# mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions

arXiv: https://arxiv.org/abs/1703.03373 · HTML: https://mlsanity.com/paper/1703.03373/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2017.

## Built on by (most cited first, 16 of 16)

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- [HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization](https://mlsanity.com/paper/2210.00798/) (2022)
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- [Facilitating automated conversion of scientific knowledge into scientific simulation models with the Machine Assisted Generation, Calibration, and Comparison (MAGCC) Framework](https://mlsanity.com/paper/2204.10382/) (2022)
- [Modeling and Optimizing Laser-Induced Graphene](https://mlsanity.com/paper/2107.14257/) (2021)
- [A hybrid estimation of distribution algorithm for joint stratification and sample allocation](https://mlsanity.com/paper/2201.04068/) (2022)
- [AR-Sieve Bootstrap for the Random Forest and a simulation-based comparison with rangerts time series prediction](https://mlsanity.com/paper/2410.00942/) (2024)
- [mlr3mbo: Bayesian Optimization in R](https://mlsanity.com/paper/2603.29730/) (2026)

## Abstract (arXiv)

We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model. It is designed for both single- and multi-objective optimization with mixed continuous, categorical and conditional parameters. Additional features include multi-point batch proposal, parallelization, visualization, logging and error-handling. mlrMBO is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases, e.g., any regression learner from the mlr toolbox for machine learning can be used, and infill criteria and infill optimizers are easily exchangeable. We empirically demonstrate that mlrMBO provides state-of-the-art performance by comparing it on different benchmark scenarios against a wide range of other optimizers, including DiceOptim, rBayesianOptimization, SPOT, SMAC, Spearmint, and Hyperopt.