OptimizerGenSA
class that implements generalized simulated annealing. Calls
GenSA::GenSA()
from package GenSA.
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 .
This Optimizer can be instantiated via the dictionary
mlr_optimizers or with the associated sugar function opt()
:
mlr_optimizers$get("gensa") opt("gensa")
smooth
temperature
acceptance.param
verbose
trace.mat
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.
$optimize()
supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress()
to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress")
.
bbotk::Optimizer
-> OptimizerGenSA
Inherited methods
new()
Creates a new instance of this R6 class.
OptimizerGenSA$new()
clone()
The objects of this class are cloneable with this method.
OptimizerGenSA$clone(deep = FALSE)
deep
Whether to make a deep clone.
if(requireNamespace("GenSA")) { library(paradox) domain = ParamSet$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) }#>#> 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