As the knowledge economy migrates away from static knowledge, such as fixed information, and develops the ability to deal more and more with meta-processes, it’s developing tools such as evolutionary computing. Evolutionary computing finds an answer by massive numbers of trial and error iterations, eventually ‘evolving’ a design that meets a set of criteria. In one particular case, that criteria included avoiding patented technology. In that particular case, the ‘evolved’ design was more efficient than the patented one. (From the Economist: Don’t Invent, Evolve)
“I HAVE not failed. I have just found 10,000 ways that won’t work.” So said Thomas Edison, the prolific inventor, speaking of his laborious attempts to perfect the incandescent light bulb. Although 10,000 trial-and-error attempts might sound a little over the top, an emerging technique for developing inventions knocks even Edison’s exhaustive approach into a cocked hat. Evolutionary design, as it is known, allows a computer to run through tens of millions of variations on an invention until it hits on the best solution to a problem.
As its name suggests, evolutionary design borrows its ideas from biology. It takes a basic blueprint and mutates it in a bid to improve it without human input. As in biology, most mutations are worse than the original. But a few are better, and these are used to create the next generation. Evolutionary design uses a computer program called an evolutionary algorithm, which takes the initial parameters of the design (things such as lengths, areas, volumes, currents and voltages) and treats each like one gene in an organism.
It’s also another datapoint that shows how the PC has the potential to level the playing field between smaller and larger organizations. Here it is, doing something that until very recently was only within the reach of a few large corporations or government agencies:
