Evolving Modular, Regular Neural Networks

Our work shows that networks evolve to be modular when there is a cost for network connections: such modularity improves evolvability. Modularity is ubiquitous in natural networks, such as genetic regulatory networks, protein networks, and the neural networks that make up animal brains. It is thought that such modularity is a key reason natural networks are so complex and functional. Evolving modular artificial neural networks should thus allow the evolution of more complex phenotypes, and may be a prerequisite to evolving truly artificially intelligent robots.