The existence of modules is recognized at all levels of the biological hierarchy. In order to understand what modules are, why and how they emerge and how they change, it would be necessary to start a joint effort by researchers in different disciplines (evolutionary and developmental biology, comparative anatomy, physiology, neuro- and cognitive science). This is made difficult by disciplinary specialization. In this paper we claim that, because of the strong similarities in the intellectual agenda of artificial life and evolutionary biology and of their common grounding in Darwinian evolutionary theory, a close interaction between the two fields could easily take place. Moreover, by considering that artificial neural networks draw an inspiration from neuro- and cognitive science, an artificial life approach to the problem could theoretically enlarge the field of investigation. The present work is the first one in which an artificial life model based on neural networks and genetic algorithms is used to understand the mechanisms underlying the evolutionary origin of modularity. An interesting problem that we will address in this paper is whether modules that start as repeated elements because of genetic duplication can develop to become specialized modules. A linear regression statistical analysis performed on simulation data confirms this hypothesis and suggests a new mode for the evolution of modularity.
