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bbotk (version 0.2.2)

mlr_optimizers_cmaes: Optimization via Covariance Matrix Adaptation Evolution Strategy

Description

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

Arguments

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

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

Run this code
# NOT RUN {
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)

# Returns best scoring evaluation
instance$result

# Allows access of data.table of full path of all evaluations
instance$archive$data()
# }

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