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.
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 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
 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)