NEAT vs Reinforcement Learning

Question

As far as I know, NEAT (Neuroevolution of Complementary Topologies) is an algorithm that uses the concept of evolution to train a neural network. On the other hand, reinforcement learning is a type of machine learning with the concept of “rewarding” more successful nodes.

What is the difference between these two fields as they seem to be very similar? Or is NEAT derived from reinforcement training?

Answer 1

In short, they have nothing in common.

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