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.

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

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("gensa") # Modifies the instance by reference optimizer$optimize(instance)
#> x x_domain y #> 1: -6.591949e-17 <list[1]> 4.345379e-33
# Returns best scoring evaluation instance$result
#> x x_domain y #> 1: -6.591949e-17 <list[1]> 4.345379e-33
# Allows access of data.table of full path of all evaluations instance$archive$data()
#> x y x_domain timestamp batch_nr #> 1: -4.445223e-03 1.976000e-05 <list[1]> 2020-10-25 04:09:52 1 #> 2: 7.550175e-01 5.700515e-01 <list[1]> 2020-10-25 04:09:52 2 #> 3: -4.199731e-01 1.763774e-01 <list[1]> 2020-10-25 04:09:52 3 #> 4: -4.445223e-03 1.976000e-05 <list[1]> 2020-10-25 04:09:52 4 #> 5: -4.444223e-03 1.975112e-05 <list[1]> 2020-10-25 04:09:52 5 #> 6: -4.446223e-03 1.976890e-05 <list[1]> 2020-10-25 04:09:52 6 #> 7: 4.445223e-03 1.976000e-05 <list[1]> 2020-10-25 04:09:52 7 #> 8: 4.446223e-03 1.976890e-05 <list[1]> 2020-10-25 04:09:52 8 #> 9: 4.444223e-03 1.975112e-05 <list[1]> 2020-10-25 04:09:52 9 #> 10: -6.591949e-17 4.345379e-33 <list[1]> 2020-10-25 04:09:52 10