PyGAD: an intuitive genetic algorithm Python library

• Published in: Multimedia tools and applications Vol. 83; no. 20; pp. 58029 - 58042
• Main Author: Gad, Ahmed Fawzy
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2024; Springer Nature B.V
• Subjects: Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Genes; Genetic algorithms; Libraries; Multimedia; Multimedia Information Systems; Multiple objective analysis; Mutation; Optimization; Python; Special Purpose and Application-Based Systems; Deep learning; PyTorch; Genetic algorithm; Evolutionary algorithm; Keras; Optimization; NumPy; Python
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-17167-y

This paper introduces PyGAD, an open-source easy-to-use Python library for building the genetic algorithm (GA) and solving multi-objective optimization problems. PyGAD is designed as a general-purpose optimization library with the support of a wide range of parameters to give the user control over its life cycle. This includes, but not limited to, the population, fitness function, gene value space, gene data type, parent selection, crossover, and mutation. Its usage consists of 3 main steps: build the fitness function, create an instance of the pygad.GA class, and call the pygad.GA.run() method. The library supports training deep learning models created either with PyGAD itself or with frameworks such as Keras and PyTorch. Given its stable state, PyGAD is also in active development to respond to the user’s requested features and enhancements received on GitHub.