1) "explainable AI" is better in GA than in deep learning. GA gives you a structure that works and probably easier to understand than any Deep Learning model (which it is a huge math function). There are so many things that also doesn't make sense why they work in deep learning but we still use them, that is the same with GA.
2) Knowing the fitness function doesn't mean you can solve the problem. When the search space is so big you need something to search on it, and there is where GA can shine. It is also the same with Deep Learning and mostly evolutionary computation. The search space is so huge for a "brute force algorithm". You need heuristics and GA works well for some problems, the same way gradient descent works well for others too.
1) "explainable AI" is better in GA than in deep learning. GA gives you a structure that works and probably easier to understand than any Deep Learning model (which it is a huge math function). There are so many things that also doesn't make sense why they work in deep learning but we still use them, that is the same with GA.
2) Knowing the fitness function doesn't mean you can solve the problem. When the search space is so big you need something to search on it, and there is where GA can shine. It is also the same with Deep Learning and mostly evolutionary computation. The search space is so huge for a "brute force algorithm". You need heuristics and GA works well for some problems, the same way gradient descent works well for others too.