The behavior of ants is certainly amazing. But take a look at behavior based robotics to see how well it scales. You won't find something as capable as, say, a

The behavior of ants is certainly amazing. But take a look at behavior based robotics to see how well it scales. You won't find something as capable as, say, a cat. At least not by human design. The system needed to evolve such behaviors through EA would need to be quite complicated. We've seen systems that can do a pretty good job on mobility -- like that flopping starfish from Cornell. But locomotion is exactly the type of problem that has a very precise, quantitative objective function. But perhaps I was too broad. I should have said orderable, which is really all that matters. You have to know something is better than something else. But you can't just take a euclidean distance in high dimensional objectives. Things just don't work like that. A real-world example: work-life balance. The two parts are just not really comparable, making optimization difficult. That's what I'm talking about. Also, EA has lots of potential for lots of areas, but you have to compare it to other methods of solving a problem to talk about quality of the approach. Machine Learning offers a host of methods. Here is a great list of them http://www.cs.cmu.edu/~awm/ http://www.cs.cmu.edu/~awm/