Evolving Neural Network Connectivity
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, a stochastic search technique, for determining connectivity in feedforward neural networks. The method is capable of simultaneously evolving both the connection scheme and the network weights. The number of connections are incorporated into an objective function so that network parameter optimization is done with respect to network complexity as well as mean pattern error. Experimental results are shown for simple binary mapping problems. Neutral networks, Evolutionary programming, Signal detection.
- Operations Research
- Computer Systems