Genetic Algorithms

Steven Johnson writes:

[A genetic algorithm] creates a random population of potential solutions, then tests each one for success, selecting the best of the batch to pass on their “genes” to the next generation, including slight mutations to introduce variation. The process is repeated until the program evolves a workable solution. Originally developed in the 1960s by John Holland at the University of Michigan, genetic algorithms are increasingly being harnessed for real-world tasks such as designing more efficient refrigerators.

Genetic algorithms make it possible for computers to do something profound, something that looks an awful lot like thinking. And that little animated figure learning how to walk showcases some design developments that permit computers to make their own decisionswithout guidance from humans.

Bill Gross [of Idealabs] believes genetic algorithms have the potential to revolutionize engineering. Instead of using software as merely a visualization tool that helps draw a contraption, he envisions genetic algorithms that can handle the entire design process. You define your organism, your genes, and your fitness function and let the software do the hard work of actually figuring it out.

“I think this is the way engineering should be done: Instead of defining your part or your circuit board, define your objective and let the software evolve the answer. Let’s say I want a table. Instead of drawing out a table, you say, My constraints are these: I want a plane at this height, with this sideways rigidity, and so on. And then you tell the software, OK, you’ve got bars, beams, screws, boltsmake the best thing you can at the lowest cost.”

Genetic algorithm advocates often talk about their software in the language of ecosystems: predators and prey, species and resources. But Gross has another idealess rain forest and more assembly line. “Let’s say you give the software access to the entire McMaster-Carr industrial supply catalog. They have 400,000 parts in stock: screws, bolts, hinges, everything. So you’ve got the whole gene pool of those parts available.” Somewhere in that mix is the machine you’re dreaming of, and simulated evolution may well be the fastest way to find it.

“You state your objectives, let the thing evolve with the optimum combination of parts at the lowest price, and the machine will be there this afternoon,” Gross says, his voice rising with excitement. “That’s an extreme exaggerationbut not that extreme!”

If I had to take time off from daily work, then the one area I’d like to work on is this!

Published by

Rajesh Jain

An Entrepreneur based in Mumbai, India.