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

### Table of Contents

## Installation

You can install the latest version of jMetalPy with `pip`

,

`pip install jmetalpy # or "jmetalpy[distributed]"`

**Notes on installing with **`pip`

`pip`

jMetalPy includes features for parallel and distributed computing based on pySpark and Dask.

These (extra) dependencies are *not* automatically installed when running `pip`

, which only comprises the core functionality of the framework (enough for most users):

`pip install jmetalpy`

This is the equivalent of running:

`pip install "jmetalpy[core]"`

Other supported commands are listed next:

```
pip install "jmetalpy[dev]" # Install requirements for development
pip install "jmetalpy[distributed]" # Install requirements for parallel/distributed computing
pip install "jmetalpy[complete]" # Install all requirements
```

##
👋

Hello, world! Examples of configuring and running all the included algorithms are located in the documentation.

```
from jmetal.algorithm.multiobjective import NSGAII
from jmetal.operator import SBXCrossover, PolynomialMutation
from jmetal.problem import ZDT1
from jmetal.util.termination_criterion import StoppingByEvaluations
problem = 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:

```
from jmetal.util.solution import get_non_dominated_solutions, print_function_values_to_file, \
print_variables_to_file
front = get_non_dominated_solutions(algorithm.get_result())
# save to files
print_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:

```
from jmetal.lab.visualization import Plot
plot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])
plot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png')
```

## Features

The 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 alongside jMetal.
- 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] Added CompositeSolution class to support mixed combinatorial problems. #69

## License

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