> I'm puzzled why GP is so unpopular compared to say, type system research
Genetic Programming is not getting much attention because it is inherently inefficient, and there are much better optimization methods out there. Genetic Programming, similarly to Monte Carlo methods, is often the "easy way out" when you are trying to solve an optimization task where you cannot (bother to) model the process.
For example: You could train Neural Networks' weights with Genetic Programming, but that's extremely inefficient compared to gradient descent.
Bottom-line: Genetic Programming is not a magic bullet, and is not guaranteed to converge to a workable solution in a "reasonable" time.
Genetic Programming is not getting much attention because it is inherently inefficient, and there are much better optimization methods out there. Genetic Programming, similarly to Monte Carlo methods, is often the "easy way out" when you are trying to solve an optimization task where you cannot (bother to) model the process.
For example: You could train Neural Networks' weights with Genetic Programming, but that's extremely inefficient compared to gradient descent.
Bottom-line: Genetic Programming is not a magic bullet, and is not guaranteed to converge to a workable solution in a "reasonable" time.