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Researchers simulate 25,000 generations of evolution, boost artificial intelligence

Engineers and robotics researchers from Cornell University have solved a biological mystery and boosted artificial intelligence. They recently simulated 25,000 generations of evolution within computers in their quest to answer why biological networks are likely to be organized as modules. This is a discovery that will lead to a better understanding of the evolution of complexity, according to a news release from Cornell.

Researchers believe that their discovery will also help evolve artificial intelligence, so robot brains can obtain the “grace and cunning” of animals.

Many biological networks are organized into modules – dense clusters of interconnected parts within a complex network. For years biologists have sought an answer to why humans, bacteria and other organisms evolved in a modular fashion. Biologists Richard Dawkins, Günter P. Wagner, and the late Stephen Jay Gould labeled the question of modularity as key to the debate over “the evolution of complexity.”

Scientists have long thought that modules evolved because entities that were modular could respond to change more rapidly, and therefore had an adaptive advantage over non-modular entities. Researchers think that this theory may not be enough to explain the evolution of complexity.

Engineers and robotics researchers found that evolution creates modules not because they generate more adaptable designs, but because modular designs have fewer and shorter network connections, which are “costly” to construct and maintain.

Researchers tested their theory by simulating the evolution of networks with and without a cost for network connections.

“Once you add a cost for network connections, modules immediately appear. Without a cost, modules never form. The effect is quite dramatic,” says Jeff Clune, a former visiting scientist at Cornell University.

Their findings may help give a reason for the near-universal presence of modularity in a diverse range of biological networks.

“Being able to evolve modularity will let us create more complex, sophisticated computational brains,” adds Clune.

“We’ve had various attempts to try to crack the modularity question in lots of different ways,” says Hod Lipson, Cornell associate professor of mechanical and aerospace engineering. “This one by far is the simplest and most elegant.”

The study’s findings were recently published in the journal Proceedings of the Royal Society.