NeuroEvolution of Augmenting Topologies (NEAT)

Last Updated: March 25, 2020

Definition - What does NeuroEvolution of Augmenting Topologies (NEAT) mean?

NeuroEvolution of Augmenting Topologies or NEAT is often described as a genetic solution for improving neural networks. The NEAT concept can be used to provide a new model for selecting typologies for a neural network and for initializing weights.

Techopedia explains NeuroEvolution of Augmenting Topologies (NEAT)

The idea with NEAT is that networks start small and simple, and then complexify as they evolve. Specifically, NEAT alters the weighting parameters and the network structure, and uses the principle of species evolution to promote innovation in the process.

Experts point out that as an automation procedure, NEAT provides a radically new way of defining topology, since traditionally, engineers who utilized a particular topology in the network simply learned about input weights through training data.

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