Title :
Evolving neural network connectivity
Author :
McDonnell, John R. ; Waagen, Don
Author_Institution :
NCCOSC, San Diego, CA, USA
Abstract :
The application of evolutionary programming, a stochastic search technique, for determining connectivity in feedforward neural networks is investigated. 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
Keywords :
feedforward neural nets; stochastic programming; binary mapping problems; connection scheme; evolutionary programming; feedforward neural networks; mean pattern error; network complexity; network parameter optimization; network weights; neural network connectivity; objective function; stochastic search technique; Computational complexity; Computer architecture; Feedforward neural networks; Genetic algorithms; Genetic programming; Neural networks; Process design; Signal processing algorithms; Stochastic processes; Testing;
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
DOI :
10.1109/ICNN.1993.298671
