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In a gun, the easiest way would be having many sets of weights and put them all in a virtual gun array. But if all the sets perform similar to each other, the virtual gun will not be able to pick the optimal one due to noise.

And flatteners actively try to screw up most statistics, including virtual gun scores.

For tuning weights in a gun, I'd strongly recommend testing things offline with WaveSim or similar. It's a couple orders of magnitude faster to test against pre-gathered data.

Also, I've done quite a bit of tuning of gun weights this way. The gun weights in Diamond were evolved through a genetic algorithm with WaveSim. But honestly it barely outperforms the hand-tuned weights I had before that. I'm fairly skeptical that it's worth ever straying from "generally optimal" in order to eventually get "bot-specific optimal". That is, I think a gun with generally optimal weights tuned over 10,000 matches will beat bot-specific optimal tuned over <= 35 rounds.

Worth noting that all the tuning I've done was specifically on attribute weights though, not decay rate or other factors. My main gun doesn't decay, just my Anti-Surfer gun. (And you may not want to tune an AS gun against pre-gathered data, though Skilgannon has.)

Based on information from you and others who say that weights on predictors other than data decay have very little effect on performance, the method I described might only be useful for adjusting your data decay in an anti surfer gun. However, it might also allow you to add more specialized predictors which started at weights of zero and were only used if found to be relevant. For example, what if you found that looking at some statistic of the past 5 GFs a bot goes to helps against bots using flatteners, but not at all against anything else. Adding this to a statically weighted gun would probably decrease performance against everyone but opponents using flatteners. I might use WaveSim when I'm working on an anti non adaptive targeting system.