There is actually a whole lot of variance between individual silicon chips, even two chips right next to each other on the wafer will preform slightly differently. They will all meet the spec on the datasheet, but datasheets always specify ranges, not exact values.
That said, it seems like it would be very doable to first evolve a chip with the functionality you need in a single environment, then slowly vary parameters to evolve it to be more robust.
Or vice versa begin evolving the algorithm using a fitness function that is the average performance across 5 very different chips to ensure some robustness is built in from the beginning.
There were also a bunch of cells that had inputs, but no outputs. When you disconnected them... the circuit stopped working. Shades of "magic" and "more magic".
I've never worked with it, but I've had a fascination with GA/GP ever since this paper/the Tierra paper. I do wonder why it's such an attractive technique - simulated annealing or hill climbing just don't have the same appeal. It's the biological metaphor, I think.
Injecting noise and other constraints (like forcing it place circuits in different parts of the device) are totally valid when it needs to evolve in-place.
For the most part, I think it would be better to run in a simulator where it can evolve against an abstract model, then it couldn't overfit to the specific device and environment. This doesn't work if the best simulator of the system is the system itself.
https://en.wikipedia.org/wiki/Robust_optimization
https://www2.isye.gatech.edu/~nemirovs/FullBookDec11.pdf
Robust Optimization https://www.youtube.com/watch?v=-tagu4Zy9Nk
That's was truly interesting about it to me.
But mistakes aside, what would it be if the chips from the factory could learn / fine-tune how to work (better) , on the run..
Or maybe you were getting a leak from the audio signal before modulation, e.g. via power lines or something.