Abstract: Embodiment method and associated apparatus relate to altering the expected value of a system modeled by a random process simplified to produce a binary outcome. Various embodiments modify a genetic algorithm to optimize such settings as population size, number of iterations to convergence, mutation chance, and sample space. Some embodiment ARON implementations correctly predict game outcome relative to the spread, based on transforming unrelated raw data, applying the transformed raw data to a modified genetic algorithm, generating multiple expected outcomes determined by the modified genetic algorithm as a function of the transformed raw data, and filtering the outcomes as a function of predefined metrics to produce a single end result that can be utilized effectively by an evolutionary-style algorithm.