They're quite successful on complex multiobjective problems, but you need a lot of compute power to cover the solution space sufficiently. Most problems either

They're quite successful on complex multiobjective problems, but you need a lot of compute power to cover the solution space sufficiently. Most problems either 1) don't have such stringent requirements on multiobjective optimization (i.e. the problem can be recast with a single objective function, likely one with a gradient), or 2) more typical machine learning algorithms are "good enough" and require less compute power. But for instance designing complex geometries under numerous constraints is well-suited to evolutionary algorithms. EAs also shine when the evaluation for fitness is computationally-intensive, as you can defer the fitness evaluations until absolutely necessary.