The paper does not give that many details about the evolution part. Normally, evolutionary algorithms contain some cross-over component where solutions can breed with each other. Otherwise it's better classified as hill climbing / beam search.
One intriguing caption mentioned something requiring 16 “mutations”. I’d sure like to know how these mutations work.
I fear it’s not really evolutionary algorithms in the typical sense.
There's also 'evolutionary strategy' algorithms that do not use the typical mutation and crossover, but instead use a population of candidates (search samples) to basically approximate the gradient landscape.