guardian | In what appears to be the first study of its kind, computer
scientists report that an algorithm discovered more than 50 years ago in
game theory and now widely used in machine learning is mathematically
identical to the equations used to describe the distribution of genes
within a population of organisms. Researchers may be able to use the
algorithm, which is surprisingly simple and powerful, to better
understand how natural selection works and how populations maintain
their genetic diversity.
By viewing evolution
as a repeated game, in which individual players, in this case genes,
try to find a strategy that creates the fittest population, researchers
found that evolution values both diversity and fitness.
Some
biologists say that the findings are too new and theoretical to be of
use; researchers don't yet know how to test the ideas in living
organisms. Others say the surprising connection, published Monday in the
advance online version of the Proceedings of the National Academy of
Sciences, may help scientists understand a puzzling feature of natural
selection: The fittest organisms don't always wipe out their weaker
competition. Indeed, as evidenced by the menagerie of life on Earth,
genetic diversity reigns.
"It's a very different way to look at
selection," said Stephen Stearns, an evolutionary biologist at Yale
University who was not involved in the study. "I always find radically
different ways of looking at a problem interesting."
