I'm trying to optimize a set of patterns in an image, but unfortunately they only way I have of evaluating the images results in smaller versions of the pattern scoring much better than larger versions.
As a result, the optimization methods I've tried all just end up shrinking the pattern down until they run into aliasing issues, and start becoming erratic.
I've tried normalizing the images by resizing the patterns to all be the same size at each step, but the optimization algorithm quickly just adds 'decoy' pixels to the corners, defeating the normalization.
I've considered trying to do the optimization with a vectorized form, but evaluating a pattern requires it to be rasterized to a particular size anyway. Vectorizing the pattern also doesn't solve the problem with normalization and adding decoy geometry.
How can I prevent the optimization process from just scaling down the image?