paref.moo_algorithms.two_dimensional.scan_evenly_2d#

Classes

ScanEvenly2D([max_iter_minimizer, ...])

Initialize the algorithms hyperparameters
class paref.moo_algorithms.two_dimensional.scan_evenly_2d.ScanEvenly2D(max_iter_minimizer: int = 100, training_iter: int = 2000, learning_rate: float = 0.05, min_required_evaluations: int = 20, min_distance_to_evaluated_points: float = 0.02)[source]#

Bases: GPRMinimizer

Initialize the algorithms hyperparameters

Parameters:

  • max_iter_minimizer (int default 100) – maximum number of iterations of the differential evolution algorithm

  • training_iter (int default 2000) – maximum training iterations of the GPR(s)

  • learning_rate (float default 0.05) – learning rate of the training of the GPR(s)

  • min_required_evaluations (int default 20) – minimum number of evaluations required for the training (must be greater or equal than 20)

  • min_distance_to_evaluated_points (float default 2e-2) – required minimum distance to already evaluated points

property sequence_of_pareto_reflections#
property supported_codomain_dimensions: int#