I suppose with Genetic Programming, given an appropriate set of descriptive symbols, it is relatively easy to understand the final result and intuit if there is

I suppose with Genetic Programming, given an appropriate set of descriptive symbols, it is relatively easy to understand the final result and intuit if there is any over-fitting involved. On the other hand, machine learning results are typically a black box, the weights involved typically do not easily lend themselves to understanding the nuances of the solution.