paref.moo_algorithms.multi_dimensional.find_1_pareto_points#

Classes

Find1ParetoPoints([max_iter_minimizer, ...])

Find 1 Pareto points
class paref.moo_algorithms.multi_dimensional.find_1_pareto_points.Find1ParetoPoints(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

Find 1 Pareto points

Note

Use this algorithm if you want to find a 1 Pareto point (i.e. a minimum in some component) for each component, f.e. in order to estimate the dimension of the Pareto front.

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#