(shameless plug:) here's a paper I worked on where we used evolved neural networks to control a robot (simulated, hoping to reproduce on the real robot soon):
I've been fascinated by this idea since before I saw Karl Sim's work. My take on it now is that it is something that could really break out, but there needs to be some hard-ass work on scaling, and on how to apply the technique effectively. The hardware we have now is so off the charts compared to 1994, you would suppose that everything would be easier -- but not really. Evolution is one application that can really, really eat up every ounce of CPU you throw at it, and still scream for more.
But flops aren't the sticking point, at least not yet. What is extremely difficult is finding ways to map evolutionary techniques to real-world problems effectively. It's just such a radical way of thinking that it tends to work at cross-purposes to the 'normal' patterns of engineering.
Very interesting, I've done work evolving neural networks for biped walking. As far as I know, usually researchers use monolithic NNs, why did you use 22 separate NNs? I've never seen anything like that, opens my mind up to some new NN architectures.
All evolutionary approaches seem pretty susceptible to local optima when tackling ambitious problems like biped gaits. You maintain diversity through demes and use incremental evolution?
One thing I hate about research in CS is that rarely is the source code provided to reproduce the research. I spent 3 months unable to reproduce the seminal Reil + Husbands biped walking paper (Reil went on to found NaturalMotion, which makes natural motion for CGI in movies). I swear they used some sort of voodoo magic. Is the source available for your simulator?
http://arxiv.org/abs/0907.1839
I've been fascinated by this idea since before I saw Karl Sim's work. My take on it now is that it is something that could really break out, but there needs to be some hard-ass work on scaling, and on how to apply the technique effectively. The hardware we have now is so off the charts compared to 1994, you would suppose that everything would be easier -- but not really. Evolution is one application that can really, really eat up every ounce of CPU you throw at it, and still scream for more.
But flops aren't the sticking point, at least not yet. What is extremely difficult is finding ways to map evolutionary techniques to real-world problems effectively. It's just such a radical way of thinking that it tends to work at cross-purposes to the 'normal' patterns of engineering.
I can elaborate if anyone cares.