# PyGAD: An Intuitive Genetic Algorithm Python Library

arXiv: https://arxiv.org/abs/2106.06158 · HTML: https://mlsanity.com/paper/2106.06158/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 53 references that are in the map.
- First posted on arXiv 11 Jun 2021.

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## Abstract (arXiv)

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.