paref.moo_algorithms.multi_dimensional.fill_gaps_of_pareto_front#

Classes

FillGapsOfParetoFront([max_iter_minimizer, ...])

Fill gaps of Pareto front
class paref.moo_algorithms.multi_dimensional.fill_gaps_of_pareto_front.FillGapsOfParetoFront(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

Fill gaps of Pareto front

Note

Use this algorithm if you want to fill the gaps between the currently found Pareto front by Pareto points (Pareto front of the evaluations) which are closest to the center.

Examples

# TBA: add

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: SequenceParetoReflections#
property supported_codomain_dimensions: None#