The evolutionary origins of modularity

Author(s): 
Clune J
Mouret JB
Lipson H
Year: 
2013
Abstract: 

A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks—their organization as functional, sparsely connected subunits—but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes.


Why does modularity evolve? The evolutionary origins of modularity

Engineered and evolved things are organized in modules (e.g. organs or car parts), yet why modularity evolves remains one of biology's most important open questions. This paper shows for the first time that modularity evolves not because it speeds up adaptation, as the leading theory holds, but because it saves on "wiring costs". Connections in biological networks have costs (e.g. building and maintaining them), and modular networks use fewer connections. These results help explain the ubiquitous modularity in biological networks, such as genetic modules and the neural modules in our brains, and will help scientists evolve smarter artificial intelligence. Interestingly, the modular networks that evolve do adapt faster, meaning that adaptation is a consequence of modularity, not its main cause.

Evolving Regular, Modular Neural Networks

I (Jeff Clune) summarize my research into evolving modular, regular neural networks, which are digital models of brains. The property of regularity is produced by using HyperNEAT, a generative encoding based on concepts from developmental biology. The property of modularity arises because we add a cost for connections between neurons in the network. Evolving structurally organized neural networks, including those that are regular and modular, is a necessary step in our long-term quest of evolving computational intelligence that rivals or surpasses human intelligence.

Non-Adaptive Evolvability

Evolving Modular Networks: Video for "The Evolutionary Origins of Modularity"

Why does modularity evolve? The evolutionary origins of modularity

Engineered and evolved things are organized in modules (e.g. organs or car parts), yet why modularity evolves remains one of biology's most important open questions.

Evolving Regular, Modular Neural Networks

I (Jeff Clune) summarize my research into evolving modular, regular neural networks, which are digital models of brains. The property of regularity is produced by using HyperNEAT, a generative encoding based on concepts from developmental biology.

Non-Adaptive Evolvability

Evolving Modular Networks: Video for "The Evolutionary Origins of Modularity"

Pub. Info: 
Proceedings of the Royal Society B. 280: 20122863
BibTeX: 

@article{clune2013evolutionary,
title={The evolutionary origins of modularity},
author={Clune, Jeff and Mouret, Jean-Baptiste and Lipson, Hod},
journal={Proceedings of the Royal Society b: Biological sciences},
volume={280},
number={1755},
pages={20122863},
year={2013},
publisher={The Royal Society}
}