pavelchristof

Saddle points are not much of an issue too: https://arxiv.org/abs/1710.07406

The fact that you don't need your fitness function to be differentiable is a big advantage :) if you had the gradient it would always help to use it.

In reinforcement learning: evolutionary algorithms work by applying peturbations in the parameters space, not in the action space. If your problem is sensitive to action-space perturbations (requires consistent strategies) it might not be possible or efficient to use "standard" RL. Evolutionary strategies (ES) are quite competitive in RL, especially paired with some dimensonality reduction technique (unsupervised VAE).

Local minima are not really an advantage. ES are nearly local - they sample the space around the current solution to approximate the gradients. They'll get stuck in local minima sometimes too (if there are any). Bayesian optimization is more interesting, as it's actually global.

Absolutely no. Random search does not work in high dimensions because of the "curse of dimensionality" - the number of directions to search grows exponentially with dimension. Gradient descent avoids the problem because it knows the right direction.

Evolutionary strategies are doing (natural) gradient descent using an estimate of the gradient. Here's a good article: http://www.inference.vc/evolutionary-strategies-embarrassing...