>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.