[apl]

An AlexNet/ResNet-type moment may be in the cards for GAs, but I wouldn't put any money on it. They're typically only one better than brute-force. This can be good enough (and is certainly easy to implement), but if you can get a gradient for your problem -- you should use that. And nowadays, you can typically get a gradient!

Most recent advances in the fields you mentioned were driven by gradient-based optimization (e.g., drug design, routing, or chip design: https://www.nature.com/articles/s41586-021-03544-w).

Nature can't SGD through genomes but has a metric ton of time, so evolution might be near-ideal for sexual reproduction. We typically don't have billions of generations, trillions of instantiations, and complex environments to play with when optimizing functions... It's telling that the fastest-evolving biological system (our brain!) certainly doesn't employ large-scale GA; if anything, it probably approximates gradients via funky distributed rules.

EDIT: The most modern application I can think of was some stuff from OpenAI (https://openai.com/blog/evolution-strategies/). But the point here is one of computational feasibility -- if they could backprop through the same workload, they would.

[api]

If biological evolution wasn't much better than brute force we would not be here. There is no way you could randomly generate a functional genome for anything non-trivial in any reasonable multiple of the age of the universe even if you had a trillion Earths to work with.

But ... GAs are not biological evolution. I think the real issue is that present day GAs only approximate some aspects of biological evolution, but they're very "chunky" in the same way that primitive neural network models are. They get generation and selection but actual biological evolution involves much deeper processes than that. Evolutionary theory is rich and quite fascinating.