Batali ran populations of 30 to 100 individuals on his workstations and has duplicated the results on as many as 40 nodes of SDSC's Intel Paragon. A run of several hundred generations lasts two or three days on a workstation, but because it parallelizes well, Batali expects to cut the time to several hours on the Paragon. Then the child joins the population to grow up and learn the language. At first, the individuals communicate with essentially random messages. As the population evolves, the messages become shorter as the individuals converge on the minimal sequence that meets all the grammar rules and still uniquely represents the meaning.
Different languages arise in each simulation.
However, it's not very human-like. But like a true language, the networks can understand some novel meanings. Batali's simulations suggest that evolution and learning play two distinct and vital roles. The similarities to human language stem from how learning and evolution interact. Evolution dominates early and establishes the broad outline from which languages arise.