{"id":6565,"date":"2023-06-29T11:28:44","date_gmt":"2023-06-29T19:28:44","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=6565"},"modified":"2025-04-24T17:15:16","modified_gmt":"2025-04-24T17:15:16","slug":"train-neural-networks-using-a-genetic-algorithm-in-python-with-pygad","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/train-neural-networks-using-a-genetic-algorithm-in-python-with-pygad\/","title":{"rendered":"Train Neural Networks Using a Genetic Algorithm in Python with PyGAD"},"content":{"rendered":"\n
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Photo by Alexander Popov<\/a> on Unsplash<\/a><\/figcaption><\/figure>\n<\/div>\n
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The genetic algorithm (GA) is a biologically-inspired optimization algorithm. It has in recent years gained importance, as it\u2019s simple while also solving complex problems like travel route optimization, training machine learning algorithms, working with single and multi-objective problems, game playing, and more.<\/p>\n

Deep neural networks are inspired by the idea of how the biological brain works. It\u2019s a universal function approximator, which is capable of simulating any function, and is now used to solve the most complex problems in machine learning. What\u2019s more, they\u2019re able to work with all types of data (images, audio, video, and text).<\/p>\n

Both genetic algorithms (GAs) and neural networks (NNs) are similar, as both are biologically-inspired techniques. This similarity motivates us to create a hybrid of both to see whether a GA can train NNs with high accuracy.<\/p>\n

This tutorial uses PyGAD<\/a>, a Python library that supports building and training NNs using a GA. PyGAD<\/a> offers both classification and regression NNs. Before starting, install PyGAD<\/a> using pip<\/code>.<\/p>\n

This is the outline:<\/p>\n