The problem with curve fitting examples for GAs is that on one side there is often a more efficient way to solve the problem and, more importantly, that it assumes you know what the equation looks like.
Having used (and still) genetics algorithms to build a good approximation function for a natural phenomenon, I found that I needed to spend a lot of time crafting my chromosomes structure so that it would get a better chance of eventually solving the problem. I also had to put correlation in the fitness function because it would otherwise get stuck in a local maxima where it completely eliminated most of the inputs but the resulting curve looked nothing like the targeted one.
However I am still not completely satisfied with the approximations I get and I would be VERY interested in recommendation for more advanced reading on the subject. Anyone?
Comments
The problem with curve fitting examples for GAs is that on one side there is often a more efficient way to solve the problem and, more importantly, that it assumes you know what the equation looks like.
Having used (and still) genetics algorithms to build a good approximation function for a natural phenomenon, I found that I needed to spend a lot of time crafting my chromosomes structure so that it would get a better chance of eventually solving the problem. I also had to put correlation in the fitness function because it would otherwise get stuck in a local maxima where it completely eliminated most of the inputs but the resulting curve looked nothing like the targeted one.
However I am still not completely satisfied with the approximations I get and I would be VERY interested in recommendation for more advanced reading on the subject. Anyone?