Adaptation and Learning for Pattern Recognition: A Comparison Between Neural and Evolutionary Computation

The work presented in this chapter is aimed at developing self-governing artificial systems that are able to operate in complex, uncertain and dynamic application domains, by mimicking the learning and adaptation capabilities exhibited by biological systems. For this purpose, we are exploring the possibility offered by the artificial neural networks and evolutionary computation paradigms for automatically extracting the set of prototypes describing the variability present in a data set. In particular, this chapter reports the results of an experiment designed for comparing the performance exhibited by a Learning Vector Quantization network and an Evolutionary Learning System using a Breeder Genetic Algorithm. For the sake of generality, the comparison has been performed on a complex classification problem obtained by generating a synthetic data set according to the distribution of distributions model.