To be fair to the downvoters, on the surface I appear to be whining about a headline. But what I'm actually doing is complaining about a common thread I see in the industry, where people use ML as a crutch to avoid thinking, and are frequently proud of "solutions" that don't even work despite their absurd computational expense. And for some reason, people in ML command ridiculously inflated salaries.
I'm all about using the right tool for the job. In this case, I'd say algebra, if the job is "park the car." If the job is "teach genetic algorithms with a simple example," no complaints except for the headline.
But if the point is to each genetic algorithms with a simple example -- is there a better simple example problem where a genetic algorithm is a more appropriate approach?
And if there's not a simple example where a genetic algorithm approach is clearly a good tactic, or if it's not straight-forward to learn to distinguish when a GA approach makes sense, is teaching GAs useful?
I'm all about using the right tool for the job. In this case, I'd say algebra, if the job is "park the car." If the job is "teach genetic algorithms with a simple example," no complaints except for the headline.