Hello,

I'm looking for the best multi-parameter optimization methods in R that:

1) Use brute force (non-genetic parameter value selection, i.e. tests all
parameter combinations...) to see results for all combinations of parameter
values requested by the user.

2) Use a genetic algorithm (skips some parameter value combinations via a
genetic algorithm selection process...) to speed up the optimization
process.

Yes, #1 and #2 above conflict, thus I would like to test both types of
optimizations.

I am optimizing trading program parameters over time series data (package
timeSeries) and I'd like to avoid re-inventing the wheel (save myself some
coding time). I have a back testing function that I can use in the
optimization function.

I have been here:
http://cran.r-project.org/web/views/Optimization.html

And it is looking like optim() is my best bet; however, I want to double
check with the community first.

Thanks!

-- 
Ben Nachtrieb
M.S.Finance / Investment Management, University of Denver
Beta Gamma Sigma member

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