How does NeuroEvolution of Augmenting Topologies contribute to genetic machine learning?

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NeuroEvolution of Augmenting Topologies (NEAT) contributes to genetic machine learning by providing a cutting-edge innovative model based on the principles of genetic algorithms that help to optimize networks according to both the weights and the structures of a network.

Genetic algorithms in general are artificial intelligence and machine learning models that are in some way based upon the principle of natural selection – models that work by iterative processing of that principle of selecting the best result for a given need. These are part of a broader category of "evolutionary algorithms" in what professionals called the "evolutionist school" of machine learning – one that is highly structured around biological evolutionary principles.

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The NeuroEvolution of Augmenting Topologies network is a Topology and Weight Evolving Artificial Neural Network (TWEAN) – it optimizes both the network topology and the weighted inputs of the network – subsequent versions and features of NEAT have helped to adapt this general principle to specific uses, including video game content creation and planning of robotic systems.

With tools like NeuroEvolution of Augmenting Topologies, artificial neural networks and similar technologies can involve in some of the same ways that biological life has evolved on the planet – however, the technologies can generally evolve very quickly and in many sophisticated ways.

Resources like a NeuroEvolution of Augmenting Topologies users group, a software FAQ and other elements can help build a fuller understanding of how NEAT works and what it means in the context of evolutionary machine learning. Essentially, by streamlining the structure of a network and changing input weights, NEAT can get human handlers of machine learning systems closer to their goals, while eliminating a lot of the human labor involved in setup. Traditionally, with simple feedforward neural networks and other early models, the structuring and setting of weighted inputs relied on human training. Now, it is automated with these systems to a high degree.

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Justin Stoltzfus
Justin Stoltzfus

Justin Stoltzfus is an independent blogger and business consultant assisting a range of businesses in developing media solutions for new campaigns and ongoing operations. He is a graduate of James Madison University.Stoltzfus spent several years as a staffer at the Intelligencer Journal in Lancaster, Penn., before the merger of the city’s two daily newspapers in 2007. He also reported for the twin weekly newspapers in the area, the Ephrata Review and the Lititz Record.More recently, he has cultivated connections with various companies as an independent consultant, writer and trainer, collecting bylines in print and Web publications, and establishing a reputation…