Why can genetic algorithms work in high-dimensional search spaces?

We show that the effective dynamics of the elitist $(1+M)$ genetic algorithm is, in the limit of small mutations, normalized gradient descent on the loss in the presence of anisotropic Gaussian white noise. In expectation, therefore, a simple mutation-selection genetic algorithm follows the gradient