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This paper presents a new method called NeuroEvolution of Augmenting Topologies (NEAT) that evolves both the connection weights and topology of neural networks. NEAT is shown to outperform other methods on a reinforcement learning benchmark by incrementally growing complexity and protecting innovative topologies. The paper claims NEAT is more efficient because it employs principled crossover of topologies, protects innovations, and grows from minimal structures.

Author

Kashinath

Language

EN