NeuroEvolution of Augmenting Topologies

What Does NeuroEvolution of Augmenting Topologies 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.

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Techopedia Explains NeuroEvolution of Augmenting Topologies

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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Margaret Rouse

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.