Genetic programming is not an algorithm. It is a community of researchers interested in applying stochastic optimization / metaheuristics methods to discover an

Genetic programming is not an algorithm. It is a community of researchers interested in applying stochastic optimization / metaheuristics methods to discover and/or optimize computer programs and other computational devices. -- Which saying that it can be formally just about anything but is knit together by various hunches and analogies. This sort of approach has a certain strength through flexibility. I would offer that the strength of a fully formally defined system has the strength that is can be meta-analyzed - propositions about what the entire system is capable of can proven (for example, see neural network approximation theorem). Also, neural networks today have a set-upon using "relu" layers, which basically "nothing but smooth" layers (continuous, piecewise-linear functions) and gradient descent works fine.