Preference-based Evolutionary Optimization Using Generalized Racing Algorithms

We propose a generic approach to evolutionary optimization that is suitable for problems in which candidate solutions are difficult to assess: Instead of a deterministic, numerical evaluation of the fitness of individual candidates, we proceed from stochastic, qualitative evaluations in the form of pairwise comparisons between competing candidates. Our extension is based on a proper specification of the selection operator under these conditions and makes use of a preference-based version of an adaptive sampling scheme known as racing algorithms.