In role-based access control (RBAC), roles are traditionally defined as sets of permissions. Roles specified by administrators may be inaccurate, however, such that data mining methods have been proposed to learn roles from actual permission utilization. These methods minimize variation from an information theoretic perspective, but they neglect the expert knowledge of administrators. In this paper, we propose a strategy to enable a controlled evolution of RBAC based on utilization. To accomplish this goal, we extend a subset enumeration framework to search candidate roles for an RBAC model that addresses an objective function which balances administrator beliefs and permission utilization. The rate of role evolution is controlled by an administrator-specified parameter. To assess effectiveness, we perform an empirical analysis using simulations, as well as a real world dataset from an electronic medical record system (EMR) in use at a large academic medical center (over 8000 users, 140 roles, and 140 permissions). We compare the results with several state-of-the-art role mining algorithms using 1) an outlier detection method on the new roles to evaluate the homogeneity of their behavior and 2) a set-based similarity measure between the original and new roles. The results illustrate our method is comparable to the state-of-the-art, but allows for a range of RBAC models which tradeoff user behavior and administrator expectations. For instance, in the EMR dataset, we find the resulting RBAC model contains 22% outliers and a distance of 0.02 to the original RBAC model when the system is biased toward administrator belief, and 13% outliers and a distance of 0.26 to the original RBAC model when biased toward permission utilization.