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304 Appendix B. CD Tools

B.5 Genetic Neural Networks

The Neuro-Genetic program allows the user to perform classification of patterns
using multilayer perceptrons (up to three layers) trained with the back-propagation
or with a genetic algorithm. It is therefore possible to compare both training
methods.
In order to use Neuro-Genetic, an MLP classification project must first be
defined (go to menu &oject and select New Project or click the appropriate button
in the toolbar), specifying the following items:
1. Data file. This is a text file with the information organized by rows and
columns, separated by tabs. Each column corresponds to a network input or
output and each row corresponds to a different pattern.
2. Training set and test set. To specify the training set input values, the initial and
final columns and the initial and final rows in the data file should be indicated.
For the output values, only the initial and final columns are needed (the rows are
the same). The same procedure must be followed for the test set.
3. Training procedure (genetic algorithm or back-propagation).
4. Neural network architecture. It is possible to specify a network with 1 or 2
hidden layers . One linear output layer (with the purpose of scaling the output
value) can also be specified. If the check box corresponding to the linear output
layer is checked, the number of neurons for the first hidden and second hidden
layers must be indicated.
5. Initial weights. The complete path for the initial weight file must also be filled
in, or else a file with random weights must be generated (by clicking the
appropriate button). This file includes all the weights and bias values for the
defined neural network. It is a text file, with extension .wgt, containing the
weight values in individual rows, ordered as:
w,q, where

n varies from 1 to the number of layers (includes output layer);
i varies from 1 to the number of neurons in that layer;
j varies from 0 (bias value) to the number of inputs or neurons in the previous
layer (if n>l).

Once a project has been defined, it can be saved for later re-use with the menu
option Save Project. Network training can be started (or stopped) using the
respective buttons. No validation set is used during training, therefore the user
must decide when to stop the training, otherwise training stops when the specified
error goal or the maximum number of iterations is reached.
Once the training is complete the user can inspect the weights and the predicted
values and errors in the training set and test set. It is also possible to visualize the
error evolution during the training procedure by selecting the Errors Chart option.
The following parameters must be indicated independently of the training
technique: