There are improvements within the GA field that try to approximate gradients or do other trickery to create a "feedback loop to improve the speed of the walk" w

There are improvements within the GA field that try to approximate gradients or do other trickery to create a "feedback loop to improve the speed of the walk" while still being fundementally not much more than the traditional evolution loop. This is mostly nice in fields like Reinforcement Learning where computing gradients can be difficult. GAs can be quite useful in situations where there are tons of very bad local minima/maxima and you desire the global minima/maxima. Unfortunately, neural networks have lots of really great local minima/maxima and the state space is so large that you'll likely never get the global minima/maxima (and you wouldn't know it if you did). This is why "Neuroevolution" of neural network weights hasn't really caught on.