[diab0lic]

The difference is that in machine learning the changes between iterations are themselves caused by the gradient, in evolution they are entirely random.

Evolution randomly generates changes and if they offer a breeding advantage they’ll become accepted. Machine learning directs the change towards a goal.

Machine learning is directed change, evolution is accepted change.

[rcxdude]

It's more efficient, but the end result is basically the same, especially considering that even if there's no noise in the optimization algorithm, there is still noise in the gradient information (consider some magical mechanism for adjusting behaviour of an animal after it's died before reproducing. There's going to be a lot of nudges one way or another for things like 'take a step to the right to dodge that boulder that fell on you').

[TeMPOraL]

> Machine learning is directed change, evolution is accepted change.

Either way, it rolls down the gradient. Evolution just measures the gradient implicitly, through parallel rejection sampling.