This work presents a novel approach to learning the quality and performance of an algorithm through the use of evolution. When an algorithm is developed to solve a given problem, the designer is presented with questions about the performance of their proposed method and its relative performance against existing methods. This is an inherently difficult task. However, under the current paradigm, the standard response to this situation is to use a known fixed set of datasets - or simulate new datasets themselves - and a common metric amongst the proposed method and its competitors. The collated algorithms are then assessed based on this metric with often minimal consideration for the appropriateness or reliability of the datasets being used, and the robustness of the method(s) in question [1, 13, 19].