First, the right tool for the job. ANNs are able to be a general function approximator with sufficient training to be a cost-effective choice to implement. Second, ANNs have been around about 35 years longer than GP. The TWEANNs I am studying, and that I already mentioned in a previous reply in this thread, hybridize ANNs and EC (GAs and GP), so if you include Neural Networks that utilize Evolutionary Computation techniques to modify weights or topology, then GP is being used to an extent. Replication as a variable in EC is the key force in biology, and I only see more use of EC techniques to enhance the general function approximators that are ANNs. Further, there are also hybridized computing machines that have been made, and are being made with FPGAs and GPUs. Finance and supercomputing are just two areas that are looking to utilize them. In some, the FPGAs are simply there for updating special computation programs that feed the GPUs. There is some research with a GP optimizer updating the FPGAs and then using the GPUs for the massive parallelization of the computations.