Best fit curve for randomized data

I'm writing an evolution algorithm program to find best fit curves for random data sets.

This picture should give you an idea of what i'm doing...

http://www.vbforums.com/attachment.p...postid=1738801
It shows a randomised set of test data that is based on a unknown 3rd order polynomial. The red line is what I want my algorithm to generate, using a genetic algorithm.

So i have something to aim for, how long typically would it take a decent traditional algorithm to find a best fit curve for such data (given 1000 points of test data). I plan to support up to 11th order polynomials! Are we talking minutes? Seconds? Milliseconds? Hours?

I know this is a bit vague, but I'm hoping someone has some experience in this area and could impart some wisdom. :)

I wrote such a program, but for a smaller number of data points, on the order of 24. My program supported polynomial solutions up to the 12th degree. For this program, run seems to be
"instantaneous". Good luck with yours!