PROJECT CellNet: EVOLVING AN AUTONOMOUS PATTERN RECOGNIZER

N. KHARMA, T. KOWALIW, E. CLEMENT, C. JENSEN, A. YOUSSEF, J. YAO
2004 International journal of pattern recognition and artificial intelligence  
We describe the desire for a black box approach to pattern classification: a generic Autonomous Pattern Recognizer, which is capable of self-adapting to specific alphabets without human intervention. The CellNet software system is introduced, an evolutionary system that optimizes a set of pattern-recognizing agents relative to a provided set of features and a given pattern database. CellNet utilizes a new genetic operator designed to facilitate a canalization of development: Merger. CellNet
more » ... izes our own set of arbitrarily chosen features, and is applied to the CEDAR Database of handwritten Latin characters, as well as to a database of handwritten Indian digits provided by CENPARMI. CellNet's cooperative co-evolutionary approach shows significant improvement over a more standard Genetic Algorithm, both in terms of efficiency and in nearly eliminating over-fitting (to the training set). Additionally, the binary classifiers autonomously evolved by CellNet return validation accuracies approaching 98% for both Latin and Indian digits, with no global changes to the system between the two trials.
doi:10.1142/s0218001404003587 fatcat:odsa24aqgffmjmpnrhyqxxk2jm