NEAT, or NeuroEvolution of Augmenting Topologies, is an evolutionary algorithm designed to generate artificial neural networks. It’s distinctive for its method of evolving these networks by starting simple and gradually increasing complexity through mutations and crossovers. This approach can add new neurons or connections over generations, which allows the network to become increasingly complex and better at solving the tasks it’s trained on

One of the main challenges NEAT addresses is the competing conventions problem, where different genetic representations might produce the same functionality. NEAT solves this by assigning a unique innovation number to new mutations, enabling a consistent historical tracking of gene evolution. This innovation number system facilitates more effective crossover operations between genomes by matching corresponding genes​.

NEAT has been extended in various ways to suit different applications. For instance, rtNEAT allows for real-time evolution in dynamic environments, making it ideal for applications like video games or robotic control. HyperNEAT, another extension, is designed to evolve large-scale neural networks, leveraging geometric patterns for more complex tasks​


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