Data mining and data classification over biomedical data are two of the most important
research fields in computer science. Among the great diversity of technique that computer science can
use for this purpose, Artificial Neural Networks (ANNs) are one of the most suited. One of the main
problems in the development of this technique, ANNs, 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,
recently, many different studies have emerged in which different ANN developmental techniques, more or less automated,
are described, all of them having several pros and cons. In this paper, the authors have focused to develop a new technique
to perform this process over biomedical data. The new technique is described in which two Evolutionary Computation
(EC) techniques are mixed in order to automatically develop ANNs. These techniques are Genetic Algorithms (GAs) and
Genetic Programming (GP). The work goes further, and the system described here allows the obtaining of simplified
networks with a low number of neurons for resolving the problems adequately. Those already existing systems that use
EC for ANN development are compared with the system proposed here. For this purpose, some of the most frequently
biomedical databases have been used in order to measure the behaviour of the system and also to compare the results
obtained with other ANN generation and training methods with EC tools. The authors have also used other databases that
are frequently used to compare this kind of method in order to obtain a more general view of the new system’s
performance. 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.
Furthermore, the system has other features like variable selection. This last feature is able to discover new knowledge
about the problems being solved.