NeuroEvolution of Augmenting Topologies (NEAT) is a kind of Genetic Algorithm for the generation of developing artificial neural networks. It is a neuroevolution technique that alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. Reinforcement learning is about agents, learning policies to perform well in the environment. Thus they solve a different, more complex problems. Theoretically, you could learn NEAT using RL, as you might pose the problem of "given a neural network as a state, learn how to modify it over time to get better performance".