class, evaluator, random_state: Optional[int] = None, log_dir: str = '.', verbose: int = 0, population_size: int = 100, sample_size: int = 10, surrogate_model: str = 'RF', acq_func: str = 'UCB', kappa: float = 0.001, xi: float = 1e-06, n_points: int = 10000, liar_strategy: str = 'cl_max', n_jobs: int = 1, mode: str = 'async', **kwargs)[source]


Aging evolution with Bayesian Optimization.

This algorithm build on the Regularized Evolution. It cumulates Hyperparameter optimization with Bayesian optimisation and Neural architecture search with regularized evolution.

  • problem (NaProblem) – Neural architecture search problem describing the search space to explore.

  • evaluator (Evaluator) – An Evaluator instance responsible of distributing the tasks.

  • random_state (int, optional) – Random seed. Defaults to None.

  • log_dir (str, optional) – Log directory where search’s results are saved. Defaults to “.”.

  • verbose (int, optional) – Indicate the verbosity level of the search. Defaults to 0.

  • population_size (int, optional) – the number of individuals to keep in the population. Defaults to 100.

  • sample_size (int, optional) – the number of individuals that should participate in each tournament. Defaults to 10.

  • surrogate_model (str, optional) – Surrogate model used by the Bayesian optimization. Can be a value in ["RF", "ET", "GBRT", "DUMMY"]. Defaults to "RF".

  • acq_func (str, optional) – Acquisition function used by the Bayesian optimization. Can be a value in ["UCB", "EI", "PI", "gp_hedge"]. Defaults to "UCB".

  • kappa (float, optional) – Manage the exploration/exploitation tradeoff for the “UCB” acquisition function. Defaults to 0.001 for strong exploitation.

  • xi (float, optional) – Manage the exploration/exploitation tradeoff of "EI" and "PI" acquisition function. Defaults to 0.000001 for strong exploitation.

  • n_points (int, optional) – The number of configurations sampled from the search space to infer each batch of new evaluated configurations. Defaults to 10000.

  • liar_strategy (str, optional) – Definition of the constant value use for the Liar strategy. Can be a value in ["cl_min", "cl_mean", "cl_max"] . Defaults to "cl_max".

  • n_jobs (int, optional) – Number of parallel processes used to fit the surrogate model of the Bayesian optimization. A value of -1 will use all available cores. Defaults to 1.

  • mode (str, optional) – Define if the search should be asynchronous or batch synchronous. Choice in [“sync”, “async”]. Defaults to “async”.



Execute the search algorithm.


Terminate the search.

search(max_evals: int = - 1, timeout: Optional[int] = None)

Execute the search algorithm.

  • max_evals (int, optional) – The maximum number of evaluations of the run function to perform before stopping the search. Defaults to -1, will run indefinitely.

  • timeout (int, optional) – The time budget (in seconds) of the search before stopping. Defaults to None, will not impose a time budget.


a pandas DataFrame containing the evaluations performed.

Return type



Terminate the search.


SearchTerminationError – raised when the search is terminated with SIGALARM