Design of Evolvable Biologically Inspired Classifiers

Complex systems are emergent, self-organizing and adaptive systems. They are pervasive in nature and usually hard to analyze or understand. Often they appear intelligent and show favorable properties such as resilience and anticipation. In this paper we describe a classifier model inspired by complex systems theory. Our model is a generalization of neural networks, boolean networks and genetic programming trees called computational networks. Designing computational networks by hand is infeasible when dealing with complex data. For designing our classifiers we developed an evolutionary design algorithm. Four extensions of this algorithm are presented. Each extension is inspired by natural evolution and theories from the evolutionary computing literature. The experiments show that our model can be evolutionary designed to act as a classifier. We show that our evolved classifiers are competitive compared to the classifiers in the Weka classifier collection. These experiments lead to the conclusion that using our evolutionary algorithm to design computational networks is a promising approach for the creation of classifiers. The benefits of the evolutionary extensions are inconclusive, for some datasets there is a significant performance increase while for other datasets the increase is very minimal.