mlr_optimizers_cmaes: Optimization via Covariance Matrix Adaptation Evolution...

In bbotk: Black-Box Optimization Toolkit

Optimization via Covariance Matrix Adaptation Evolution Strategy

Description

OptimizerBatchCmaes class that implements CMA-ES. Calls libcmaesr::cmaes() from package libcmaesr, which is a lightweight interface to the libcmaes C++ library. The algorithm is typically applied to search space dimensions between three and fifty.

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

start_values

character(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. If set to "custom", the start values can be passed via the start parameter.

start

numeric()
Custom start values. Only applicable if start_values parameter is set to "custom".

seed

integer(1)
Seed of the random number generator of libcmaes. Unset by default, in which case the generator is seeded from R and the optimization is reproducible with set.seed().

All remaining parameters are passed to libcmaesr::cmaes_control(), see there for their meaning. 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, i.e. max_fevals, max_iter, and ftarget. The internal convergence criteria of the algorithm still apply, so the optimization can stop before the Terminator is triggered.

Batch evaluation

The optimizer evaluates a whole generation of lambda points in one batch. The Terminator is only checked between generations, so the number of evaluations can exceed the budget of TerminatorEvals by up to lambda - 1 points.

Progress Bars

⁠$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").

Super classes

Optimizer -> OptimizerBatch -> OptimizerBatchCmaes

Methods

Public methods

• OptimizerBatchCmaes$new()

• OptimizerBatchCmaes$clone()

Inherited methods

• Optimizer$format()
• Optimizer$help()
• Optimizer$print()
• OptimizerBatch$optimize()

---

OptimizerBatchCmaes$new()

Creates a new instance of this R6 class.

Usage

OptimizerBatchCmaes$new()

---

OptimizerBatchCmaes$clone()

The objects of this class are cloneable with this method.

Usage

OptimizerBatchCmaes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# example only runs if libcmaesr is available
if (mlr3misc::require_namespaces("libcmaesr", quietly = TRUE)) {
# define the objective function
fun = function(xs) {
  list(y = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 - (xs[[3]] + 4)^2 + 10)
}

# set domain
domain = ps(
  x1 = p_dbl(-10, 10),
  x2 = p_dbl(-5, 5),
  x3 = p_dbl(-5, 5)
)

# set codomain
codomain = ps(
  y = p_dbl(tags = "maximize")
)

# create objective
objective = ObjectiveRFun$new(
  fun = fun,
  domain = domain,
  codomain = codomain,
  properties = "deterministic"
)

# initialize instance
instance = oi(
  objective = objective,
  terminator = trm("evals", n_evals = 20)
)

# load optimizer
optimizer = opt("cmaes")

# trigger optimization
optimizer$optimize(instance)

# all evaluated configurations
instance$archive

# best performing configuration
instance$result
}

bbotk documentation built on Sept. 2, 2026, 5:08 p.m.