RE: DM: Genetic Algorithms

[Sarabjot S. Anand]

I found Warrens e-mail very interesting. It raises some very 
interesting questions - 
What is the definition of a "good algorithm"? 
Also, how can we identify "bad initial values" so that we can steer 
clear of them?

Sarab

-----Original Message-----
From:   Warren Sarle [SMTP:saswss@unx.sas.com]
Sent:   Tuesday, August 12, 1997 6:22 PM
To:     Ron Hartman
Cc:     datamine-l@nautilus-sys.com
Subject:        Re: DM: Genetic Algorithms

Ron Hartman asks:
> There's been a lot of "buzz" about using Genetic Algorithms in Data 
>Mining Applications.
> 1.) Can anyone share their experiences using these techniques to 
>solve business problems, especially in direct marketing applications?
> 2.) Are they ready for "Prime Time"?  (are there any significant 
>risks?)
> 3.) What are good resources to further my knowledge?

GAs are very fragile and require a large amount of tuning and
customization by the user; otherwise they will fail to solve even
extremely simple problems (Jennison and Sheehan 1995).  I have seen 
many
papers that show that GAs are better than other poor algorithms, or 
are
better than good algorithms with bad initial values. For example, 
there
are numerous papers showing that GAs work better than standard 
backprop
for neural nets, but practically _anything_ works better than standard
backprop.  But I have never seen any demonstration that GAs are better
than other good algorithms with reasonable initial values for any 
models
commonly used for data mining.

   Jennison, C. and Sheehan, N. (1995), "Theoretical and Empirical
   Properties of the Genetic Algorithm as a Numerical Optimizer,"
   Journal of Computational and Graphical Statistics, 4, 296-318.

-- 

Warren S. Sarle       SAS Institute Inc.   The opinions expressed here
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