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Evol-AutoNN: Evolutionary Automation of Neural Network

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dc.contributor.author Sivapathasundaram, Janakan
dc.contributor.author Poravi, Guhanathan
dc.date.accessioned 2025-04-13T01:29:37Z
dc.date.available 2025-04-13T01:29:37Z
dc.date.issued 2022
dc.identifier.citation Sivapathasundaram, J. and Poravi, G. (2022) ‘Evol-AutoNN: Evolutionary Automation of Neural Network’, in 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON). 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON), pp. 1–6. Available at: https://doi.org/10.1109/SMARTGENCON56628.2022.10084184. en_US
dc.identifier.uri https://ieeexplore.ieee.org/document/10084184
dc.identifier.uri http://dlib.iit.ac.lk/xmlui/handle/123456789/2246
dc.description.abstract As the domain of data science keeps on growing it leads to the need for a tool that breaks the barriers for non-experts to get into the field of data science. In this paper, we introduce the project of automating the neural network with the Evolutionary algorithm in the name of Evol-AutoNN. Evol-AutoNN is a biomimicry approach to the prediction of tabular data using the well-known two biological concepts that came from nature. Biological Evaluation and the Neural Network that came from the human brain are the two concepts applied in this project. Biomimicry is a new procedure to do innovation that tries to give a strong solution to a human problem by mimicking the patterns identified in nature. It is developed to overcome the problem of finding the best initial setting for the neural network for a given problem by the user. Evol-AutoNN imitates the process followed in both natural selection and genetics. As such, this work entitles to be an important step in the automation of neural networks for tabular data. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Evolutionary Algorithm en_US
dc.subject Biomimetics en_US
dc.subject Neural Network en_US
dc.title Evol-AutoNN: Evolutionary Automation of Neural Network en_US
dc.type Article en_US


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