That's not really relevant to this. That is for selecting hyper-parameters for statistical models.

I know the article doesn't mention it, but you can use the exact same techniques for preventing overfitting in machine learning.

That's basically what I said, and this is optimization not machine learning. The problem is the genetic algorithm fits the the specifics of the FPGA and the environment it is optimized in, and doesn't work reliably in other environments or FPGAs.