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Database Analysis with ANNs by means of Graph Evolution
Abstract
Traditionally, the development of Artificial Neural Networks (ANNs) is a slow process guided by the expert knowledge. This expert usually has to test several architectures until he finds one suitable for solving a specific problem. This makes the development of ANNs a slow process in which the expert has to do much effort. This chapter describes a new method for the development of Artificial Neural Networks, so it becomes completely automated. Since ANNs are complex structures with very high connectivity, traditional algorithms are not suitable to represent them. For this reason, in this work graphs with high connectivity that represent ANNs are evolved. In order to measure the performance of the system and to compare the results with other ANN development methods by means of Evolutionary Computation (EC) techniques, several tests were performed with problems based on some of the most used test databases in Data Mining. These comparisons show that the system achieves good results that are not only comparable to those of the already existing techniques but, in most cases, improve them.
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