Research Updates, Press Coverage, Robot Videos, and Positions Available
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Jeff Clune
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Mar 10, 2013, 3:35:49 PM3/10/13
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Hello all,
Here is an update on major developments recently
I published a new paper with Jean-Baptiste Mouret and Hod Lipson on why modules evolve in biological networks, such as neural networks, genetic regulatory networks, metabolic networks and protein-protein interaction networks. Clune J, Mouret J-B, Lipson H (2013) The evolutionary origins of modularity. Proceedings of the Royal Society B. 280: 20122863. http://dx.doi.org/10.1098/rspb.2012.2863
Many press outlets wrote about that work, including
A team of us published a paper showing that HyperNEAT produces the best gait yet for a real robot.
On the robot platform we tested on, HyperNEAT outperformed nine other machine learning algorithms from three previous papers to produce the fasted gait yet recorded for this robot. I think this result is great for our community because it shows that HyperNEAT can perform great--and outperform traditional machine learning algorithms--on difficult, real-world engineering problems, instead of just in simulation and on diagnostic/toy problems.
Cite: Lee S, Yosinski J, Glette K, Lipson H, Clune J. 2013. Evolving gaits for physical robots with the HyperNEAT generative encoding: the benefits of simulation. Applications of Evolutionary Computing. Springer.