As programmers know, modularity is critical to making reusable, adaptable software. However, modularity is not instinctive for beginners and must be learned via painful training. Biology faces a similar problem: modularity is useful to make species more adaptable, but how did it evolve in the first place? Surprisingly, computational simulations of 25,000 generations of evolution reveal that modularity does not evolve because it makes organisms more adaptable. Instead, modularity evolves as a by-product for selection to reduce the "wiring costs" of a network [1]. The discovery greatly advances research into evolving artificially intelligent robots [2], a field where the inability to evolve modular designs has long been thought to be a key roadblock [3] to evolving truly complex, intelligent neural networks.<p>The paper [4] was published in the Proceedings of the Royal Society. You can also watch modularity evolve in this video. [5]<p>[1] http://phenomena.nationalgeographic.com/2013/01/30/the-parts-of-life/
[2] http://www.fastcompany.com/3005313/evolved-brains-robots-creep-closer-animal-learning
[3] http://www.technologyreview.com/view/428504/computer-scientists-reproduce-the-evolution-of-evolvability/
[4] http://rspb.royalsocietypublishing.org/content/280/1755/20122863
[5] http://www.youtube.com/watch?feature=player_embedded&v=SG4_aW8LMng