Raffaele Calabretta, Stefano Nolfi, Domenico Parisi and Günter
P. Wagner
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.