mbo_defaults: Defaults for OptimizerMbo

In mlr3mbo: Flexible Bayesian Optimization

Defaults for OptimizerMbo

Description

The following defaults are set for OptimizerMbo during optimization if the respective fields are not set during initialization.

• Loop Function (default_loop_function): The Bayesian optimization flavor. Defaults to bayesopt_ego for single-objective and bayesopt_smsego for multi-objective optimization.

• Surrogate (default_surrogate): The surrogate model. Uses a Gaussian process (default_gp) for purely numeric parameter spaces without dependencies, and a random forest (default_rf) otherwise. For multi-objective optimization, one surrogate learner per objective is used.

• Acquisition Function (default_acqfunction): The criterion used to propose future points. Defaults to mlr_acqfunctions_cb (confidence bound) for synchronous single-objective optimization (lambda = 3 for numeric, lambda = 1 for mixed spaces), mlr_acqfunctions_smsego for synchronous multi-objective optimization, and mlr_acqfunctions_stochastic_cb for asynchronous single-objective optimization.

• Acquisition Function Optimizer (default_acqoptimizer): The optimizer for the acquisition function. Defaults to AcqOptimizerLocalSearch.

• Result Assigner (default_result_assigner): Determines how the final result is assigned. Defaults to ResultAssignerArchive.

See Also

Other mbo_defaults: default_acqfunction(), default_acqoptimizer(), default_gp(), default_loop_function(), default_result_assigner(), default_rf(), default_surrogate()

mlr3mbo documentation built on July 21, 2026, 5:08 p.m.