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Many applications of evolutionary algorithms utilize fitness approximations, for example coarse-grained simulations in lieu of computationally intensive simulations. Here, we propose that it is better to learn approximations that accurately predict the ranks of individuals rather than explicitly estimating their real-valued fitness values. We present an algorithm that coevolves a rankpredictor which optimizes to accurately rank the evolving solution population. We compare this method with adoi:10.1145/1830483.1830652 dblp:conf/gecco/SchmidtL10a fatcat:hn7xluj7t5c6xc5yvfcvtmcexy