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?

Thanks.

-- 

Chris Johnson 	[log in to unmask]
Ex SysAdmin, now, writer 	/A bargain is something you don’t need
at a price you can’t resist.
/(Franklin Jones)