Apart from NEAT/HyperNEAT there are also other approaches to neuroevolution (I think in this context it is referred to as "Evolutionary Neural Architecture Search" [0]). Evolution in general can be applied in different ways (e.g. optimizing the architecture, replacing training gradient descent etc.).
A while ago I co-authored a paper in this space [1] and released some code for interested folks [2].
I also find the related ideas of neuroevolution of the weights of a neural networks to be fascinating in its own right.
I've implemented "cooperative coevolution" which felt like magic when I considered how good it is (on some tasks like continuous control RL problems) relative to known good methods like anything involving gradients.
I wish that this stuff was explored a bit more. Seems we are leaving the paradigm of evolutionary methods...
I've implemented "cooperative coevolution" which felt like magic when I considered how good it is (on some tasks like continuous control RL problems) relative to known good methods like anything involving gradients.
I wish that this stuff was explored a bit more. Seems we are leaving the paradigm of evolutionary methods...