
OptimizerGenSA
class that implements generalized simulated annealing. Calls
GenSA::GenSA()
from package GenSA.
This Optimizer can be instantiated via the dictionary
mlr_optimizers or with the associated sugar function opt()
:
mlr_optimizers$get("gensa") opt("gensa")
smooth
logical(1)
temperature
numeric(1)
acceptance.param
numeric(1)
verbose
logical(1)
trace.mat
logical(1)
For the meaning of the control parameters, see GenSA::GenSA()
. Note that we
have removed all control parameters which refer to the termination of the
algorithm and where our terminators allow to obtain the same behavior.
bbotk::Optimizer
-> OptimizerGenSA
new()
Creates a new instance of this R6 class.
OptimizerGenSA$new()
clone()
The objects of this class are cloneable with this method.
OptimizerGenSA$clone(deep = FALSE)
deep
Whether to make a deep clone.
# NOT RUN {
library(paradox)
domain = ParamSet$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)
# Returns best scoring evaluation
instance$result
# Allows access of data.table of full path of all evaluations
instance$archive$data()
# }
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