Can confirm. I've played around with differential evolution and a few others. Nothing was able to beat CMA-ES. At best, after some manual tuning it was just barely matching the untuned CMA-ES. (The task was to optimize a small neural network, gradients not easily available.)

CMA-ES is not suited for very large problems (more than a few hundred parameters), but there are several variants that can handle this, like sep-CMA-ES.