方法证据记录
NEAT
NEAT is a genetic algorithm for evolving artificial neural networks introduced by Kenneth Stanley and Risto Miikkulainen in 2002. Unlike methods that evolve weights alone, NEAT simultaneously evolves both the topology (structure) and the connection weights of neural networks. It achieves this through a direct genome encoding with historical markings that enable meaningful crossover between networks of different structures, making it applicable to reinforcement learning, game playing, and control tasks without requiring a predefined architecture.
源记录
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NeuroEvolution of Augmenting Topologies (NEAT)
分类方法记录 · ml-model / deep-learning
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