I have a quick question regarding all these "iterative" methods though:

Genetic algo is an iterative optimizer and the number of iterations is a
parameter.

Many similar algos exist. For example, neural networks,
clustering(K-means), etc.

Even the simplest one - the robust linear regression is an iterative algo.

On the stock data I have, running robust linear regression using "rlm" in R
often gives warnings on failure to converge within 20 steps.

What do you do in such situation?

I tried changing the "20" steps to "200" steps... and of course the
warnings were reduced... and the overall Sharpe ratio got some
improvement... but when I tried changing it to "100" steps, the overall
Sharpe ratio was actually worse.

Therefore, my question is, how do you deal with parameters in such tools,
be it robust linear regression, neural networks, clustering, or our topic
today, the stochastic optimizer the Genetic algo...?

Is tweaking these parameters date-mining?

Do optimized versions of such parameters have out-sample stability?

On the other hand, maybe I am just overly-fearing about the danger of
data-mining?

What do you think?

Thanks a lot!

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