Does a neuroevolution algorithm do best when it obeys biology's mutation-load law?
deleterious mutation load ⇄ neuroevolution performance · via mutation rate
Population genetics says a population's load of harmful mutations scales as U·d/s, the mutation supply over the strength of selection. A neuroevolution algorithm sets its mutation rate by hand (here, 0.03). The system proposed these are the same knob: the network should evolve best when the selection pressure it imposes is tuned to that fixed mutation rate, holding the harmful-mutation load at the optimum a biological population settles into.
The open question
If a neuroevolution run and a biological population obey the same load law, then sweeping the algorithm's selection strength against its fixed mutation rate should trace the U·d/s curve, with peak performance at the load minimum. Does that hold quantitatively, or is the shared 'mutation rate' just a coincidence of vocabulary?
What the system already tried
It read full-text papers on mutation load, selection, and neuroevolution, grounded the facts, and re-asked the jury. The quantitative link was plausible, but the facts did not pin the biological and algorithmic mutation rates to one mechanism, so it stayed open.
The sources it read
Open review
Is this a real connection or a coincidence of shared words? The facts above are grounded in the sources; the leap between them is what is unproven. Make the case, or settle it with a reference.