In A. Abraham, B. de Baets, M. Köppen, and B. Nickolay, eds., Applied Soft Computing Technologies: The Challenge of Complexity, pages 517-536, Springer-Verlag, 2006.
Designing Neural Networks Using Gene Expression Programming
Introduction
An artificial neural network is a computational device that consists of many simple connected units (neurons) that work in parallel. The connections between the units or nodes are weighted usually by real-valued weights. Weights are the primary means of learning in neural networks, and a learning algorithm is used to adjust the weights (e.g.,
Anderson 1995).
More specifically, a neural network has three different classes of units: input, hidden, and output units. An activation pattern is presented on its input units and spreads in a forward direction from the input units through one or more layers of hidden units to the output units. The activation coming into a unit from other units is multiplied by the weights on the links over which it spreads. All incoming activation is then added together and the unit becomes activated only if the incoming result is above the unit’s threshold.
In summary, the basic elements of a neural network are the units, the connections between units, the weights, and the thresholds. And these are the elements that must be encoded in a linear chromosome so that populations of such structures can adapt in a particular selection environment in order to evolve solutions to different problems.