I'll try to get a better answer with diagrams soon, but I will be gone over the weekend so it may not be ready until Monday. One thing I was thinking about was Bearing Offset Normalized by Reasonable Travel Path Index targeting (an excellent name right?) where the bins would be scaled non-linearly. So, if I started wall smoothing at GF 0.5 then 0.0 through 0.5 would all be bins of similar size, but 0.5 through 1.0 would be a different size. However, I haven't tried it yet because I have had other things to do and I don't think it would be much of an improvement on precise MEA. However, the main issue I have, with all GF targeting algorithms, is basically that a robot going forwards 0.5 GF's and then back as far as it can will not necessarily end up at GF 0 due to wall smoothing. I cannot think of any solution to this problem (besides simulating the enemy's movement with the same algorithm from an identical list of random numbers) but I think that changing the bin size might make the problem worse.
That being said, I have to admit that my imprecise MEA didn't seem too bad. However, I think this was because the wallsmoothing approximation that I used will work as long as the enemy starts facing a certain range of directions. Because wallsmoothing is so common and this range of directions was basically that a robot would use while wallsmoothing, that wasn't a bad assumption, but I don't like making it.