(the reasonable way is embedding search, which runs much faster with some precomputation, but you still have to store things)

I think a lot of the genetic algorithms people do implement recombination-like things. Most of the things operated on aren't really structured like genomes so it makes less sense there.

> But intelligence like you or I's operates in an unconstrained problem space. I don't think you can apply gradient descent because, how the heck could you possibly score a behavior?

> This is where evolution excels as an algorithm. It can take an infinite problem space and consistently come up with "valid" solutions to it.

Evolutionary search also relies on scoring. Genetic algorithms on computers hardcode a "fitness function" to determine what solutions are good and should be propagated and biological evolutionary processes are implicitly selecting on "inclusive genetic fitness" or something. You can't apply gradient-based optimizers directly to all of these, though, because they are not (guaranteed to be) differentiable. There are lots of ways to optimize against nondifferentiable functions in smarter ways than evolutionary search, and these come under "reinforcement learning", which does work but is generally more annoying than (self-)supervised algorithms.

> I think Darwin Machines might be able to explain "animal intelligence". But human intelligence is a whole other deal. There's some incredible research on it that is (as far as I can tell) largely undiscovered by AI engineers that I can share if you're interested.

As far as I know human brains are more or less a straight scaleup of smaller primate brains.