  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.
