I still see papers written on them occasionally. I have always wanted to implement one, but I've never had a use case. I think there are certain categories of problems where they excel, but in the real world, most of the time, there seems to be a better approach.

One real-world use case I saw was using genetic algorithms to design an automobile brake rotor to reduce heat (or increase heat dissipation). From what I remember of the presentation... Basically, they had a very large number of mathematical definable designs with many input variables. The interactions between these different variables were not necessarily clear. Elements of one of these designs might combine well with elements from a totally separate design. And the model to test them was computationally expensive.

They were able to use this genetic algorithm to design a rotor that, at least on the computer, was meaningfully better than their companies (and likely the industry's) state of the art.