We show that a fitness function, when taken together with an algorithm, can be reformulated as a set of probability distributions. This set can, in some cases, be equivalently viewed as an information vector which gives ordering information about pairs of search points in the domain. Certain performance criteria definable over such an information vector can be learned by linear regression in such a way that extrapolations can sometimes be made: the regression can make performance predictions about functions it has not seen. In addition, the vector can be taken as a model of the fitness function and used to compute features of it like difficultly via vector calculations.
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