EAs are effectively ML techniques. It's all a game of search.

The biggest problem I have seen with these algorithms is that they are wildly irrespective of the underlying hardware that they will inevitably run on top of. Koza, et. al., were effectively playing around in abstraction Narnia when you consider how impractical their designs were (are) to execute on hardware.

An L1-resident hill climber running on a single Zen4+ thread would absolutely smoke every single technique from the 90s combined, simply because it can explore so much more of the search space per unit time. A small tweak to this actually shows up on human timescales and so you can make meaningful iterations. Being made to wait days/weeks each time you want to see how your idea plays out will quickly curtail the space of ideas.

Neural networks already take a long time to train so throwing out gradient descent entirely for tuning weights doesn't scale great.

Genetic programming can solve classic control problems with a few instructions when they can solve it, so that's cool.

There's a lot of problems where you're searching among many possibilities in a space that has lots of pieces in each solution. If you can encode the solution and fitness, a GA can give you an answer if you play with the knows enough. You also might not need to be an expert in that domain, like writing heuristics. If you know some, they might still help.

Same thing with radiation patterns. You can make a directional antenna that has a huge amount of gain in one direction. The trade-off is that it's deaf and dumb in every other direction. (See a Yagi-Uda design, for instance.)

Physics is immutable and when it comes to antenna design there really is no such thing as free lunch. Other than coming up with some wacky shapes I don't really think AI is going to be able to create any type of "magic" antenna that's somehow a perfect isotropic radiator with a low SWR across some huge range of wavelengths.