Reward function design and overfitting or “cheating” (optimizing to some incidental thing instead of the real problem) is a major reason genetic and evolutionar

Reward function design and overfitting or “cheating” (optimizing to some incidental thing instead of the real problem) is a major reason genetic and evolutionary AI approaches never took off. I don’t think anyone ever figured out how to make reward function design easier or make any kind of unsupervised evolutionary learning work. I played with EC a lot in college and these systems were almost comically good at the “cheating” part of overfitting. I watched evolving programs do things like learn the scheduling behavior of the OS kernel (because the reward function was threaded) or the disk timing differences resulting from where different parts of the sample set were stored on the drive. They could guess the answer by inferring load time.