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Relative fitness is a new black-box approach to modeling storage devices. Whereas conventional black-box models train to predict a device's performance given "device-independent" workload characteristics, relative fitness models learn to predict the changes in performance between specific devices. There are two advantages. First, unlike conventional modeling, relative fitness does not depend entirely on workload characteristics; performance and resource utilization (e.g., cache usage) can also be used to describe a workload. This is beneficial when workload characteristics are difficult to express (e.g., temporal locality). Second, because relative fitness models are constructed for each pair of devices, changes in workload characteristics (e.g., I/O inter-arrival delay) can be modeled. Therefore, unlike a conventional model, a relative fitness model can be used by applications with a closed I/O arrival process. In this article, we present relative fitness as an evolution of the conventional model and share some early results.