> I wonder if coupling them with ML techniques could bring them back.

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.

The main use of evolutionary algorithms in machine learning currently is architecture search for neural networks. There's also work on pipeline design, finding the right way to string things together.

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.

They're not in the press a lot. They're probably still in production behind the scenes. I was reading about using them for scheduling not long ago. Btw, a toy one I wrote to show how they work got best results with tournament selection with significant mutations (closer to 20%).

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.

Much more robust than almost all modern ML algorithms which let's be real, aren't exactly applicable to anything outside recommendation systems and 2D image processing.

This comment really sums it up well. Literally everything with antenna design is a trade-off. You can design an antenna to radiate very well at a given wavelength. The better it is at doing this, the worse it tends to be at every other wavelength. You can make an antenna that radiates to some degree across a wide array of wavelengths, but it's not actually going to work very well across any of them.

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.

"Do not confuse inexperience with creativity"...

Not only are they pathological, but when you order a example be built because CST confirmed that the design would kick ass and you put it in the chamber and actually measure it for real, you walk away wondering why you wasted so much time and money.

Antenna design feels like some occult arts

"It has become fashionable to design wire antennas with some type of optimizer program, almost independent of good physics or high-quality performance. The results sometimes have wire segments in all directions; see Figure 5.24 for an example. A long total wire length may achieve resonance in a small volume, but there are several disadvantages. If Z is the normal monopole direction, the X currents tend to cancel, as do the Y currents. However, in certain directions the cross- polarized field may not be negligible. Longer total wire length increases loss resistance, reduces efficiency, and increases reactance. And generally the bandwidth is narrow. Examples are Altshuler and Linden (2004), Choo et al. (2005), Altshuler (2005), and Best (2002, 2003). Use of fractals and meanderlines to fill space (Gonz
alez-Arbesu ́ et al., 2003; Best and Morrow, 2002) suffers from the same problems.

“Do not confuse inexperience with creativity” (Linda Whittaker) is appropriate here."

As a rocket scientist I assure you it's been tried

As an avid observer of rocket design, I suppose that hasn't happened because SSTO may not have any good solutions. I further suppose that the design parameters are so constrained there is very little opportunity for a generative or evolutionary, or any other AI-driven design approach, to do more than optimize some components.

SpaceX's solution with Starship definitely demonstrates how difficult a problem it is. Raptor are the best engines humanity has, all the landing hardware is part of the launch tower to save weight, and those seem to be table stakes. Stoke aerospace has a fantastically genius solution with their regeneratively cooled heatshield / expander cycle aerospike engine. It literally turns the energy you're trying to burn off during re-entry into delta-v in the opposing direction while reducing weight and complexity.

A good rule of thumb: never mock someone’s enthusiasm or excitement about learning something, even if it’s old news to you. Let people enjoy discovering things.

In my limited understanding, there are many factors differentiating between antennas, different antennas are better at emitting/receiving at different frequencies, and also there's directionality in the mix . For example a satellite dish and an FM radio antenna are both antennas, they're certainly not the same thing.

The requirements and constraints depend on the product. For example, the geometry of your product to accommodate the antenna is not always the same; the (internal and external) environment of the case is also different; there may be requirement of combining various frequency bands into one antenna; etc.

There are many different types of antennas, each with different tradeoffs. Some examples of the tradeoffs are:

- How well does it work at a specific frequency, if you're just trying to transmit/receive on one specific frequency

- How well does it work on the frequency range(s) if you're working on more than a specific frequency

- How well does it block frequencies that you don't want to send/receive

- How well directional is it to trade off using lots of radiation to blast in many directions vs a higher focus beam using less energy or getting less interference from other directions

- How much physical space do you have in each dimension?

These are just a few examples, but for example you can provide a much "better" connection in almost every sense of the word if you can make your antenna directional (point between the source and destination) only on a specific frequency, and be huge, but most of the time you have some physical space constraints, multiple frequencies to deal with, and the potential that your signal could at least come from some degrees in each the x/y/z axes, and sometimes it needs to be omnidirectional.

Again, these are just examples, but you end up with these types of design considerations that play into larger system design (can you put more transmitters up to encourage directionality, limit frequencies, etc).

There are some well known "base" antenna types like dipole, yagi-uda, circular, and log periodic dipole array if you want to look them up by name and see some of the known tradeoffs and design choices, but virtually any wire can be an antenna and there are an unlimited number of shapes, nearly all of which don't have known radiation characteristics