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 utilizes 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.
Improved Learning in Constructive Neural Networks for Pattern Classification using Genetic Algorithm
