Just read through most of the paper. It's basically an attempt to build an autoencoder feature extractor using genetic programming, by having multiple levels o

Just read through most of the paper. It's basically an attempt to build an autoencoder feature extractor using genetic programming, by having multiple levels of feature extractors, somewhat similar to how convolutional neural networks look for basic features (edges) at a low level and then complex features (eyes, etc) at higher levels. They make a point of only using arithmetic operations (add, multiply, etc) rather than higher-level operations (edge detection, etc) in an attempt to not introduce any domain knowledge requirement or bias. The paper also contains a lot of background of the current state of genetic programming and a proposal for further research. I think it's an interesting attempt to push GP using some of the structure we've learned is useful for deep neural networks. I've always found genetic programming intriguing, although it's hard to argue with its inefficiency these days compared to DNN's.