Metadata-Version: 2.1
Name: deatf
Version: 0.1
Summary: Distributed Evolutionary Algorithms in TensorFlow (DEATF) is a framework where networks generated with TensorFlow are evolved via DEAP.
Home-page: https://github.com/IvanHCenalmor/deatf
Author: Ivan Hidalgo
Author-email: ivanhcenalmor@domain.com
License: MIT
Keywords: NEUROEVOLUTION,DEAP,TENSORFLOW,GENETIC,ALGORITHMS
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# DEATF

[![Python](https://img.shields.io/pypi/pyversions/tensorflow.svg?style=plastic)](https://badge.fury.io/py/tensorflow)
[![TensorFlow](https://img.shields.io/badge/TensorFlow-2.5-green)](https://www.tensorflow.org/)
[![DEAP](https://img.shields.io/badge/DEAP-1.0-brightgreen)](https://deap.readthedocs.io/en/master/)

Distributed Evolutionary Algorithms in TensorFlow (DEATF) is a framework where networks generated with <a href="https://www.tensorflow.org/">TensorFlow</a> [[1]](#1) are evolved via <a href="deap.readthedocs.org/">DEAP</a> [[2]](#2). DEATF is a framework directly based in <a href="https://github.com/unaigarciarena/EvoFlow">EvoFlow</a> [[3]](#3) framework created by Unai Garciarena.

<p align="left">
<a href="https://github.com/deap/deap"><img src="https://repository-images.githubusercontent.com/20035587/2559bd00-9a75-11e9-9686-0697d18522cf" height=250 align="right" /></a>
<a href="https://www.tensorflow.org/"><img src="https://upload.wikimedia.org/wikipedia/commons/2/2d/Tensorflow_logo.svg" height=250 align="left" /></a>
</p>

## Installation

## Requirements

## Example

## References
<a id="1">[1]</a> 
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., ... & Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467.

<a id="2">[2]</a> 
Fortin, F. A., Rainville, F. M. D., Gardner, M. A., Parizeau, M., & Gagné, C. (2012). DEAP: Evolutionary algorithms made easy. Journal of Machine Learning Research, 13(Jul), 2171-2175.

<a id="3">[3]</a> 
Garciarena, U., Santana, R., & Mendiburu, A. (2018, July). Evolved GANs for generating Pareto set approximations. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 434-441). ACM.

