metaheuristic_designer.strategies.bayesian_optimization.bayesian_optimization module

Bayesian Optimization strategy.

class BayesianOptimization(initializer, objfunc, parent_sel=None, name='Bayesian Optimization', rng=None, **kwargs)

Bases: PopulationBasedStrategy

Bayesian Optimization using a Gaussian Process surrogate.

This strategy replaces the usual perturbation operator with a BOOperator, which fits a GP model to the current population and uses an acquisition function to propose new candidates.

Parameters:

initializerInitializer

Population initializer (provides the starting points).

parent_selParentSelection, optional

Parent selection method (default: identity).

namestr, optional

Display name (default "Bayesian Optimization").

**kwargs

Forwarded to BOOperator (e.g., batch_size, max_samples, kernel).

Attributes:

params

Access parameter values by attribute-style lookup.

population_size

Gets the amount of individuals in the population.

Parameters:

• initializer (Initializer)

• objfunc (ObjectiveFunc)

• parent_sel (ParentSelection)

• name (str)

• rng (Optional[RNGLike])

Methods

extra_report()	Hook called at the end of the optimization (intended for subclasses).
extra_step_info()	Hook called after each generation (intended for subclasses).
gather_parameters()	Collect the current parameters from all sub-components.
get_params()	Return a copy of the current parameter dictionary.
get_state()	Gets the current state of the search strategy as a dictionary.
initialize(objfunc)	Initializes the optimization search strategy.
step(prev_population, objfunc)	Performs a single iteration of the algorithm on a given population.
store_kwargs([progress])	Store keyword arguments and evaluate them at the given progress.
update(progress)	Advances the state of the search by one iteration.
update_kwargs([progress])	Add or replace parameters and immediately evaluate them.

reset