OptimizerCmaes class that implements CMA-ES. Calls adagio::pureCMAES() from package adagio.

Dictionary

This Optimizer can be instantiated via the dictionary mlr_optimizers or with the associated sugar function opt():

mlr_optimizers$get("cmaes")
opt("cmaes")

Parameters

par

numeric()

sigma

numeric(1)

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.

Super class

bbotk::Optimizer -> OptimizerCmaes

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerCmaes$new()


Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerCmaes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(paradox) library(data.table) 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$new(fun = objective_function, domain = domain, codomain = codomain) terminator = trm("evals", n_evals = 2) instance = OptimInstanceSingleCrit$new(objective = objective, search_space = search_space, terminator = terminator) optimizer = opt("cmaes", par = 1) # Modifies the instance by reference optimizer$optimize(instance)
#> x x_domain y #> 1: -0.4000435 <list[1]> 0.1600348
# Returns best scoring evaluation instance$result
#> x x_domain y #> 1: -0.4000435 <list[1]> 0.1600348
# Allows access of data.table of full path of all evaluations instance$archive$data()
#> x y x_domain timestamp batch_nr #> 1: -0.4000435 0.1600348 <list[1]> 2020-10-25 04:09:51 1 #> 2: 1.0000000 1.0000000 <list[1]> 2020-10-25 04:09:51 2