NEO (Neural Evolution of Augmenting Topologies)

NeuroEvolution of Augmenting Topologies (NEO) is an algorithm that uses evolutionary principles to develop neural networks. Unlike traditional methods that fix a network's structure, NEO evolves both the network's connections and its architecture over time, improving its ability to perform tasks. It does this by simulating processes similar to natural selection—testing, selecting, mutating, and reproducing different network designs—ultimately discovering efficient and effective configurations without human intervention. This approach allows neural networks to adapt and optimize themselves for complex problems, making it useful for tasks like robotics, game playing, and autonomous systems.