A computer science team at The University of Texas at Austin has found that robots evolve more quickly and efficiently¹ after a virtual mass extinction² modeled after real-life disasters such as the one that killed off the dinosaurs³. Beyond its implications for artificial intelligence, the research supports the idea that mass extinctions actually speed up evolution by unleashing⁴ new creativity in adaptations. Computer scientists Risto Miikkulainen and Joel Lehman co-authored the study published today in the journal PLOS One, which describes how simulations of mass extinctions promote novel features and abilities in surviving lineages.

 

"Focused destruction can lead to surprising outcomes," said Miikkulainen, a professor of computer science at UT Austin. "Sometimes you have to develop something that seems objectively worse in order to develop the tools you need to get better."

 

In biology, mass extinctions are known for being highly destructive, erasing⁵ a lot of genetic⁶ material from the tree of life. But some evolutionary⁷ biologists hypothesize that extinction events actually accelerate evolution by promoting those lineages that are the most evolvable, meaning ones that can quickly create useful new features and abilities. 

 

Miikkulainen and Lehman found that, at least with robots, this is the case. For years, computer scientists have used computer algorithms inspired by evolution to train simulated robot brains, called neural⁸ networks, to improve at a task from one generation to the next. The UT Austin team's innovation in the latest research was in examining how mass destruction could aid in computational evolution.

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