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

Evolving Artificial Neural Networks That Are Both Modular and Regular

This video accompanies the following paper(s):

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.

This video accompanies the following paper(s):

Talk at the Santa Fe Institute: Two Projects in BioInspired AI. Evolving regular, modular, hierarchical neural networks, and robot damage recovery.

This video accompanies the following paper(s):

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

This video accompanies the following paper(s):

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.

This video accompanies the following paper(s):

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

This video accompanies the following paper(s):

Talk summarizing "Evolving Neural Networks That Are Both Modular and Regular: HyperNEAT Plus the Connection Cost Technique"

Talk given by Joost Huizinga at the 2014 GECCO Conference in Vancouver, British Columbia.

This video accompanies the following paper(s):

Evolving Artificial Neural Networks That Are Both Modular and Regular

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.

Talk at the Santa Fe Institute: Two Projects in BioInspired AI. Evolving regular, modular, hierarchical neural networks, and robot damage recovery.

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

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.

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

Talk summarizing "Evolving Neural Networks That Are Both Modular and Regular: HyperNEAT Plus the Connection Cost Technique"

Talk given by Joost Huizinga at the 2014 GECCO Conference in Vancouver, British Columbia.


Publications