Using a high-powered black-box technique to regress a one-dimensional continuous outcome against a one-dimensional continuous predictor seems misguided.
If you want to characterize how well your evolutionary learning idea works, try it on data that you've generated, where you know the "underlying math". See if you can recover the program that generated the data or one that's equivalent to it. Or try it on really big, messy data where no one knows the right answer and see if you/it can do better than the obvious competitors like SVM, k-NN, CART, etc.
The middle ground of working on an easy/messy problem, where any sane method will give you and adequate answer but there's no known ground truth, is not going to make a very compelling story.
Using a high-powered black-box technique to regress a one-dimensional continuous outcome against a one-dimensional continuous predictor seems misguided.
I don't get this. You could have a rather complicated generator for this data set. A simple regression would imply the data points were independent, but the value at time T may have [likely has] a relation to value at T-3. So it seems a good problem to me.
This is the bimonthly 'What are you working On?' thread. Previous threads are here. So here's the question:
What are you working on?
Here are some guidelines: