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A knowledge-based computer assisted decision (KB-CAD) system is a case-based reasoning system previously proposed for breast cancer detection. Although it was demonstrated to be very effective for the diagnostic problem, it was also shown to be computationally expensive due to the use of mutual information between images as a similarity measure. Here, the authors propose to alleviate this drawback by reducing the case-base size. The problem is formalized and a genetic algorithm is utilized as an optimization tool. Appropriate for the problem representation and operators are presented and discussed. A clinically relevant index of the area under the receiver operator characteristic curve is used as a measure of the system performance during the optimization and testing stages. Experimental results show that application of the proposed method can significantly reduce the case-base size while the classification performance of the KB-CAD, in fact, increases.