Since GAs are analogue of biological natural selection/evolution, I will explore that a little: Evolution didn't build humans by starting with the problem of '

Since GAs are analogue of biological natural selection/evolution, I will explore that a little: Evolution didn't build humans by starting with the problem of 'build a human' - it branches into solving various local niche problems simultaneously (primarily motivated by, how to replicate self). See also: Darwin's finches. I would assume that biological evolution also spends a lot of time stuck in local maxima with short periods of volatile change as a new local maximum is identified (this hypothesis fits perhaps conveniently with the fossil record). So natural selection is excellent for blindly solving problems with progressive goal(s) - which are rarely of the type identified by pure computer science. But, hard optimization problems with an open-ended solution could be well-suited. A problem such as this where a near-miss answer doesn't get you any closer to the correct answer makes it difficult or impossible to identify relative genetic fitness - evolution is more likely to get stuck in an ineffective local maximum. Evolution may be slower at solving this problem than pure random guessing. Interestingly, the article introduces the aspect of preparation time, which is not exactly what I understood from the xkcd comic. I believe this would allow an artificial escape route for the algorithm, since it's less likely to get stuck in a wrong local maximum considering price alone. tl;dr: Agreed, biological evolution doesn't seem to effectively solve a yes/no problem like this.