In the first article I briefly mention my motivation for covering GAs first when teaching ML: it does a great job of introducing ML core concepts, and it's interesting. It's hard to teach ML to many people if you start with the comparatively boring kNN or k-means; starting with GAs gets people excited early and provides a foundation off of which you can jump into the more traditional ML topics. It shows students that there really are interesting things to learn, and they're more likely to stick it through the "boring" topics. I've been teaching this stuff for a few years, and this approach hasn't failed me yet.