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.
Videos
Publications
- (2016) The evolutionary origins of hierarchy. PLoS Computational Biology. (pdf)
- (2016) Identifying core functional networks and functional modules within artificial neural networks via subsets regression. Proceedings of the Genetic and Evolutionary Computation Conference. Nominated for a best paper award. (pdf)
- (2016) Does aligning phenotypic and genotypic modularity improve the evolution of neural networks?. Proceedings of the Genetic and Evolutionary Computation Conference. (pdf)
- (2015) Neural modularity helps organisms evolve to learn new skills without forgetting old skills. PLoS Computational Biology 11(4): e1004128. (pdf)
- (2014) Evolving neural networks that are both modular and regular: HyperNEAT plus the Connection Cost Technique. Proceedings of the Genetic and Evolutionary Computation Conference. 697-704. (pdf) (Source code)
- (2013) The evolutionary origins of modularity. Proceedings of the Royal Society B. 280: 20122863. (pdf) (Experimental Data, Source Code, Scripts)
- (2011) A novel generative encoding for evolving modular, regular and scalable networks. Proceedings of the Genetic and Evolutionary Computation Conference. 1523-1530. (pdf)
- (2010) Investigating whether HyperNEAT produces modular neural networks. Proceedings of the Genetic and Evolutionary Computation Conference. 635-642. (pdf)