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Estimation of distribution algorithms (EDA) is an active area of research within the field of evolutionary algorithms. While EDAs have shown great promise on difficult problems with strong epistasis between genes, such as hierarchical and deceptive problems, they have not been a choice for non-stationary problems where the target solution changes over time. This work aims to explore the diversity within the population of an EDA using a supervised classifier. We introduce a technique, sampling-mutation, that can help increase the useful diversity within the population. We show that sampling-mutation increases the performance of an EDA on a non-stationary problem and a hierarchical problem.