Package: jmetalpy
A paper introducing jMetalPy is available at:https://doi.org/10.1016/j.swevo.2019.100598Table of ContentsInstallationUsageFeaturesChangelogLicenseInstallationYou can install the latest version of jMetalPy withpip,pip install jmetalpy  # or "jmetalpy[distributed]"Notes on installing withpipjMetalPy includes features for parallel and distributed computing based onpySparkandDask.These (extra) dependencies arenotautomatically installed when runningpip, which only comprises the core functionality of the framework (enough for most users):pip install jmetalpyThis is the equivalent of running:pip install "jmetalpy[core]"Other supported commands are listed next:pip install "jmetalpy[dev]"  # Install requirements for developmentpip install "jmetalpy[distributed]"  # Install requirements for parallel/distributed computingpip install "jmetalpy[complete]"  # Install all requirementsHello, world! 👋Examples of configuring and running all the included algorithms are locatedin the documentation.fromjmetal.algorithm.multiobjectiveimportNSGAIIfromjmetal.operatorimportSBXCrossover,PolynomialMutationfromjmetal.problemimportZDT1fromjmetal.util.termination_criterionimportStoppingByEvaluationsproblem=ZDT1()algorithm=NSGAII(problem=problem,population_size=100,offspring_population_size=100,mutation=PolynomialMutation(probability=1.0/problem.number_of_variables,distribution_index=20),crossover=SBXCrossover(probability=1.0,distribution_index=20),termination_criterion=StoppingByEvaluations(max_evaluations=25000))algorithm.run()We can then proceed to explore the results:fromjmetal.util.solutionimportget_non_dominated_solutions,print_function_values_to_file,\print_variables_to_filefront=get_non_dominated_solutions(algorithm.get_result())# save to filesprint_function_values_to_file(front,'FUN.NSGAII.ZDT1')print_variables_to_file(front,'VAR.NSGAII.ZDT1')Or visualize the Pareto front approximation produced by the algorithm:fromjmetal.lab.visualizationimportPlotplot_front=Plot(title='Pareto front approximation',axis_labels=['x','y'])plot_front.plot(front,label='NSGAII-ZDT1',filename='NSGAII-ZDT1',format='png')FeaturesThe current release of jMetalPy (v1.5.7) contains the following components:Algorithms: local search, genetic algorithm, evolution strategy, simulated annealing, random search, NSGA-II, NSGA-III, SMPSO, OMOPSO, MOEA/D, MOEA/D-DRA, MOEA/D-IEpsilon, GDE3, SPEA2, HYPE, IBEA. Preference articulation-based algorithms (G-NSGA-II, G-GDE3, G-SPEA2, SMPSO/RP); Dynamic versions of NSGA-II, SMPSO, and GDE3.Parallel computing based on Apache Spark and Dask.Benchmark problems: ZDT1-6, DTLZ1-2, FDA, LZ09, LIR-CMOP, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).Encodings: real, binary, permutations.Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).Quality indicators: hypervolume, additive epsilon, GD, IGD.Pareto front approximation plotting in real-time, static or interactive.Experiment class for performing studies either alone or alongsidejMetal.Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.Changelog[v1.6.0] Refactor class Problem, the single-objective genetic algorithm can solve constrained problems, performance improvements in NSGA-II, generation of Latex tables summarizing the results of the Wilcoxon rank sum test, added a notebook folder with examples.[v1.5.7] Use of linters for catching errors and formatters to fix style, minor bug fixes.[v1.5.6] Removed warnings when using Python 3.8.[v1.5.5] Minor bug fixes.[v1.5.4] Refactored quality indicators to accept numpy array as input parameter.[v1.5.4] AddedCompositeSolutionclass to support mixed combinatorial problems.#69LicenseThis project is licensed under the terms of the MIT - see theLICENSEfile for details.