Metadata-Version: 1.2
Name: jmetalpy
Version: 0.5.1
Summary: JMetalPy. Python version of the jMetal framework
Home-page: https://github.com/jMetal/jMetalPy
Author: Antonio J. Nebro
Author-email: antonio@lcc.uma.es
Maintainer: Antonio J. Nebro, Antonio Benítez-Hidalgo
Maintainer-email: antonio@lcc.uma.es, antonio.b@uma.es
License: MIT
Description: <p align="center">
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          <img src=docs/source/jmetalpy.png alt="jMetalPy">
          <br/>
        </p>
        
        # jMetalPy: Python version of the jMetal framework
        [![Build Status](https://img.shields.io/travis/jMetal/jMetalPy.svg?style=flat-square)](https://travis-ci.org/jMetal/jMetalPy)
        [![Read the Docs](https://img.shields.io/readthedocs/jmetalpy.svg?style=flat-square)](https://readthedocs.org/projects/jmetalpy/)
        [![PyPI License](https://img.shields.io/pypi/l/jMetalPy.svg?style=flat-square)]()
        [![PyPI Python version](https://img.shields.io/pypi/pyversions/jMetalPy.svg?style=flat-square)]()
        
        ## Table of Contents
        - [Installation](#installation)
        - [Usage](#usage)
        - [Features](#features)
        - [License](#license)
        
        ## Installation
        To download jMetalPy just clone the Git repository hosted in GitHub:
        ```bash
        $ git clone https://github.com/jMetal/jMetalPy.git
        $ python setup.py install
        ```
        
        Alternatively, you can install it with `pip`:
        ```bash
        $ pip install jmetalpy
        ```
        
        ## Usage
        Examples of configuring and running all the included algorithms are located [in the docs](https://jmetalpy.readthedocs.io/en/latest/examples.html).
        
        ## Features
        The current release of jMetalPy (v0.5.1) contains the following components:
        
        * Algorithms: random search, NSGA-II, SMPSO, SMPSO/RP.
        * Benchmark problems: ZDT1-6, DTLZ1-2, unconstrained (Kursawe, Fonseca, Schaffer, Viennet2), constrained (Srinivas, Tanaka).
        * Encodings: real, binary.
        * 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.
        * Density estimator: crowding distance.
        * Graphics: Pareto front plotting (2 or more objectives).
        * Laboratory: Experiment class for performing studies.
        
        <p align="center">
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          <img src=docs/source/visualization.png alt="Visualization">
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        </p>
        
        ## License
        This project is licensed under the terms of the MIT - see the [LICENSE](LICENSE) file for details.
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Programming Language :: Python :: 3.6
