mlr_optimizers_gensa: Generalized Simulated Annealing

In bbotk: Black-Box Optimization Toolkit

Generalized Simulated Annealing

Description

OptimizerBatchGenSA 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

par

numeric()
Initial parameter values. Default is NULL, in which case, default values will be generated automatically.

start_values

character(1)
Create "random" start values or based on "center" of search space? In the latter case, it is the center of the parameters before a trafo is applied. By default, nloptr will generate start values automatically. Custom start values can be passed via the par parameter.

For the meaning of the control parameters, see GenSA::GenSA(). 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.

Internal Termination Parameters

The algorithm can terminated with all Terminators. Additionally, the following internal termination parameters can be used:

maxit

integer(1)
Maximum number of iterations. Original default is 5000. Overwritten with .Machine$integer.max.

threshold.stop

numeric(1)
Threshold stop. Deactivated with NULL. Default is NULL.

nb.stop.improvement

integer(1)
Number of stop improvement. Deactivated with -1L. Default is -1L.

max.call

integer(1)
Maximum number of calls. Original default is 1e7. Overwritten with .Machine$integer.max.

max.time

integer(1)
Maximum time. Deactivate with NULL. Default is NULL.

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 classes

bbotk::Optimizer -> bbotk::OptimizerBatch -> OptimizerBatchGenSA

Methods

Public methods

• OptimizerBatchGenSA$new()

• OptimizerBatchGenSA$clone()

Inherited methods

• bbotk::Optimizer$format()
• bbotk::Optimizer$help()
• bbotk::Optimizer$print()
• bbotk::OptimizerBatch$optimize()

---

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerBatchGenSA$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerBatchGenSA$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. \Sexpr[results=rd]{tools:::Rd_expr_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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.32614/rj-2013-002")}.

Examples

# example only runs if GenSA is available
if (mlr3misc::require_namespaces("GenSA", quietly = TRUE)) {
# define the objective function
fun = function(xs) {
  list(y = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
}

# set domain
domain = ps(
  x1 = p_dbl(-10, 10),
  x2 = p_dbl(-5, 5)
)

# set codomain
codomain = ps(
  y = p_dbl(tags = "maximize")
)

# create objective
objective = ObjectiveRFun$new(
  fun = fun,
  domain = domain,
  codomain = codomain,
  properties = "deterministic"
)

# initialize instance
instance = oi(
  objective = objective,
  terminator = trm("evals", n_evals = 20)
)

# load optimizer
optimizer = opt("gensa")

# trigger optimization
optimizer$optimize(instance)

# all evaluated configurations
instance$archive

# best performing configuration
instance$result
}

bbotk documentation built on Nov. 5, 2025, 6:23 p.m.