bbotk (version 1.7.0)

mlr_optimizers_gensa: 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()

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Method new()

Creates a new instance of this R6 class.

Usage

OptimizerBatchGenSA$new()

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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.

Examples

if (FALSE) { # requireNamespace("GenSA", quietly = TRUE)
}
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 = OptimInstanceBatchSingleCrit$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)