In genetic algorithms, any gradient found would be implied by way of the fitness function and would not be something to inherently pursue. There are no free lunches like with chain rule of calculus.
GP is essentially isomorphic with beam search where the population is the beam. It is a fancy search algorithm. It is not "training" anything.
True, genetic algorithms are only implied, but those implied gradients are used in the more successful evolutionary strategies. So while they might not look like it (because it's not used in a continuous descent) they still very much work like (although they represent a smoother function than) regular back-prop gradients when aggregated.