The genetic programming scene kind of evolved into NEAT, HyperNEAT and ES-HyperNEAT as a meta learning concept.
Connections between layers/nodes are serialized as genes of agents with phenotypes and dominant/recessive markers, and an observing CPPN learns to categorize agents into different traits to find more efficient breeding mechanisms.
It's a strong concept, and AFAIK it's still used a lot in the robotics world where you have to guarantee behaviors and have to be able to reproduce behaviors due to safety regulations.
There was a nice intro video into the underlying base concept which is called NEAT by a youtuber named SethBling [2]
Connections between layers/nodes are serialized as genes of agents with phenotypes and dominant/recessive markers, and an observing CPPN learns to categorize agents into different traits to find more efficient breeding mechanisms.
It's a strong concept, and AFAIK it's still used a lot in the robotics world where you have to guarantee behaviors and have to be able to reproduce behaviors due to safety regulations.
There was a nice intro video into the underlying base concept which is called NEAT by a youtuber named SethBling [2]
[1] http://eplex.cs.ucf.edu/ESHyperNEAT/
[2] https://m.youtube.com/watch?v=qv6UVOQ0F44