Innovators and innovative companies must be fearless. They cannot be afraid to fail because failure comes with the territory. That is a lesson one of America’s greatest innovators and inventors, Thomas Edison, was fond of teaching. The Economist began an article on innovation with Edison’s words: “I have not failed. I have just found 10,000 ways that won’t work.” [“Don’t invent, evolve,” 8 December 2007 issue] Edison was speaking about his most famous effort, his attempt to perfect the incandescent light bulb. The focus of the article was how the “traditional trial-and-error approach,” like the one used by Edison, “can be automated by software that mimics natural selection.” The value of such software should be immediately obvious.
“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.”
Computer processor speed makes a huge difference in these efforts. The faster the computer the quicker the algorithms can churn out evolved solutions to a problem. Edison would have been amazed by the process, but he also would probably have been one of the first people to adapt it to his own purposes.
“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. Collectively, these genes comprise the product’s genome. By randomly mutating these genes and then breeding them with other, similarly mutated genomes, new offspring designs are created. These are subjected to simulated use by a second program. If a particular offspring is shown not to be up to the task, it is discarded. If it is promising, it is selectively bred with other fit offspring to see if the results, when subject to further mutation, can do even better.”
In an earlier post [Modeling Swarm Behavior], I noted that researchers are increasingly turning to nature to find answers to today’s problems. Whereas the industrial age spawned an engineering mindset — that is the belief that all problems can be solved by elegant engineering — the information age has spawned a more biological mindset — that looking to nature for answers.
“The idea of evolutionary algorithms is not new. Until recently, however, their use has been confined to projects such as refining the aerodynamic profiles of car bodies, aircraft fuselages and wings. That is because only large firms have been able to afford the supercomputers needed to mutate and crossbreed large virtual genomes—and then simulate the behaviour of their offspring—for perhaps 20m generations before the perfect design emerges. What has changed, in this as in so much else, is the availability and cheapness of computing power. According to John Koza of Stanford University, who is one of the pioneers of the field, evolutionary designs that would have taken many months to run on PCs are now feasible in days.”
I don’t know how long it took Edison to complete his 10,000 experiments with the light bulb, but it certainly was more than a few days — months I suspect. This speed of development processes has forever changed the R&D landscape.
“The result is that the range of applications to which the principles of evolutionary design are being applied is growing fast. Among those revealed at the Genetic and Evolutionary Computation Conference held in London this summer were long-life USB memory sticks, superfast racing-yacht keels, ultra-high-bandwidth optical fibres, high performance Wi-Fi antennae (evolved to avoid patent fees), cochlear implants that can optimise themselves to individual patients and a cancer-biopsy analyser that was evolved to match a human pathologist’s tumour-spotting skills.”
The article ended with the story of the high-performance Wi-Fi antennae.
“Perhaps the most cunning use of an evolutionary algorithm … is by Dr Koza himself. His team at Stanford developed a Wi-Fi antenna for a client who did not want to pay a patent-licence fee to Cisco Systems. The team fed the algorithm as much data as they could from the Cisco patent and told the software to design around it. It succeeded in doing so. The result is a design that does not infringe Cisco’s patent—and is more efficient to boot. A century and a half after Darwin suggested natural selection as the mechanism of evolution, engineers have proved him right once again.”
As computing speeds increase and computing costs decrease, evolutionary algorithms are likely to be used by more and more businesses. Who knows, they might even speed up the patent process which is almost broken. If an algorithm can be shown to be effective in working around current patents, then certification of that algorithm could replace costly and time consuming patent searches. The Patent Office needs to be creative enough to see how new technologies might be used to change the ways it has worked in the past. Using evolutionary algorithms to develop new products more effectively and efficiently is only one use, I suspect, for that technology. As it is introduced to different disciplines, evolutionary algorithms will help solve more than product development challenges. Among them could be logistics challenges, defense challenges, curriculum challenges — the list goes on and on just like life.
