A paper introducing JMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598

• Installation
• Usage
• Features
• License

To download jMetalPy just clone the Git repository hosted in GitHub:

git clone https://github.com/jMetal/jMetalPy.git
python setup.py install

Alternatively, you can install it with pip:

pip install jmetalpy

Examples of configuring and running all the included algorithms are located in the examples folder.

The current release of jMetalPy (v1.5.2) 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 plotting for problems with two or more objectives (as scatter plot/parallel coordinates/chordplot) in real-time, static or interactive.
• Experiment class for performing studies either alone or alongside jMetal.
• Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.

This project is licensed under the terms of the MIT - see the LICENSE file for details.