I love it! Great to see these kind of applications

Interesting, thanks for sharing your thoughts! I'm a bit curious about why genetic algorithms might be better for these strange objective functions, as compared to something like simulated annealing. I can understand that a pure gradient method could easily be insufficient, but do the underlying components of genetic algorithms (like cross-over, etc.) really provide a distinct advantage here? Especially when the fitness is probably directly related to the gradient anyways

> In my experience, the crossover implementation is the most important one to focus on

I've heard this as well

Cool! I'd never heard of the building block hypothesis before, thanks for sharing.

Agreed. That being said, I think the prior is that you still need to have enough understanding of the state space to be able to design good mutations, cross-over, and fitness. This can easily add a lot of overhead. In contrast, I think that other cool methods like swarm optimization and ant colony optimization are also promising and in some ways simpler.

Absolutely, lots of great work is being done in this domain right as we speak. Neuromorphic computing and analog computing I personally think are some of the most exciting things to look out for in the next 10 or so years.

> isn't RL agent-competitions approaches (i.e. simulating games between agents with different parameter values and iterating on this agents) a form of genetic algorithms?

Hmm, I hadn't thought about RL like that. I guess the signal from a reward function based on competition could be considered "fitness", and then perhaps some form of cross-over is done in the way we iterate on and update the agents. Interesting thought.

Cool idea, thanks for sharing!

My thoughts too. Simulated annealing and similar strategies seem to intuitively be better is most cases where traditional gradient methods aren't applicable. I can imagine a handful of cases where genetic algorithms MIGHT be better, but even then I am not fully convinced and it just feels gimmicky.

This is a reasonable question but I believe you are misunderstanding. The randomization of parameters in a neural network (I assume you are talking about initialization?) is certainly not the same as a mutation in a GA. Mutation occurs randomly, sure, but is selected for and crossed over, whereas hill-climbing and gradient descent simply move on the gradient and do not use either random mutations or cross-over so are not genetic.