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I never understood the appeal of NEAT. It's easy to conceive of mutation operators on fully connected layers instead of graphs of neurons and evaluate a lot fas
by nurettin 2y ago
I never understood the appeal of NEAT. It's easy to conceive of mutation operators on fully connected layers instead of graphs of neurons and evaluate a lot faster. NEAT also seems to have at least a dozen hyperparameters.
- Rhapso 2y agoIt mostly manages its hyperparameters itself. There is a reward function, one of the hyperparameters we do have to set, for condensing functionality into topology instead of smearing it obscurely across laters. You can just look at a NEAT network analytically and know what is going on there.