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.

### Super class

`bbotk::Optimizer` -> `OptimizerGenSA`

### Methods

#### Public methods

- `OptimizerGenSA$new()`

- `OptimizerGenSA$clone()`

Inherited methods

- `bbotk::Optimizer$format()`

- `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

    library(paradox)

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

    optimizer = opt("gensa")

    # 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]