Optimization via Generalized Simulated Annealing

Source: R/OptimizerGenSA.R

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

Inherited methods

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")) {
library(paradox)

domain = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1)))

search_space = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1)))

codomain = ParamSet$new(list(ParamDbl$new("y", tags = "minimize")))

objective_function = function(xs) {
  list(y = as.numeric(xs)^2)
}

objective = ObjectiveRFun$new(fun = objective_function,
                              domain = domain,
                              codomain = codomain)
terminator = trm("evals", n_evals = 10)
instance = OptimInstanceSingleCrit$new(
 objective = objective,
 search_space = search_space,
 terminator = terminator)

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
#>               x           y  x_domain           timestamp batch_nr
#>  1:  1.00000000 1.000000000 <list[1]> 2021-01-24 15:47:16        1
#>  2: -1.00000000 1.000000000 <list[1]> 2021-01-24 15:47:16        2
#>  3: -0.09565958 0.009150756 <list[1]> 2021-01-24 15:47:16        3
#>  4: -1.00000000 1.000000000 <list[1]> 2021-01-24 15:47:16        4
#>  5: -0.36603397 0.133980864 <list[1]> 2021-01-24 15:47:16        5
#>  6:  0.07004452 0.004906235 <list[1]> 2021-01-24 15:47:16        6
#>  7:  0.62335028 0.388565566 <list[1]> 2021-01-24 15:47:16        7
#>  8: -0.73025654 0.533274622 <list[1]> 2021-01-24 15:47:16        8
#>  9:  0.27423165 0.075202996 <list[1]> 2021-01-24 15:47:16        9
#> 10:  0.20915585 0.043746171 <list[1]> 2021-01-24 15:47:16       10