Modularity in evolved artificial neural networks

Abstract

The relationship between evolution (genetic & developmental processes of an evolutionary system) and modularity (its support for modular structures) is explored. Modules are defined as structures with common origin; either evolutional or developmental. In the former case, processes supporting modularity operate on the phylogenetic level, in the latter, on the ontogenetic level. Three such processes are identified (duplication, divergence, covergence). The existence of these processes determine the system’s support for modularity. Modules are analysed in the particular context of artificial neural networks (ANNs), where they appear as subnetworks. Gruau’s cellular developmental encoding is used as an example, and an extension is proposed which better supports modularity.