Sorry that no one has gotten back to you. I guess that may be some indicator that no one here knows much about GP. Me included. I only have peripheral knowledge on the subject from judging a few projects at science fairs. But, given that, I can tell you that there are at least a few people out there using it. If nothing else, then for research projects.
I did some internet searching to educate myself a little more before replying to you. I found a site that appears to have a really good tutorial for GP. See http://www.geneticprogramming.com/Tutorial/index.html
. In particular, note the flowchart they have for genetic programs. Not terribly complicated as you look at it but when you start reading the details it gets pretty hairy.
Also note that it makes a distinction between genetic programming and genetic algorithms. Genetic algorithms try to find an optimum data set. Genetic programming is a specific niche of GA where the data set is made up of programs. That is, GP attempts to find an optimum program from a population of random programs that mutate over time. You keep evolving the program until you find the best program that works. Looking at the description you gave, you appear to be describing GA rather than GP.
Carnegie Mellon has an FAQ on GA at http://www.cs.cmu.edu/Groups/AI/html/fa ... c/top.html
. In particular, look at the answer to What’s a Genetic Algorithm at http://www.cs.cmu.edu/Groups/AI/html/fa ... doc-2.html
Depending on how complicated your problem is, I don’t think it would be too difficult to write your own GA, which means you wouldn’t have to depend on other simulation software. I also think run-of-the-mill computers of today would be able to handle a simple-to-medium level GA. It all comes down to what is the problem you’re trying to solve.
Take a look at these links and click around those sites for additional info. If you have more questions after that, post them back here and we’ll try to help out.