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The addition of this competitive co-evolution yields a larger and more varied database, artificially increasing the difficulty of the classification task. Application to the CEDAR database of handwritten characters shows an increase in the reliability of the evolution of recognisers, as well as in the elimination of over-fitting, relative to the original CellNet software.<\/jats:p>","DOI":"10.1142\/s1469026805001441","type":"journal-article","created":{"date-parts":[[2006,1,12]],"date-time":"2006-01-12T12:03:15Z","timestamp":1137067395000},"page":"305-320","source":"Crossref","is-referenced-by-count":7,"title":["USING COMPETITIVE CO-EVOLUTION TO EVOLVE BETTER PATTERN RECOGNISERS"],"prefix":"10.1142","volume":"05","author":[{"given":"TARAS","family":"KOWALIW","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Concordia University, 1455 de Maisonneuve Blvd. W. Montr\u00e9al, Qu\u00e9bec, Canada. 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