  Data mining and data classification over biomedical data are two of the most
important research fields in computer science. Among the great diversity of
techniques that can be used for this purpose, Artifical Neural Networks (ANNs)
is one of the most suited. One of the main problems in the development of this
technique is the slow performance of the full process. Traditionally, in this
development process, human experts are needed to experiment with different
architectural procedures until they find the one that presents the correct
results for solving a specific problem. However, many studies have emerged in
which different ANN developmental techniques, more or less automated, are
described. In this paper, the authors have focused on developing a new
technique to perform this process over biomedical data. The new technique is
described in which two Evolutionary Computation (EC) techniques are mixed to
automatically develop ANNs. These techniques are Genetic Algorithms and Genetic
Programming. The work goes further, and the system described here allows to
obtain simplified networks with a low number of neurons to resolve the
problems. The system is compared with the already existent system which also
uses EC over a set of well-known problems. The conclusions reached from these
comparisons indicate that this new system produces very good results, which in
the worst case are at least comparable to existing techniques and in many cases
are substantially better.
