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$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)
}
#> Loading required namespace: GenSA
#> 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:  0.24023076 0.057710820 <list[1]> 2021-09-17 04:11:52        1
#>  2: -0.97656091 0.953671213 <list[1]> 2021-09-17 04:11:52        2
#>  3: -0.22191587 0.049246652 <list[1]> 2021-09-17 04:11:52        3
#>  4: -0.63463741 0.402764647 <list[1]> 2021-09-17 04:11:52        4
#>  5:  0.03938558 0.001551224 <list[1]> 2021-09-17 04:11:52        5
#>  6:  1.00000000 1.000000000 <list[1]> 2021-09-17 04:11:52        6
#>  7: -0.81987020 0.672187138 <list[1]> 2021-09-17 04:11:52        7
#>  8:  0.61639058 0.379937345 <list[1]> 2021-09-17 04:11:53        8
#>  9: -0.60987571 0.371948385 <list[1]> 2021-09-17 04:11:53        9
#> 10:  0.52542766 0.276074230 <list[1]> 2021-09-17 04:11:53       10