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[D] Neuroevolution and Deep Learning Comparison

Does anyone have a benchmark/comparison of a neuroevolution algorithm for machine learning such as NEAT and a more traditional deep learning approach? Preferably an interactive one that features many different kinds of tasks and tracks the performance of different sizes of models as they train. I have done some experiments with genetic algorithms in the past and would like to see if they provide any long-term advantages over gradient-descent based methods at any type of problem. Any help would be appreciated. (Repost because forgot to tag post the first time.)

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