Python version of the jMetal framework
jMetalPy: Python version of the jMetal framework
A paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598
Table of Contents
You can install the latest version of jMetalPy with
including parallel and distributed computing dependencies such as pySpark and Dask:
pip install "jmetalpy[complete]"
You can also install the core functionality of the framework (which is often enough for most users):
pip install "jmetalpy[core]"
pip install jmetalpy
Examples of configuring and running all the included algorithms are located in the documentation.
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.
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