Optimization via Covariance Matrix Adaptation Evolution Strategy
OptimizerCmaes class that implements CMA-ES. Calls
adagio::pureCMAES() from package adagio.
This Optimizer can be instantiated via the dictionary
mlr_optimizers or with the associated sugar function opt():
mlr_optimizers$get("cmaes")
opt("cmaes")sigmanumeric(1)
start_valuescharacter(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.
For the meaning of the control parameters, see adagio::pureCMAES(). 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.
$optimize() supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress() to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress").
bbotk::Optimizer -> OptimizerCmaes
new()
Creates a new instance of this R6 class.
OptimizerCmaes$new()
clone()
The objects of this class are cloneable with this method.
OptimizerCmaes$clone(deep = FALSE)
deepWhether to make a deep clone.
if(requireNamespace("adagio")) {
library(paradox)
domain = ParamSet$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)
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