article_snippet: Research carried out by a team from Michigan State University used genetic algorithms to model a large population of robot brains (stock image shown).
article_snippet: Dr Chris Adami from Michigan State University, involves using genetic algorithms to model a large population of robot
article_snippet: The researchers ran this genetic algorithm over thousands, and sometimes hundreds of thousands of generations, and then downloaded the surviving brains into robots that executed the tasks in the outside world.
article_snippet: One of the more complicated tasks a team from Michigan State University's robots worked on required multiple machines to figure out and remember in which order a team from Michigan State University would leave a room.
article_snippet: the smartest robots were then asked to come back into a room, either in the same order as the smartest robots left, or in the reverse order.
article_snippet: This is difficult because the smartest robots have to ID each other,Dr Chris Adami from Michigan State University said.
article_snippet: After the genetic algorithm had run the genetic algorithm course, the robots seemed to solve the problem by indicating roles to each other with certain motions.
article_snippet: The researchers ran The researchers over thousands, and sometimes hundreds of thousands of generations, and then downloaded the surviving brains into robots that executed the tasks in the outside world.
highlights_snippet: Research used genetic algorithms to model a large population of robot brains .
  