The Effect of Connection Cost on Modularity in Evolved Neural Networks

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.