PyGAD: An Intuitive Genetic Algorithm Python Library

TL;DR

PyGAD is a user-friendly, customizable Python library for genetic algorithms that supports deep learning model training and offers extensive parameter control for optimization tasks.

Contribution

Introduces PyGAD, a flexible, open-source Python library for genetic algorithms with extensive customization and deep learning integration capabilities.

Findings

Supports training with Keras and PyTorch

Offers extensive parameter customization

Active development with user feedback

Abstract

This paper introduces PyGAD, an open-source easy-to-use Python library for building the genetic algorithm. PyGAD supports a wide range of parameters to give the user control over everything in its life cycle. This includes, but is not limited to, population, gene value range, gene data type, parent selection, crossover, and mutation. PyGAD is designed as a general-purpose optimization library that allows the user to customize the fitness function. Its usage consists of 3 main steps: build the fitness function, create an instance of the pygad.GA class, and calling the pygad.GA.run() method. The library supports training deep learning models created either with PyGAD itself or with frameworks like Keras and PyTorch. Given its stable state, PyGAD is also in active development to respond to the user's requested features and enhancement received on GitHub https://github.com/ahmedfgad/GeneticAlgorithmPython. PyGAD comes with documentation https://pygad.readthedocs.io for further details and examples.