Evolving Regular, Modular Neural Networks

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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 [1]. The property of modularity arises because we add a cost for connections between neurons in the network [2]. 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.

For more information, including all of my publications, press articles about my work, and additional videos, please visit JeffClune.com and subscribe to this channel. For infrequent updates about my research and to ask or discuss questions about it, please subscribe to the JeffCluneResearch Google Group athttp://goo.gl/DJ1wK

[1] Clune J, Stanley KO, Pennock RT, Ofria C (2011) On the performance of indirect encoding across the continuum of regularity. IEEE Transactions on Evolutionary Computation. 15(3): 346-367. PDF: http://goo.gl/qYHPR

[2] Clune J, Baptiste-Mouret J-B, Lipson H (2012) The evolutionary origins of modularity. ArXiv. 1207.2743v1. PDF:http://arxiv.org/pdf/1207.2743v1.pdf

 

(Source: Evolving AI Lab)

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