mlrMBO: A brief introduction

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The main goal of mlrMBO is to optimize expensive black-box functions by model-based optimization (aka Bayesian optimization) and to provide a unified interface for different optimization tasks and algorithmic MBO variants. Supported are, among other things:

This vignette gives a brief overview of the features of mlrMBO. A more detailed documentation can be found on:



Installing mlrMBO will also install and load the dependencies mlr, ParamHelpers, and smoof. For this tutorial, you also need the additional packages DiceKriging and randomForest.


General MBO workflow

  1. Define objective function and its parameters using the package smoof.
  2. Generate initial design (optional).
  3. Define mlr learner for surrogate model (optional).
  4. Set up a MBO control object.
  5. Start the optimization with mbo().

As a simple example we minimize a cosine-like function with an initial design of 5 points and 10 sequential MBO iterations. Thus, the optimizer is allowed 15 evaluations of the objective function in total to approximate the optimum.

Objective Function

Instead of manually defining the objective, we use the smoof package which offers many toy and benchmark functions for optimization. = makeCosineMixtureFunction(1) = convertToMinimization(

You are not limited to these test functions but can define arbitrary objective functions with smoof.

  name = "my_sphere",
  fn = function(x) {
    sum(x*x) + 7
  par.set = makeParamSet(
    makeNumericVectorParam("x", len = 2L, lower = -5, upper = 5)
  minimize = TRUE

Check ?smoof::makeSingleObjectiveFunction for further details.

Initial Design

Before MBO can really start, it needs a set of already evaluated points - the inital design, as we have to initially learn our first machine learning regression model to propose new points for evaluation. If no design is given (i.e. design = NULL), mbo() will use a Maximin Latin Hypercube lhs::maximinLHS() design with n = 4 * getNumberOfParameters( points. If the design does not include function outcomes mbo() will evaluate the design first before starting with the MBO algorithm. In this example we generate our own design.

des = generateDesign(n = 5, par.set = getParamSet(, fun = lhs::randomLHS)

We will also precalculate the results:

des$y = apply(des, 1,

Note: mlrMBO uses y as a default name for the outcome of the objective function. This can be changed in the control object.

Surrogate Model

We decide to use Kriging as our surrogate model because it has proven to be quite effective for numerical domains. The surrogate must be specified as a mlr regression learner: = makeLearner("", predict.type = "se", covtype = "matern3_2", control = list(trace = FALSE))

Note: If no surrogate learner is defined, mbo() automatically uses Kriging for a numerical domain, otherwise random forest regression.


The MBOControl object allows customization of the optimization run and algorithmic behavior of MBO. It is created with makeMBOControl(), and can be modified with further setter-functions.

For further customization there are the following functions:

control = makeMBOControl()
control = setMBOControlTermination(control, iters = 10)
control = setMBOControlInfill(control, crit = makeMBOInfillCritEI())

Start the optimization

Finally, we start the optimization process and print the result object. It contains the best best found solution and its corresponding objective value.

run = mbo(, design = des, learner =, control = control, = TRUE)


For more insights into the MBO process, we can also start the previous optimization with the function exampleRun() instead of mbo(). This augments the results of mbo() with additional information for plotting. Here, we plot the optimization state at iterations 1, 2, and 10.

run = exampleRun(, learner =, control = control, = FALSE)
plotExampleRun(run, iters = c(1L, 2L, 10L), pause = FALSE)

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mlrMBO documentation built on June 25, 2018, 9:04 a.m.