This is a fascinating demo of genetic algorithms for image reconstruction! The animated GIF showing the evolution process is a great touch. I’m curious about how you tuned the genetic algorithm’s parameters (e.g., mutation rate, population size) to balance convergence speed and image quality—any interesting challenges there? Also, for anyone else who’s played with this, have you tried feeding it different types of target images (e.g., complex vs. simple)? How does the algorithm handle those variations?

Interesting approach! The title is misleading though. I do not understand how this relates to pixel art. Pixel art is placing pixel on a constrained canvas and choosing colors on a limited palette which this does not seem to be. Maybe this could be considered blurring effect or image reconstruction/approximation?

How do you think your example would compare against a simple lowpass + downsampling?

I think for most complicated pixel art, "reimagining" the image is necessary to keep it visually appealing and discernible at low-res, like in the examples here:

https://en.wikipedia.org/wiki/Pixel_art

Pixel art isn't just bucketing a higher resolution image into a lower resolution. If you're going to use that term, you should probably have at least a basic understanding of the medium.

Cool!

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