela B, Hoeng J (2013). “Generalized Simulated Annealing for Global Optimization: The GenSA Package.” The R Journal, 5(1), 13. doi: 10.32614/rj-2013-002.

### Examples

    if (requireNamespace("GenSA")) {

      search_space = domain = ps(x = p_dbl(lower = -1, upper = 1))

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

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

      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]