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For questions related to neuroevolution (or neuro-evolution) techniques, such as NEAT, that are used to evolve (or train) artificial neural networks (that is, they are used evolve their parameters or topology), inspired by the natural evolution. A neuroevolution algorithm is thus an evolutionary algorithm where the genomes (individuals or chromosomes) are artificial neural networks.

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How can I automate the choice of the architecture of a neural network for an arbitrary problem?

Assume that I want to solve an issue with a neural network that either I can't fit to existing architectures (perceptron, Konohen, etc) or I'm simply not aware of the existence of those or I'm unable …
Zoltán Schmidt's user avatar