Abstract
Modularity, often observed in biological systems, does not
easily arise in computational evolution. We explore the effect
of adding a small fitness cost for each connection between
neurons on the modularity of neural networks produced by
the NEAT neuroevolution algorithm. We find that this connection cost does not increase the modularity of the best network produced by each run of the algorithm, but that it does
lead to increased consistency in the level of modularity produced by the algorithm.