On the effect of network modularity on evolutionary search

Modularity is an omnipresent feature of biological neural networks. It is also a cornerstone of indirect genetic encodings and developmental evolutionary algorithms for neural networks. Modularity may give evolution the ability to reflect regularities in the environment in its solutions, thus making good solutions easier to find. Furthermore, it has been proposed that the density of highly fit solutions is higher in modular networks than in non-modular networks. In this paper we investigate how the degree of modularity in neural networks affects the search landscape for neuroevolution. We use multi-objective evolution to explicitly guide evolution towards modular and non-modular areas of network search space. We find that the fitness landscape is radically different in these different areas, but that network modularity is not accompanied by increased efficiency on a modular classification task. We therefore cannot find support for the popular assumption that modular networks are ldquobetterrdquo than non-modular networks.