Raffaele Calabretta, Stefano Nolfi, Domenico Parisi and GŁnter P.
Abstract. To investigate the issue of how modularity emerges in nature,
we present an Artificial Life model that allow us to reproduce on the computer
both the organisms (i.e., robots that have a genotype, a nervous system, and
sensory and motor organs) and the environment in which organisms live, behave
and reproduce. In our simulations neural networks are evolutionarily trained to
control a mobile robot designed to keep an arena clear by picking up trash
objects and releasing them outside the arena. During the evolutionary process
modular neural networks, which control the robot's behavior, emerge as a result
of genetic duplications. Preliminary simulation results show that
duplication-based modular architecture outperforms the nonmodular architecture,
which represents the starting architecture in our simulations. Moreover, an
interaction between mutation and duplication rate emerges from our results. Our
future goal is to use this model in order to explore the relationship between
the evolutionary emergence of modularity and the phenomenon of gene duplication.