Evolution is a family of obsolete black-box optimization algorithms. It is a population-based method: many samples are drawn from an initial distribution, and merged, mutated or removed depending on their fitness metric. Its primary advantage is that it continues to work when the fitness metric is a pathological (expensive-to-compute, discontinuous, non-differentiable, etc) function, unlike gradient descent, which has replaced it in most practical applications.
It's already far too late.