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ANN Development with EC Tools: An Overview
Abstract
Among all of the Artificial Intelligence techniques, Artificial Neural Networks (ANNs) have shown to be a very powerful tool (McCulloch & Pitts, 1943) (Haykin, 1999). This technique is very versatile and therefore has been succesfully applied to many different disciplines (classification, clustering, regression, modellization, etc.) (Rabuñal & Dorado, 2005). However, one of the greatest problems when using ANNs is the great manual effort that has to be done in their development. A big myth of ANNs is that they are easy to work with and their development is almost automatically done. This development process can be divided into two parts: architecture development and training and validation. As the network architecture is problem-dependant, the design process of this architecture used to be manually performed, meaning that the expert had to test different architectures and train them until finding the one that achieved best results after the training process. The manual nature of the described process determines its slow performance although the training part is completely automated due to the existence of several algorithms that perform this part. With the creation of Evolutionary Computation (EC) tools, researchers have worked on the application of these techniques to the development of algorithms for automatically creating and training ANNs so the whole process (or, at least, a great part of it) can be automatically performed by computers and therefore few human efforts has to be done in this process
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