paref.moo_algorithms.multi_dimensional.min_g

Functions

calculate_optimal_scaling_g(fun, g, ...[, ...])
calculate_optimal_scaling_x(fun, ...[, ...])

Classes

MinG([max_iter_minimizer, training_iter, ...])

Initialize the algorithms hyperparameters

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

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

apply_moo_operation(blackbox_function: BlackboxFunction) → None

Apply moo operation constructed as above

Parameters:

blackbox_function (BlackboxFunction) – blackbox function to which algorithm is applied

abstract property sequence_of_pareto_reflections

property supported_codomain_dimensions: None

paref.moo_algorithms.multi_dimensional.min_g.calculate_optimal_scaling_g(fun, g, blackbox_function, max_iter_minimizer: int = 500)

paref.moo_algorithms.multi_dimensional.min_g.calculate_optimal_scaling_x(fun, blackbox_function, max_iter_minimizer: int = 500)