In this paper we propose a method for evaluating the performance of an evolutionary learning system aimed at producing the optimal set of prototypes to be used by a handwriting recognition system. The trade-off between generalization and specialization embedded into any learning process is managed by iteratively estimating both consistency and completeness of the prototypes, and by using such an estimate for tuning the learning parameters in order to achieve the best performance with the smallest set of prototypes. Such estimation is based on a characterization of the behavior of the learning system, and is accomplished by means of three performance indices. Both the characterization and the indices do not depend on either the system implementation or the application, and therefore allow for a truly blackbox approach to the performance evaluation of any evolutionary learning system. (C) 2002 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved. [References: 30]
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