Nice article.<p>> However, it seems likely that the answer lies beyond biology. Karthik Raman, a former postdoc in Wagner’s lab, now at the Indian Institute of Technology Madras, has studied much the same issues of functional equivalence of different circuits not for genes but for electronic components that carry out binary logic functions. By randomly rewiring circuits of 16 components and figuring out which of them will perform particular logic operations, Raman found that they too have this evolvable topology.8 But crucially, this property appeared only if the circuits were complex enough—if they had too few components, small changes destroyed their function. “The more complex they are, the more rewiring they tolerate,” says Wagner. Not only does this open up possibilities for electronic circuit design using Darwinian principles, but it suggests that evolvability, and the corollary of creativity or innovability, is a fundamental feature of complex networks like those found in biology.<p>This should have some applications to neural networks. If the networks are complex enough then random "mutations" should allow for the same functionality as the original network. I wonder why more AI researchers aren't looking at this kind of work. Tuning network architecture by hand seems much more tedious than using evolutionary tactics to come up with novel network architectures.