| Name | jmetalpy JSON |
| Version |
1.7.0
JSON |
| download |
| home_page | https://github.com/jMetal/jMetalPy |
| Summary | Python version of the jMetal framework |
| upload_time | 2024-06-21 07:54:15 |
| maintainer | Antonio J. Nebro, Antonio Benitez-Hidalgo |
| docs_url | None |
| author | Antonio J. Nebro |
| requires_python | >=3.7 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml)
[]()
[](https://doi.org/10.1016/j.swevo.2019.100598)
[]()
[](https://github.com/psf/black)
A paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598
### Table of Contents
- [Installation](#installation)
- [Usage](#hello-world-)
- [Features](#features)
- [Changelog](#changelog)
- [License](#license)
## Installation
You can install the latest version of jMetalPy with `pip`,
```console
pip install jmetalpy # or "jmetalpy[distributed]"
```
<details><summary><b>Notes on installing with <tt>pip</tt></b></summary>
<p>
jMetalPy includes features for parallel and distributed computing based on [pySpark](https://spark.apache.org/docs/latest/api/python/index.html) and [Dask](https://dask.org/).
These (extra) dependencies are *not* automatically installed when running `pip`, which only comprises the core functionality of the framework (enough for most users):
```console
pip install jmetalpy
```
This is the equivalent of running:
```console
pip install "jmetalpy[core]"
```
Other supported commands are listed next:
```console
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
```
</p>
</details>
## Hello, world! 👋
Examples of configuring and running all the included algorithms are located [in the documentation](https://jmetal.github.io/jMetalPy/multiobjective.algorithms.html).
```python
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:
```python
from jmetal.util.solution import get_non_dominated_solutions, print_function_values_to_file,
print_variables_to_file
front = get_non_dominated_solutions(algorithm.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:
```python
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')
```
<img src=docs/source/_static/NSGAII-ZDT1.png width=450 alt="Pareto front approximation">
## Features
The current release of jMetalPy (v1.7.0) 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, RWA, 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](https://github.com/jMetal/jMetal).
* Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.
|  |  |
|-------------- | ---------------- |
|  |  |
## Changelog
* [v1.7.0] Add RWA benchmark, refactor classes BinarySolution and BinaryProblem.
* [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](https://github.com/jMetal/jMetalPy/blob/master/jmetal/core/solution.py#L111) class to support mixed combinatorial problems. [#69](https://github.com/jMetal/jMetalPy/issues/69)
## License
This project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.
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"description": "\n\n[](https://github.com/jMetal/jMetalPy/actions/workflows/ci.yml)\n[]()\n[](https://doi.org/10.1016/j.swevo.2019.100598)\n[]()\n[](https://github.com/psf/black)\n\nA paper introducing jMetalPy is available at: https://doi.org/10.1016/j.swevo.2019.100598\n\n### Table of Contents\n- [Installation](#installation)\n- [Usage](#hello-world-)\n- [Features](#features)\n- [Changelog](#changelog)\n- [License](#license)\n\n## Installation\n\nYou can install the latest version of jMetalPy with `pip`, \n\n```console\npip install jmetalpy # or \"jmetalpy[distributed]\"\n```\n\n<details><summary><b>Notes on installing with <tt>pip</tt></b></summary>\n<p>\n\njMetalPy includes features for parallel and distributed computing based on [pySpark](https://spark.apache.org/docs/latest/api/python/index.html) and [Dask](https://dask.org/).\n\nThese (extra) dependencies are *not* automatically installed when running `pip`, which only comprises the core functionality of the framework (enough for most users):\n\n```console\npip install jmetalpy\n```\n\nThis is the equivalent of running: \n\n```console\npip install \"jmetalpy[core]\"\n```\n\nOther supported commands are listed next:\n\n```console\npip install \"jmetalpy[dev]\" # Install requirements for development\npip install \"jmetalpy[distributed]\" # Install requirements for parallel/distributed computing\npip install \"jmetalpy[complete]\" # Install all requirements\n```\n\n</p>\n</details>\n\n## Hello, world! \ud83d\udc4b\n\nExamples of configuring and running all the included algorithms are located [in the documentation](https://jmetal.github.io/jMetalPy/multiobjective.algorithms.html).\n\n```python\nfrom jmetal.algorithm.multiobjective import NSGAII\nfrom jmetal.operator import SBXCrossover, PolynomialMutation\nfrom jmetal.problem import ZDT1\nfrom jmetal.util.termination_criterion import StoppingByEvaluations\n\nproblem = ZDT1()\n\nalgorithm = NSGAII(\n problem=problem,\n population_size=100,\n offspring_population_size=100,\n mutation=PolynomialMutation(probability=1.0 / problem.number_of_variables(), distribution_index=20),\n crossover=SBXCrossover(probability=1.0, distribution_index=20),\n termination_criterion=StoppingByEvaluations(max_evaluations=25000)\n)\n\nalgorithm.run()\n```\n\nWe can then proceed to explore the results:\n\n```python\nfrom jmetal.util.solution import get_non_dominated_solutions, print_function_values_to_file,\n print_variables_to_file\n\nfront = get_non_dominated_solutions(algorithm.result())\n\n# save to files\nprint_function_values_to_file(front, 'FUN.NSGAII.ZDT1')\nprint_variables_to_file(front, 'VAR.NSGAII.ZDT1')\n```\n\nOr visualize the Pareto front approximation produced by the algorithm:\n\n```python\nfrom jmetal.lab.visualization import Plot\n\nplot_front = Plot(title='Pareto front approximation', axis_labels=['x', 'y'])\nplot_front.plot(front, label='NSGAII-ZDT1', filename='NSGAII-ZDT1', format='png')\n```\n\n<img src=docs/source/_static/NSGAII-ZDT1.png width=450 alt=\"Pareto front approximation\">\n\n## Features\nThe current release of jMetalPy (v1.7.0) contains the following components:\n\n* 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.\n* Parallel computing based on Apache Spark and Dask.\n* Benchmark problems: ZDT1-6, DTLZ1-2, FDA, LZ09, LIR-CMOP, RWA, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).\n* Encodings: real, binary, permutations.\n* Operators: selection (binary tournament, ranking and crowding distance, random, nary random, best solution), crossover (single-point, SBX), mutation (bit-blip, polynomial, uniform, random).\n* Quality indicators: hypervolume, additive epsilon, GD, IGD.\n* Pareto front approximation plotting in real-time, static or interactive.\n* Experiment class for performing studies either alone or alongside [jMetal](https://github.com/jMetal/jMetal).\n* Pairwise and multiple hypothesis testing for statistical analysis, including several frequentist and Bayesian testing methods, critical distance plots and posterior diagrams.\n\n|  |  |\n|-------------- | ---------------- |\n|  |  |\n\n## Changelog\n\n* [v1.7.0] Add RWA benchmark, refactor classes BinarySolution and BinaryProblem.\n* [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.\n* [v1.5.7] Use of linters for catching errors and formatters to fix style, minor bug fixes.\n* [v1.5.6] Removed warnings when using Python 3.8.\n* [v1.5.5] Minor bug fixes.\n* [v1.5.4] Refactored quality indicators to accept numpy array as input parameter.\n* [v1.5.4] Added [CompositeSolution](https://github.com/jMetal/jMetalPy/blob/master/jmetal/core/solution.py#L111) class to support mixed combinatorial problems. [#69](https://github.com/jMetal/jMetalPy/issues/69)\n\n## License\n\nThis project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.\n",
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