From Genes to Neural Networks: Understanding and Building NEAT (Neuro-Evolution of Augmenting Topologies) fromScratch

The article provides a comprehensive guide to implementing NEAT (Neuro-Evolution of Augmenting Topologies), a pioneering neuroevolution algorithm that evolves neural network architectures alongside weights, enabling the automatic discovery of optimal network topologies. It details the core innovations of NEAT, such as speciation, incremental growth of networks, and genetic encoding, offering a step-by-step code walkthrough to facilitate practical reproduction and understanding of the algorithm from scratch.