NEAT (NeuroEvolution of Augmenting Topologies) is a genetic algorithm for evolving artificial neural networks. It starts with simple networks and incrementally adds complexity, evolving both network structure and connection weights to solve a given task. It is commonly used in reinforcement learning and robotics to automatically design controllers for agents.
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