metaheuristic_designer.simple.bayesian_optimization module#
Ready-to-run Bayesian optimization wrappers.
- bayesian_optimization_binary(objfunc, population_size=50, encoding=None, rng=None, **kwargs)[source]#
Bayesian optimization for binary-coded vectors (not supported yet).
- Return type:
- Parameters:
objfunc (ObjectiveFunc)
population_size (int)
encoding (Encoding | None)
rng (int | Generator | None)
- bayesian_optimization_discrete(objfunc, population_size=50, encoding=None, rng=None, **kwargs)[source]#
Bayesian optimization for integer-coded vectors (not supported yet).
- Return type:
- Parameters:
objfunc (ObjectiveFunc)
population_size (int)
encoding (Encoding | None)
rng (int | Generator | None)
- bayesian_optimization_real(objfunc, population_size=50, encoding=None, rng=None, **kwargs)[source]#
Bayesian optimization for real-coded vectors.
- Return type:
- Parameters:
- objfuncObjectiveFunc
The objective function to optimize.
- population_sizeint, optional
Number of individuals in the initial population (default 50).
- encodingEncoding, optional
Encoding applied to the genotype.
- rngRNGLike, optional
Random seed or generator.
- **kwargs
Forwarded to
Algorithm.
- Parameters:
objfunc (ObjectiveFunc)
population_size (int)
encoding (Encoding | None)
rng (int | Generator | None)