EDAspy.optimization package



EDAspy.optimization.eda module

class EDAspy.optimization.eda.EDA(size_gen: int, max_iter: int, dead_iter: int, n_variables: int, alpha: float = 0.5, elite_factor: float = 0.4, disp: bool = True)[source]

Bases: ABC

Abstract class which defines the general performance of the algorithms. The baseline of the EDA approach is defined in this object. The specific configurations is defined in the class of each specific algorithm.

export_settings() dict[source]

Export the configuration of the algorithm to an object to be loaded in other execution. :return: dict

property init
minimize(cost_function: callable, output_runtime: bool = True)[source]
  • cost_function – cost function to be optimized and accepts an array as argument.

  • output_runtime – true if information during runtime is desired.


EdaResult object

Return type


property pm

EDAspy.optimization.eda_result module

class EDAspy.optimization.eda_result.EdaResult(best_ind: array, best_cost: float, n_fev: int, history: list, settings: dict)[source]

Bases: object

Object used to encapsulate the result and information of the EDA during the execution

Module contents