flxsosa/DeepHyperNEAT
A public python implementation of the DeepHyperNEAT system for evolving neural networks. Developed by Felix Sosa and Kenneth Stanley. See paper here: https://eplex.cs.ucf.edu/papers/sosa_ugrad_report18.pdf
This is a specialized tool for researchers and advanced practitioners in artificial intelligence and machine learning who want to automatically design complex neural network architectures. It takes high-level task definitions and evolutionary parameters, and outputs optimized neural network structures capable of solving those tasks, along with visualizations. The primary user would be an AI/ML researcher or an evolutionary computation engineer.
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Use this if you need to evolve both the architecture and the depth of deep neural networks for a given computational task, rather than manually designing them.
Not ideal if you are looking for a plug-and-play machine learning library or a tool for general data analysis, as it requires a deep understanding of neuroevolutionary algorithms.
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77
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16
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 13, 2022
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