In the field of Optimization and Operations Research : Are there "Types" any Problems where Evolutionary Algorithms (significantly) Outperform Non-Evolutionary Algorithms?
Advancements in Machine Learning has shown us that problems that have differentiable "objective functions" (i.e. the function being optimized) - even in instances where these "objective functions" are high dimensional, non-convex, complex and noisy - they are generally best handled using some variant of the Gradient Descent Algorithm.
However, there are still some cases that even when the "objective function" is differentiable - if for some reason evaluating derivatives of the "objective function" are quite time consuming (e.g. high dimensional functions), Evolutionary Algorithms (e.g. Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing) offer advantages in such instances.
Advancements in Machine Learning has also shown us that Evolutionary Algorithms are sometimes preferred for certain types problems such as "games", in which optimal strategies are developed by mutating and combining random strategies according to their performance with respect to some target (e.g. https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies , https://www.youtube.com/watch?v=OGHA-elMrxI)
In the case of discrete combinatorial optimization problems (where the "objective function" is non-differentiable), Evolutionary Algorithms still offer strong advantages.
My Question: With all this being said, are there any particular "types" of problems where Evolutionary Algorithms are considered the "gold standard" for optimizing? Or in general, are there no such "types" of problems where Evolutionary Algorithms present clear advantages?