mlr_optimizers_irace {bbotk}R Documentation

Optimization via Iterated Racing


OptimizerIrace class that implements iterated racing. Calls irace::irace() from package irace.



A list of instances where the configurations executed on.


A function that executes the objective function with a specific parameter configuration and instance. A default function is provided, see section "Target Runner and Instances".

For the meaning of all other parameters, see irace::defaultScenario(). Note that we have removed all control parameters which refer to the termination of the algorithm. Use TerminatorEvals instead. Other terminators do not work with OptimizerIrace.

In contrast to irace::defaultScenario(), we set digits = 15. This represents double parameters with a higher precision and avoids rounding errors.

Target Runner and Instances

The irace package uses a targetRunner script or R function to evaluate a configuration on a particular instance. Usually it is not necessary to specify a targetRunner function when using OptimizerIrace. A default function is used that forwards several configurations and instances to the user defined objective function. As usually, the user defined function has a xs, xss or xdt parameter depending on the used Objective class. For irace, the function needs an additional instances parameter.

fun = function(xs, instances) {
 # function to evaluate configuration in `xs` on instance `instances`


The Archive holds the following additional columns:


The optimization result (instance$result) is the best performing elite of the final race. The reported performance is the average performance estimated on all used instances.


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


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 class

bbotk::Optimizer -> OptimizerIrace


Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.


Method clone()

The objects of this class are cloneable with this method.

OptimizerIrace$clone(deep = FALSE)

Whether to make a deep clone.


Lopez-Ibanez M, Dubois-Lacoste J, Caceres LP, Birattari M, Stuetzle T (2016). “The irace package: Iterated racing for automatic algorithm configuration.” Operations Research Perspectives, 3, 43–58. doi:10.1016/j.orp.2016.09.002.



search_space = domain = ps(
  x1 = p_dbl(-5, 10),
  x2 = p_dbl(0, 15)

codomain = ps(y = p_dbl(tags = "minimize"))

# branin function with noise
# the noise generates different instances of the branin function
# the noise values are passed via the `instances` parameter
fun = function(xdt, instances) {
  ys = branin(xdt[["x1"]], xdt[["x2"]], noise = as.numeric(instances))
  data.table(y = ys)

# define objective with instances as a constant
objective = ObjectiveRFunDt$new(
 fun = fun,
 domain = domain,
 codomain = codomain,
 constants = ps(instances = p_uty()))

instance = OptimInstanceSingleCrit$new(
  objective = objective,
  search_space = search_space,
  terminator = trm("evals", n_evals = 1000))

# create instances of branin function
instances = rnorm(10, mean = 0, sd = 0.1)

# load optimizer irace and set branin instances
optimizer = opt("irace", instances = instances)

# modifies the instance by reference

# best scoring configuration

# all evaluations$archive)

[Package bbotk version 0.7.2 Index]