>What do you think of the car example

I haven't read the paper, but only watched the brief video. I wouldn't say that's Machine Learning either.

Maybe a bad analogy but one I can come up with on a spot: A hinge isn't carpentry but metalwork and pretty much everyone agrees on that. Now if you build a wooden cabinet, you are probably using hinges; Nevertheless, you'd still call the cabinet as such carpentry, not metalwork.

Anyway, the definitions aren't clear and consistent enough to make super good and objectively true distinctions. In the end it often boils down to personal subjective interpretations.

Edit: Especially the classification of evolutionary algorithms has been an ongoing discussion for, like, decades. Which goes to show that there probably isn't an objectively right clear classification - if only because people don't agree on a single definition of Machine Learning as is. However, by the most common definitions that I know, evolutionary computation is its own subfield next to ML.

It generates data. It doesn't take data and learns patterns from that data.

If you have a very specific opinion and get defensive when someone disagrees, why pose it as a question instead of just stating your opinion?

There is no dataset from which you learn patterns. You usually evaluate objects which are then again used for mutation based on their performance.

Of course it's not happening in a vacuum, but that's not what "data" usually means.

Ups, you're right. Typo!

I'm pretty sure it's not ML by definition. Oxford definition:

the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.

There is no data(set) involved in evolutionary algorithms, so it's not ML. Genetic algorithms are usually seen as (a part of) AI, though.