Mass extinctions may actually speed up evolution, suggests a new study which found that computer simulated robots evolve more quickly and efficiently after a virtual mass extinction modelled after real-life disasters such as the one that killed off the dinosaurs.
Beyond its implications for artificial intelligence, the research supports the idea that mass extinctions actually speed up evolution by unleashing new creativity in adaptations, researchers said.
“Focused destruction can lead to surprising outcomes,” said co-author Risto Miikkulainen, a professor of computer science at The University of Texas at Austin in US.
“Sometimes you have to develop something that seems objectively worse in order to develop the tools you need to get better,” said Miikkulainen.
Mass extinctions are known for being highly destructive, erasing a lot of genetic material from the tree of life.
But some evolutionary biologists hypothesise 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.
For years, 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.
The innovation in the latest research was in examining how mass destruction could aid in computational evolution.
In computer simulations, they connected neural networks to simulated robotic legs with the goal of evolving a robot that could walk smoothly and stably.
As with real evolution, random mutations were introduced through the computational evolution process. The scientists created many different niches so that a wide range of novel features and abilities would come about.
After hundreds of generations, a wide range of robotic behaviours had evolved to fill these niches, many of which were not directly useful for walking.
Then the researchers randomly killed off the robots in 90 per cent of the niches, mimicking a mass extinction.
After several such cycles of evolution and extinction, they found that the lineages that survived were the most evolvable and, therefore, had the greatest potential to produce new behaviours.
Overall, better solutions to the task of walking were evolved in simulations with mass extinctions, compared with simulations without them.
Practical applications of the 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) and human-like game agents.
The study was published in the journal PLOS One.