Hyperparameter Tuning with Covariance Matrix Adaptation Evolution Strategy

Subclass for Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Calls adagio::pureCMAES() from package adagio.

Source

Hansen N (2016). “The CMA Evolution Strategy: A Tutorial.” 1604.00772.

Dictionary

This Tuner can be instantiated with the associated sugar function tnr():

tnr("cmaes")

Control 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.

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.

Progress Bars

$optimize() supports progress bars via the package progressr combined with a bbotk::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").

Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

Optimizer

This Tuner is based on bbotk::OptimizerBatchCmaes which can be applied on any black box optimization problem. See also the documentation of bbotk.

Resources

There are several sections about hyperparameter optimization in the mlr3book.

• Getting started with hyperparameter optimization.

• An overview of all tuners can be found on our website.

• Tune a support vector machine on the Sonar data set.

• Learn about tuning spaces.

• Estimate the model performance with nested resampling.

• Learn about multi-objective optimization.

• Simultaneously optimize hyperparameters and use early stopping with XGBoost.

• Automate the tuning.

The gallery features a collection of case studies and demos about optimization.

• Learn more advanced methods with the Practical Tuning Series.

• Learn about hotstarting models.

• Run the default hyperparameter configuration of learners as a baseline.

• Use the Hyperband optimizer with different budget parameters.

The cheatsheet summarizes the most important functions of mlr3tuning.

See also

Other Tuner: Tuner, mlr_tuners, mlr_tuners_design_points, mlr_tuners_gensa, mlr_tuners_grid_search, mlr_tuners_internal, mlr_tuners_irace, mlr_tuners_nloptr, mlr_tuners_random_search

Super classes

mlr3tuning::Tuner -> mlr3tuning::TunerBatch -> mlr3tuning::TunerBatchFromOptimizerBatch -> TunerBatchCmaes

Methods

Public methods

• TunerBatchCmaes$new()

• TunerBatchCmaes$clone()

Inherited methods

• mlr3tuning::Tuner$format()
• mlr3tuning::Tuner$help()
• mlr3tuning::Tuner$print()
• mlr3tuning::TunerBatchFromOptimizerBatch$optimize()

---

Method new()

Creates a new instance of this R6 class.

Usage

TunerBatchCmaes$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

TunerBatchCmaes$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Hyperparameter Optimization

# load learner and set search space
learner = lrn("classif.rpart",
  cp = to_tune(1e-04, 1e-1, logscale = TRUE),
  minsplit = to_tune(p_dbl(2, 128, trafo = as.integer)),
  minbucket = to_tune(p_dbl(1, 64, trafo = as.integer))
)

# run hyperparameter tuning on the Palmer Penguins data set
instance = tune(
  tuner = tnr("cmaes"),
  task = tsk("penguins"),
  learner = learner,
  resampling = rsmp("holdout"),
  measure = msr("classif.ce"),
  term_evals = 10)

# best performing hyperparameter configuration
instance$result
#>           cp minbucket minsplit learner_param_vals  x_domain classif.ce
#>        <num>     <num>    <num>             <list>    <list>      <num>
#> 1: -7.336334  15.20906 107.2338          <list[4]> <list[3]> 0.07826087

# all evaluated hyperparameter configuration
as.data.table(instance$archive)
#>            cp minbucket  minsplit classif.ce runtime_learners
#>         <num>     <num>     <num>      <num>            <num>
#>  1: -7.336334 15.209063 107.23382 0.07826087            0.005
#>  2: -9.210340 64.000000  22.89758 0.12173913            0.006
#>  3: -2.621780 31.763900 128.00000 0.07826087            0.007
#>  4: -2.302585  1.000000 106.26335 0.07826087            0.005
#>  5: -2.302585 62.039211 128.00000 0.12173913            0.006
#>  6: -4.416664 54.268412 108.94055 0.07826087            0.006
#>  7: -2.302585  4.755131  72.28910 0.07826087            0.006
#>  8: -4.734599 30.835601  24.51517 0.07826087            0.005
#>  9: -9.210340 39.906483  97.63893 0.07826087            0.006
#> 10: -6.242816 18.946310  96.50841 0.07826087            0.006
#>               timestamp warnings errors  x_domain batch_nr  resample_result
#>                  <POSc>    <int>  <int>    <list>    <int>           <list>
#>  1: 2025-06-04 09:51:52        0      0 <list[3]>        1 <ResampleResult>
#>  2: 2025-06-04 09:51:52        0      0 <list[3]>        2 <ResampleResult>
#>  3: 2025-06-04 09:51:52        0      0 <list[3]>        3 <ResampleResult>
#>  4: 2025-06-04 09:51:52        0      0 <list[3]>        4 <ResampleResult>
#>  5: 2025-06-04 09:51:52        0      0 <list[3]>        5 <ResampleResult>
#>  6: 2025-06-04 09:51:52        0      0 <list[3]>        6 <ResampleResult>
#>  7: 2025-06-04 09:51:52        0      0 <list[3]>        7 <ResampleResult>
#>  8: 2025-06-04 09:51:52        0      0 <list[3]>        8 <ResampleResult>
#>  9: 2025-06-04 09:51:52        0      0 <list[3]>        9 <ResampleResult>
#> 10: 2025-06-04 09:51:52        0      0 <list[3]>       10 <ResampleResult>

# fit final model on complete data set
learner$param_set$values = instance$result_learner_param_vals
learner$train(tsk("penguins"))