Ok. Went through all four ECJ tutorials. Played with them a
little. Learned a bit more about Java. Still don't like it.
I do have a question. If this question belong elsewhere, please
point me there and there I will ask it.
I had to wait until tutorial4 because I'm getting into symbolic
regression modeling. You have a bunch of data, these days quite
often peta-data or more. You want a mathematical model that has the
attributes of describing the data and hopefully making, successful,
predictions.
Here's my issue. If I remember correctly, it is possible to come up
with a polynomial of degree n-1, where n is the number of data
points, that precisely passes through every data point in your data
set. However, the odds of such a polynomial having any descriptive
truths about the data, let alone predictive capabilities, are pretty
small as a rule.
What you want is probably something more in the way of a spline
function, at the least, with the wonderful piece wise continuous
differential hoo-ha yada yada they taught back in the Precambrian
era when I studied math.
I googled Koza fitness tests. I've seen similar for symbolic
regression. Many look a lot like a statistical variance. Maybe I'm
missing something here, probably am. Looks to me like my
aforementioned n-1 degree polynomial would fit like the proverbial
glove with a 0 fitness measure. What's to prevent such a symbolic
regression system, ECJ or other, from simply coming up with a
useless polynomial?