## ECJ-INTEREST-L@LISTSERV.GMU.EDU

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 Subject: Re: Building ECJ. From: Chris Johnson <[log in to unmask]> Reply To: ECJ Evolutionary Computation Toolkit <[log in to unmask]> Date: Sat, 6 May 2017 15:34:39 -0400 Content-Type: multipart/alternative Parts/Attachments: text/plain (1647 bytes) , text/html (2388 bytes)
```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.

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