[eggy]

You're spot on about genetic programming. I am a self-taught person who plays with anything that strikes my fancy; I learn by playing. I read all three volumes of the Artificial Life series from the Santa Fe Institute at the time (now there are more), and went in many directions in the 1990s - Fuzzy Logic, Expert Systems, ANNs, and Evolutionary Computation (GA (Genetic Algorithms) and GP Genetic Programming), and AL (Artificial Life) all fascinating. I found, and still find, genetic programming attractive even if it has not found its niche in the ML community. I think the CI (Computational Intelligence) community at large will eventually develop well-fitted uses for it. I was trying to use an FPGA and Koza's modified GP code to have the FPGA re-program itself as a GP evolved a better program than I originally wrote to kickstart it. I didn't get too far. This was 1996-97 though. Pretty much on my own then, not really much of an Internet to find information, especially esoteric information, or cheap many-gated FPGAs. Outside of ML, GP has found moderate success. One example is this paper (sorry behind paywall, so only the paper title here), that started with using expert data, tried ANNs, then ANNs and statistics, until it used a GP approach:

"A Computational Intelligence-Based Genetic Programming Approach for the Simulation of Soil Water Retention Curves"

I also use the term ANNs over just NNs to keep it to the silicon, and not wetware ;) Although, they did hook up a small ANN to a cockroach once, IIRC...