[cornel_io]

I mean, lesson zero of optimization is when you're designing a loss function and trying to incentivize agents to perform a task, don't set it up so that suicide has a higher payoff than making partial progress on the task. Maybe make death the worst outcome, not one of the best...?

One of these days I have to actually scour the web and collect a few good examples where evolutionary methods are used effectively on problems that actually benefit from them, assuming I can find them. Almost every example you're likely to see is either a) solved much more effectively by a more traditional approach like normal gradient descent or classic control theory techniques (most physical control experiments fall into this category), b) poorly implemented because of crappy reward setup, c) fully mutation-driven and hence missing what is actually good about evolution above and beyond gradient descent (crossover), or d) using such a trivial genotype to phenotype mapping that you could never hope to see any benefit from evolutionary methods beyond what gradient descent would give you (if the genome is a bunch of neural network weights, you're definitely in this category).