Flexible and comprehensive R toolbox for model-based optimization ('MBO'), also known as Bayesian optimization. It implements the Efficient Global Optimization Algorithm and is designed for both single- and multi- objective optimization with mixed continuous, categorical and conditional parameters. The machine learning toolbox 'mlr' provide dozens of regression learners to model the performance of the target algorithm with respect to the parameter settings. It provides many different infill criteria to guide the search process. Additional features include multi-point batch proposal, parallel execution as well as visualization and sophisticated logging mechanisms, which is especially useful for teaching and understanding of algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that single components can be easily replaced or adapted by the user for specific use cases.
|Author||Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Jakob Richter [aut, cre] (<https://orcid.org/0000-0003-4481-5554>), Jakob Bossek [aut] (<https://orcid.org/0000-0002-4121-4668>), Daniel Horn [aut], Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Janek Thomas [aut] (<https://orcid.org/0000-0003-4511-6245>)|
|Maintainer||Jakob Richter <[email protected]>|
|License||BSD_2_clause + file LICENSE|
|Package repository||View on CRAN|
Install the latest version of this package by entering the following in R:
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.