I’d hammer in the morning

Why don’t more people know about (and use) genetic programming, especially for symbolic regression? GP is an approach that can be useful in all sorts of domains, for problems ranging from exploratory data analysis to design automation. SR can be a subtly informative complement to statistical modeling projects, or it can be used as a monstrously powerful open-ended exploratory machine learning engine. It rocks.

So. Do you know anything about it? [Cheating has been discouraged by eliminating outbound links from this post.]

This has become a problem for me. In seven conversations in two weeks with colleagues about work, including bosses and peers, I’ve mentioned or advised or absolutely insisted they consider GP/SR. In one case my opposite knew about GP but hadn’t considered it because he only knew about pole-balancing and stuff; in four cases they thought I was talking about genetic algorithms for parameter optimization (not that there’s anything wrong with that, but… no); in two cases I suppose they thought “symbolic regression” meant something ickily statisticky, and didn’t want to go down that road, so they played like it was some fancy newfangled numerical regression technique fad-of-the-day. Then, yesterday, in a room full of people using fast but utterly opaque SVMs to do machine learning, where the goal is to understand the system, they had thought about neither Bayesian networks nor GP/SR, both of which could tell them important things about how the system works. And in this latter case they hadn’t ever heard of SR.

I suppose now I have to do something about it.

Sigh. More in a while.