This paper discusses the training of product neural networks using genetic algorithms. Two unusual techniques are combined; product units are employed in addition to the traditional summing units and a genetic algorithm is used to train the network rather than using backpropagation. As an example, a neural network is trained to calculate the optimum width of transistors in a CMOS switch. It is shown how local minima can affect the performance of a genetic algorithm, and one method of overcoming this is presented.
David J. Janson,
James F. Frenzel,
"Training product unit neural networks with genetic algorithms", Proc. SPIE 1706, Adaptive and Learning Systems, (20 August 1992); doi: 10.1117/12.139958; https://doi.org/10.1117/12.139958