Optimization via Generalized Simulated Annealing

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.

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.

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)
}
#> Warning: CMA-ES is typically applied to search space dimensions between three and fifty. A lower search space dimension might crash.
#>               x           y  x_domain           timestamp batch_nr
#>  1:  0.65581897 0.430098528 <list[1]> 2022-06-30 04:24:06        1
#>  2:  0.78240611 0.612159322 <list[1]> 2022-06-30 04:24:06        2
#>  3:  0.53105778 0.282022364 <list[1]> 2022-06-30 04:24:06        3
#>  4:  1.00000000 1.000000000 <list[1]> 2022-06-30 04:24:06        4
#>  5: -0.37515365 0.140740261 <list[1]> 2022-06-30 04:24:06        5
#>  6:  0.08188983 0.006705945 <list[1]> 2022-06-30 04:24:06        6
#>  7: -0.10079147 0.010158921 <list[1]> 2022-06-30 04:24:06        7
#>  8:  1.00000000 1.000000000 <list[1]> 2022-06-30 04:24:06        8
#>  9:  0.08963119 0.008033750 <list[1]> 2022-06-30 04:24:06        9
#> 10:  0.89871287 0.807684829 <list[1]> 2022-06-30 04:24:06       10