Raffaele Calabretta, Stefano Nolfi, Domenico Parisi and GŁnter P.
Abstract. 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.