Components¶

Archive¶

class jmetal.component.archive.Archive¶

Bases: typing.Generic

add(solution: S) → bool¶

get(index: int) → S¶

get_name() → str¶

size() → int¶

class jmetal.component.archive.ArchiveWithReferencePoint(maximum_size: int, reference_point: typing.List[float], comparator: jmetal.component.comparator.Comparator[S], density_estimator: jmetal.component.density_estimator.DensityEstimator)¶

Bases: jmetal.component.archive.BoundedArchive

add(solution: S) → bool¶

get_reference_point() → typing.List[float]¶

class jmetal.component.archive.BoundedArchive(maximum_size: int, comparator: jmetal.component.comparator.Comparator[S] = None, density_estimator: jmetal.component.density_estimator.DensityEstimator = None)¶

Bases: jmetal.component.archive.Archive

add(solution: S) → bool¶

compute_density_estimator()¶

class jmetal.component.archive.CrowdingDistanceArchive(maximum_size: int)¶: Bases: jmetal.component.archive.BoundedArchive

class jmetal.component.archive.CrowdingDistanceArchiveWithReferencePoint(maximum_size: int, reference_point: typing.List[float])¶: Bases: jmetal.component.archive.ArchiveWithReferencePoint

class jmetal.component.archive.NonDominatedSolutionListArchive¶

Bases: jmetal.component.archive.Archive

add(solution: S) → bool¶

Comparator¶

class jmetal.component.comparator.Comparator¶

Bases: typing.Generic

compare(solution1: S, solution2: S) → int¶

get_name() → str¶

class jmetal.component.comparator.DominanceComparator(constraint_comparator=<jmetal.component.comparator.SolutionAttributeComparator object>)¶

Bases: jmetal.component.comparator.Comparator

compare(solution1: jmetal.core.solution.Solution, solution2: jmetal.core.solution.Solution) → int¶

class jmetal.component.comparator.EqualSolutionsComparator¶

Bases: jmetal.component.comparator.Comparator

compare(solution1: jmetal.core.solution.Solution, solution2: jmetal.core.solution.Solution) → int¶

class jmetal.component.comparator.RankingAndCrowdingDistanceComparator¶

Bases: jmetal.component.comparator.Comparator

compare(solution1: jmetal.core.solution.Solution, solution2: jmetal.core.solution.Solution) → int¶

class jmetal.component.comparator.SolutionAttributeComparator(key: str, lowest_is_best: bool = True)¶

Bases: jmetal.component.comparator.Comparator

compare(solution1: jmetal.core.solution.Solution, solution2: jmetal.core.solution.Solution) → int¶

Density Estimator¶

class jmetal.component.density_estimator.CrowdingDistance¶

Bases: jmetal.component.density_estimator.DensityEstimator

This class implements a DensityEstimator based on the crowding distance. In consequence, the main method of this class is compute_density_estimator().

compute_density_estimator(front: typing.List[S])¶

This function performs the computation of the crowding density estimation over the solution list.

Note

This method assign the distance in the inner elements of the solution list.

Parameters:	front – The list of solutions.

class jmetal.component.density_estimator.DensityEstimator¶

Bases: typing.List

This is the interface of any density estimator algorithm.

compute_density_estimator(solution_list: typing.List[S]) → float¶

jmetal.component.density_estimator.S = ~S¶

Evaluator¶

class jmetal.component.evaluator.Evaluator¶

Bases: typing.Generic

evaluate(solution_list: typing.List[S], problem: jmetal.core.problem.Problem) → typing.List[S]¶

static evaluate_solution(solution: S, problem: jmetal.core.problem.Problem) → None¶

get_name() → str¶

class jmetal.component.evaluator.MapEvaluator(processes=None)¶

Bases: jmetal.component.evaluator.Evaluator

evaluate(solution_list: typing.List[S], problem: jmetal.core.problem.Problem) → typing.List[S]¶

class jmetal.component.evaluator.SequentialEvaluator¶

Bases: jmetal.component.evaluator.Evaluator

evaluate(solution_list: typing.List[S], problem: jmetal.core.problem.Problem) → typing.List[S]¶

Observer¶

class jmetal.component.observer.BasicAlgorithmObserver(frequency: float = 1.0) → None¶

Bases: jmetal.core.observable.Observer

Show the number of evaluations, best fitness and computing time.

Parameters:	frequency – Display frequency.

update(*args, **kwargs)¶

class jmetal.component.observer.ProgressBarObserver(step: int, maximum: int, desc: str = 'Progress') → None¶

Bases: jmetal.core.observable.Observer

Show a smart progress meter with the number of evaluations and computing time.

Parameters:	step – Initial counter value. maximum – Number of expected iterations. desc – Prefix for the progressbar.

update(*args, **kwargs)¶

class jmetal.component.observer.VisualizerObserver(replace: bool = True) → None¶

Bases: jmetal.core.observable.Observer

update(*args, **kwargs)¶

class jmetal.component.observer.WriteFrontToFileObserver(output_directory) → None¶

Bases: jmetal.core.observable.Observer

Write function values of the front into files.

Parameters:	output_directory – Output directory. Each front will be saved on a file FUN.x.

update(*args, **kwargs)¶

jmetal.component.observer.jMetalPyLogger = <Logger jMetalPy (DEBUG)>¶

Quality indicator¶

class jmetal.component.quality_indicator.HyperVolume(reference_point: list)¶

Bases: jmetal.component.quality_indicator.Metric

Hypervolume computation based on variant 3 of the algorithm in the paper:

C. M. Fonseca, L. Paquete, and M. Lopez-Ibanez. An improved dimension-sweep algorithm for the hypervolume indicator. In IEEE Congress on Evolutionary Computation, pages 1157-1163, Vancouver, Canada, July 2006.

Minimization is implicitly assumed here!

Constructor.

compute(front: typing.List[jmetal.core.solution.Solution])¶

Before the HV computation, front and reference point are translated, so that the reference point is [0, …, 0].

Returns:	The hypervolume that is dominated by a non-dominated front.

get_name() → str¶

class jmetal.component.quality_indicator.Metric¶

Bases: object

compute(front: typing.List[jmetal.core.solution.Solution])¶

get_name() → str¶

class jmetal.component.quality_indicator.MultiList(number_lists)¶

Bases: object

A special front structure needed by FonsecaHyperVolume.

It consists of several doubly linked lists that share common nodes. So, every node has multiple predecessors and successors, one in every list.

Builds ‘numberLists’ doubly linked lists.

class Node(number_lists, cargo=None)¶: Bases: object

append(node, index)¶: Appends a node to the end of the list at the given index.

extend(nodes, index)¶: Extends the list at the given index with the nodes.

get_length(i)¶: Returns the length of the i-th list.

reinsert(node, index, bounds)¶: Inserts ‘node’ at the position it had in all lists in [0, ‘index’[ before it was removed. This method assumes that the next and previous nodes of the node that is reinserted are in the list.

remove(node, index, bounds)¶: Removes and returns ‘node’ from all lists in [0, ‘index’[.

Ranking¶

class jmetal.component.ranking.EfficientNonDominatedRanking¶

Bases: jmetal.component.ranking.Ranking

Class implementing the EDS (efficient non-dominated sorting) algorithm.

compute_ranking(solution_list: typing.List[S])¶

class jmetal.component.ranking.FastNonDominatedRanking¶

Bases: jmetal.component.ranking.Ranking

Class implementing the non-dominated ranking of NSGA-II.

compute_ranking(solution_list: typing.List[S])¶

class jmetal.component.ranking.Ranking¶

Bases: typing.List

compute_ranking(solution_list: typing.List[S])¶

get_number_of_subfronts()¶

get_subfront(rank: int)¶