← Thread:User talk:MN/Hard-coded segmentation/reply (9)
I have actually been working for awhile to find a set of gun weights that is both highly efficient against both surfers and random movers, the same with a single set for movement to be good against both Statistical, kNN and Pattern Matching targeting. Since my most recent bots I eschew the use of Flatteners and Virtual Guns. Obviously I did okay coming in number 7 in the rumble, but I feel there is still room for improvement.
Though I really do think there are different optimal weights for different enemies, but there might be a set that does well against all enemies (if not optimally).
A single set that does ok against everyone is what is already calculated with genetic tuning against the whole population at once.
Well, that brings up the question of what one exactly means by "highly efficient against both", or to put it another way, how one scores the individuals in the genetic tuning.
The most common way to interpret it may be optimizing for APS, but that will tend to give very little weight being "highly efficient against surfers". Another way to interpret is optimizing the win percentage. In my mind the phrase "highly efficient against both" would imply optimizing with a scoring system that divides the population into "surfers" and "non-surfers" giving roughly equal weight to them, but the phrase is a bit vague.
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If multi-objective optimization is being done, than surfers and non-surfers can be separated in distinct objectives. The output will be many different sets of weights, with different compromises between surfers and non-surfers, although all of them Pareto efficient.