Mass Extinctions can Accelerate Evolution, This Proves to be True in Case of Robots

In computer simulations, the researchers connected neural networks to simulated robotic legs with the goal of evolving a robot that could walk smoothly and stably.

Mass extinctions are known for being highly destructive, erasing a lot of genetic material from the tree of life. Some evolutionary 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. A new study has found that, at least with robots, this is the case. Researchers have found that these catastrophic events may actually speed up evolution by unleashing new creativity in adaptations.

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

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For several years, computer scientists have used computer algorithms inspired by evolution to train simulated robot brains, called neural networks, to improve at a task from one generation to the next. In computer simulations, the research team connected neural networks to simulated robotic legs with the goal of evolving a robot that could walk smoothly and stably. After several cycles of evolution and virtual extinction, the team discovered that the lineages that survived were the most evolvable and, therefore, had the greatest potential to produce new behaviors. Overall better solutions to the task of walking were evolved in simulations with mass extinctions, compared with simulations without them.

Practical applications of this research could include the development of robots that can better overcome obstacles such as robots searching for survivors in earthquake rubble, exploring Mars or navigating a minefield.

The study appeared in PLOS One.

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Mass Extinctions can Accelerate Evolution, This Proves to be True in Case of Robots

In computer simulations, the researchers connected neural networks to simulated robotic legs with the goal of evolving a robot that could walk smoothly and stably.