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Prototype selection is the process of" nding representative patterns from the data. Representative patterns help in reducing the data on which further operations such as data mining can be carried out. The current work discusses computation of prototypes using medoids [1], leaders [2] and distance based thresholds. After" nding the initial set of prototypes, the optimal set is found by means of genetic algorithms (GAs). A comparison of stochastic search algorithms is carried out by Susheela Devi and Narasimha Murty [3]. They conclude that performance of genetic algorithms is the best among the search algorithms. Chang and Lipmann [4] suggest the use of genetic algorithms for pattern classi" cation.

In the following sections, we discuss and compare various prototype selection methods under consideration. Comparison of results are based on nearest neighbor classi" er (NNC). Subsequently, considering those …