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The optimization of many engineering systems is challenged by the solution over-fit to the data set used to evaluate potential solutions during the evolutionary process. The solution over-fit phenomenon is hard to detect and is especially prevalent in problems involving example-based training, such as pattern feature selection and pattern classifier design. For these applications, uncontrolled over-fit can lead to biased features being extracted and degraded classifier generalization abilities. This paper details the performance of a solution over-fit control strategy used in the multiobjective evolutionary optimization of a multileveled classification system. This control, embedded within a solution validation procedure, minimizes the over-fit effects without modifying the dominance relation used in the processing of candidate solutions. Extensive experimental analysis using multiobjective genetic and memetic algorithms demonstrates both the need and the efficiency of the proposed over-fit control for pattern classification systems optimization. [ABSTRACT FROM AUTHOR]