Neuroevolution in deep neural networks
ACM SIGEVOlution2021Vol. 14(1), pp. 3–7
Citations Over TimeTop 20% of 2021 papers
Abstract
A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNNs). These methods play a crucial role in the success or failure of the DNNs for most problems. Evolutionary Algorithms are gaining momentum as a computationally feasible method for the automated optimisation of DNNs. Neuroevolution is a term that describes these processes. This newsletter article summarises the full version available at https://arxiv.org/abs/2006.05415.
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