Evolving Neural Network Pattern Classifiers
NAVAL COMMAND CONTROL AND OCEAN SURVEILLANCE CENTER RDT AND E DIV SAN DIEGO CA
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This work investigates the application of evolutionary programming for automatically configuring neural network architectures for pattern classification tasks. The evolutionary programming search procedure implements a parallel nonlinear regression technique and represents a powerful method for evaluating a multitude of neural network model hypotheses. The evolutionary programming search is augmented with the Solis Wets random optimization method thereby maintaining the integrity of the stochastic search while taking into account empirical information about the response surface. A network architecture is proposed which is motivated by the structures generated in projection pursuit regression and the cascade-correlation learning architecture. Results are given for the 3-bit parity, normally distributed data, and the T-C classifier problems. Evolutionary programming, Neural networks, Signal detection.