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

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 .

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.

In contrast to the GenSA::GenSA() defaults, we set trace.mat = FALSE. Note that GenSA::GenSA() uses smooth = TRUE as a default. In the case of using this optimizer for Hyperparameter Optimization you may want to set smooth = FALSE.

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

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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.

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("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
  as.data.table(instance$archive$data)
}
#>                 x            y  x_domain           timestamp batch_nr
#>  1: -0.3820675202 1.459756e-01 <list[1]> 2022-11-05 08:18:05        1
#>  2:  0.3773952425 1.424272e-01 <list[1]> 2022-11-05 08:18:05        2
#>  3: -0.7443174720 5.540085e-01 <list[1]> 2022-11-05 08:18:05        3
#>  4:  0.3773952425 1.424272e-01 <list[1]> 2022-11-05 08:18:05        4
#>  5:  0.3773962425 1.424279e-01 <list[1]> 2022-11-05 08:18:05        5
#>  6:  0.3773942425 1.424264e-01 <list[1]> 2022-11-05 08:18:05        6
#>  7: -0.3773952424 1.424272e-01 <list[1]> 2022-11-05 08:18:05        7
#>  8: -0.3773942424 1.424264e-01 <list[1]> 2022-11-05 08:18:05        8
#>  9: -0.3773962424 1.424279e-01 <list[1]> 2022-11-05 08:18:05        9
#> 10:  0.0003773952 1.424272e-07 <list[1]> 2022-11-05 08:18:05       10