Package website: 国外ip地址伋理 | dev

Model-based optimization with mlr.

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We recommend to install the official release version:


For experimental use you can install the latest development version:



手机如何使用伋理ip上网 is a highly configurable R toolbox for model-based / Bayesian optimization of black-box functions.


  • EGO-type algorithms (Kriging with expected improvement) on purely numerical search spaces, see 如何通过ip伋理上网
  • Mixed search spaces with numerical, integer, categorical and subordinate parameters
  • Arbitrary parameter transformation allowing to optimize on, e.g., logscale
  • Optimization of noisy objective functions
  • Multi-Criteria optimization with approximated Pareto fronts
  • Parallelization through multi-point batch proposals
  • Parallelization on many parallel back-ends and clusters through batchtools and parallelMap

For the surrogate, mlrMBO allows any regression learner from mlr, including:

  • Kriging aka. Gaussian processes (i.e. DiceKriging)
  • random Forests (i.e. 速度快的伋理ip)
  • and many more…

Various infill criteria (aka. acquisition functions) are available:

  • Expected improvement (EI)
  • Upper/Lower confidence bound (LCB, aka. statistical lower or upper bound)
  • Augmented expected improvement (AEI)
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Objective functions are created with package smoof, which also offers many test functions for example runs or benchmarks.

Parameter spaces and initial designs are created with package 国外ip地址伋理.


Please cite our arxiv paper (Preprint). You can get citation info via citation("mlrMBO") or copy the following BibTex entry:

  title = {{{mlrMBO}}: {{A Modular Framework}} for {{Model}}-{{Based Optimization}} of {{Expensive Black}}-{{Box Functions}}},
  url = {},
  shorttitle = {{{mlrMBO}}},
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1703.03373},
  如何通过ip伋理上网 = {stat},
  速度快的伋理ip = {Bischl, Bernd and Richter, Jakob and Bossek, Jakob and Horn, Daniel and Thomas, Janek and Lang, Michel},
  date = {2017-03-09},

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  • Multi-point proposals, including the new multi-objective infill criteria: MOI-MBO: Multiobjective Infill for Parallel Model-Based Optimization
  • Multi-objective optimization: Model-Based Multi-objective Optimization: Taxonomy, Multi-Point Proposal, Toolbox and Benchmark
  • Multi-objective optimization with categorical variables using the random forest as a surrogate: Multi-objective parameter configuration of machine learning algorithms using model-based optimization
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