Package

r-mlrmbo

Description

Bayesian Optimization and Model-Based Optimization of Expensive Black-
Box Functions. Flexible and comprehensive R toolbox for model-based
optimization ('MBO'), also known as Bayesian optimization. It 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.

Note