Reprint File

Heidenreich, Hunter. “Neat: An Awesome Approach to Neuroevolution.” Medium, Towards Data Science, 10 Jan. 2019, https://towardsdatascience.com/neat-an-awesome-approach-to-neuroevolution-3eca5cc7930f. Stanley, Kenneth O., and Risto Miikkulainen. “Evolving Neural Networks through Augmenting Topologies.” Evolutionary Computation, vol. 10, no. 2, 2002, pp. 99–127., https://doi.org/10.1162/106365602320169811.
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Research Plan

Name Samuel Jones School Satellite High School Category Mathematics & Computational Science Research Teacher Mrs. Molledo ISEF Form Link 1 Placeholder 1A Placeholder 1B Placeholder NeuralNetwork Implementing Mutating Topology Question or Problem Being Addressed: Reducing inefficiency during training of NEAT (NeuroEvolution of Augmenting Topologies) models and reducing complexity of NEAT models after training for better performance without sacrificing accuracy. Engineering Goals: Create a machine learning model that implements some traits of neural networks, such as optimization procedures, while also utilizing the mutable topology found in NEAT.
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