paref.moo_algorithms.multi_dimensional.find_edge_points

Classes

FindEdgePoints([max_iter_minimizer, ...])

Find edge points of Pareto front

class paref.moo_algorithms.multi_dimensional.find_edge_points.FindEdgePoints(max_iter_minimizer: int = 500, training_iter: int = 2000, learning_rate: float = 0.05, min_distance_to_evaluated_points: float = 0.02)

Bases: GPRMinimizer

Find edge points of Pareto front

Use this algorithm if you want to find the edge points of the Pareto front.

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