All Classes and Interfaces
Class
Description
Class to configure and run an asynchronous cellular genetic algorithm to solve a
DoubleProblemClass to configure and run an asynchronous cellular genetic algorithm to solve a
DoubleProblemClass to configure and run an asynchronous cellular genetic algorithm to solve a
DoubleProblemMethod implementing a constrained velocity update.
Class that initializes the velocity of the particles to 0.0
Method implementing the standard velocity PSO update strategy
Interface representing entities that evaluate a list of solutions
Template for evolutionary algorithms.
This perturbation applies a mutation operator to a fixed set of solutions according to a
frequency parameter.
Class to configure and run a generational genetic algorithm to solve a
BinaryProblemClass to configure and run a generational genetic algorithm to solve a
DoubleProblemClass to configure and run a generational genetic algorithm to solve a
DoubleProblemClass to configure and run a generational genetic algorithm to solve a
DoubleProblemClass to configure and run a generational genetic algorithm to solve a
DoubleProblemClass to configure and build an instance of a genetic algorithm
Class to configure and run a genetic algorithm to solve an instance of the TSP
Class to configure and run a genetic algorithm to solve an instance of the TSP
Class to configure and run the NSGA-II algorithm using a
GDominanceComparator, which
allows to empower NSGA-II with a preference articulation mechanism based on reference point.Class to configure and run the NSGA-II algorithm using a
GDominanceComparator, which
allows to empower NSGA-II with a preference articulation mechanism based on reference point.Class to configure and run the SMSEMOA algorithm using g-dominance
TODO: comment the interface
Class to configure and run the NSGA-II algorithm configured with standard settings.
Class to configure and run the NSGA-II algorithm configured with standard settings.
Class to configure and build an instance of the MOEA/D algorithm
Class to configure and build an instance of the MOEA/D-DE algorithm
Class to configure and run the NSGA-II algorithm configured with standard settings.
Class to configure and run the NSGA-II algorithm configured with standard settings.
Class to configure and run the NSGA-II algorithm configured with standard settings.
Class to configure and run the NSGA-II algorithm configured with standard settings.
Class to configure and run the NSGA-II algorithm configured with standard settings.
(mu , lambda) replacement.
Class that evaluates a list of solutions using threads.
Class that evaluates a list of solutions using threads.
(mu + lambda) replacement.
This class produces a mating pool composed of solutions belonging to a neighborhood.
Class to configure and run the NSGA-II algorithm configured with standard settings for solving
a binary problem (
OneZeroMax is a multi-objective variant of OneMax).Class to configure and run the NSGA-II algorithm to solve a bi-objective TSP.
Class to configure and run the NSGA-II algorithm to solve a bi-objective TSP.
Class to configure and build an instance of the NSGA-II algorithm
Class to configure and build an instance of the NSGA-II algorithm using DE operators
Class to configure and run the NSGA-II algorithm configured with standard settings.
Class to configure and run the NSGA-II-DE algorithm showing the population while the algorithm is
running
Class to configure and run the NSGA-II algorithm showing the population while the algorithm is running
Class to configure and run the NSGA-II algorithm configured with standard settings for solving
a binary problem (
OneZeroMax is a multi-objective variant of OneMax).Class to configure and run the NSGA-II algorithm to solve a bi-objective TSP.
Class to configure and run the NSGA-II algorithm showing the population while the algorithm is
running
Class to configure and run the NSGA-II algorithm showing the population while the algorithm is
running
Class to configure and run the NSGA-II algorithm configured with standard settings for solving a
binary problem (
OneZeroMax is a multi-objective variant of OneMax).Class to configure a steady-state version of NSGA-II
Class to configure a steady-state version of NSGA-II, showing the current population during
the execution of the algorithm
Class to configure and run the NSGA-II algorithm with a stopping condition based on finding
a Pareto front approximation having a hypervolume value higher than the 95% of the hypervolume
of the reference front.
Class to configure and run the NSGA-II algorithm with a stopping condition based on pressing a key.
Class to configure and run the NSGA-II algorithm with a stopping condition based a maximum
computing time.
Class to configure and run the NSGA-II algorithm configured a bounded external archive that uses
the crowding distance to remove solutions when the archive gets full.
Class to configure and run the NSGA-II algorithm showing the population while the algorithm is running
Class to configure and run the NSGA-II algorithm to solve a problem having a mixed-encoding.
Class to configure and run the NSGA-II algorithm configured with the ranking method known as
Merge non-dominated sorting ranking (DOI: https://doi.org/10.1109/TCYB.2020.2968301)
Class to configure and run the NSGA-II algorithm showing the population while the algorithm is
running
Class to configure and run the NSGA-II algorithm to solve a bi-objective problem and plotting
the result front with Smile (https://haifengl.github.io/)
Class to configure and run the NSGA-II algorithm to solve a three-objective problem and plotting
the result front with Smile (https://haifengl.github.io/)
Class to configure and run the NSGA-II algorithm showing the population while the algorithm is
running
Class to configure and run the NSGA-II algorithm using an unbounded archive that stores the
non-dominated solutions found during the search.
Given two populations of equal size, the returned population is composed of the result of the
pairwise comparison between the solutions of both populations.
Class to configure and run the NSGA-II algorithm using the
MultiThreadedEvaluation evaluator.Template for particle swarm optimization algorithms.
This class allows to select N different solutions that can be taken from a solution list (i.e, population or swarm) or
from a neighborhood according to a given probability.
Class representing a random search algorithm.
Class to configure and run the a random search.
Randomly select a number of solutions from a list, with or without replacement
Class that creates a list of randomly instantiated solutions.
A preference is a list composed of a ranking and a density estimator that specifies preferences
in the selection and replacement components of an evolutionary algorithm.
Class to configure and build an instance of a MOEA algorithm based on ranking and crowding distance components (RDS)
for replacement and selection
Class that evaluates a list of solutions sequentially.
Given an offspring population composed of a single solution, this solution is compared against a particular solution
of the population given by a
SequenceGenerator object.Class to configure and build an instance of the SMPSO algorithm
Class for configuring and running the SMPSO algorithm
Class for configuring and running the SMPSO algorithm
Class for configuring and running the SMPSO algorithm
Class for configuring and running the SMPSO algorithm
Class to configure and build an instance of the SMS-EMOA algorithm
Class to configure and build an instance of the SMS-EMOA algorithm
Class to configure and run the SMSEMOA algorithm
Class to configure and run the NSGA-II-DE algorithm showing the population while the algorithm is
running
Class to configure and run the NSGA-II-DE algorithm showing the population while the algorithm is
running
Class to configure and run the SMSEMOA algorithm
Class to configure and run the SMSEMOA algorithm
Interface representing entities that create a list of solutions applying some strategy (e.g, random)
Method implementing a velocity update strategy proposed in Standard PSO 2011.
Class that initializes the velocity of the particles according to the standard PSO 2007 (SPSO 2007)
Source: Maurice Clerc.
Class that initializes the velocity of the particles according to the standard PSO 2011 (SPSO 2011)
Source: Maurice Clerc.
Class to configure and run a steady-stat genetic algorithm to solve a
DoubleProblemClass to configure and run a synchronous cellular genetic algorithm to solve a
DoubleProblemThis interface represents classes that check the termination condition of an algorithm.
Class that allows to check the termination condition when the computing time of an algorithm
gets higher than a given threshold.
Class that allows to check the termination condition based on a maximum number of indicated
evaluations.
Class that allows to check the termination condition based on introducing a character by keyboard.
Class that allows to check the termination condition when current front is above a given
percentage of the value of a quality indicator applied to a reference front.
TODO: description missing
Interface representing velocity update strategies