Lessons learned from Mona Lisa (part 2)

    I learned yet another important lesson about why it's extremely important to provide the fitness evaluator the exact same image as shown to the user.

    If you recall the original problem I reported was that the candidate image was being scaled down before evaluation, thereby allowing many pixels to avoid detection/correction. Yesterday I enabled anti-aliasing for the image shown to the user. I figured so long as the evaluator was seeing 100% of the pixels (no scaling going on) I should be safe, but it turns out this isn't enough.

    Take a look at the following images:

Anti-aliasing Enabled

Anti-Aliasing Disabled

They show the exact same candidates with anti-aliasing enabled and disabled. Notice how the anti-aliased version has "streaks" of errors across the face, similar to the problem I was seeing when the candidate was being scaled. It turns out that sometimes the candidates contains polygons that introduce errors into the image in the form of "streaks" (polygons rendered with sub-pixel precision). The interesting thing is that aliasing suppresses these errors so the evaluator function does not see it. Consequently, the users sees a whole bunch of errors which the fitness function will never fix. Sounds familiar?

    In conclusion: you should always (always!) pass the fitness function the exact same image you display to the user. Better safe than sorry :)