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From: shapper <mdmoura@gmail.com>
Newsgroups: comp.soft-sys.matlab
Subject: Re: Find Minimum
Date: Mon, 11 Feb 2008 14:28:51 -0800 (PST)
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On Feb 11, 8:55 pm, rober...@ibd.nrc-cnrc.gc.ca (Walter Roberson)
wrote:
> In article <Xns9A419D4C96F57scottseidmanminds...@130.133.1.4>,
> Scott Seidman  <namdiestt...@mindspring.com> wrote:
>
> >rober...@ibd.nrc-cnrc.gc.ca (Walter Roberson) wrote in news:foqah7$fds$1
> >@canopus.cc.umanitoba.ca:
> >> The original poster did not ask for an engineering "close enough"
> >> solution: the original poster asked to find the parameters that minimize
> >> (over an infinite domain) the un-examinable function. You can
> >> graph all the finite subsets you want, but you cannot hide from
> >> infinity.
> >So, once again you assert that there is no way to optimize an unconstrained
> >3-parameter problem with global minima.  Many minimization problems are not
> >looking for a microscopic dirac in infinite 3D.  I suppose we could assume
> >that's that kind of function the OP is presenting, or we could assume that
> >his problem is not of that sort, and is much more mundane.
>
> Assuming that the original poster's problem is "not of that sort"
> and to be globally optimizable with any fixed algorithm is to place
> (mathematically) quite large restrictions on the details of the
> black-box function, violating the original poster's assertion that
> nothing was known of the function beyond how to evaluate it.
>
> Your claim that the original poster's problem *as originally stated*
> could be solved by a minimization algorithm was wrong.
>
> Even if you had an algorithm that (for example) after five
> function evaluations was able to predict a parameter combination that
> was unsurpassed in another billion explorations of the parameter
> space, then because of the black-box nature of the function,
> you would never be sure that you had found the global minima
> (which might take quadrillions of evaluations to find a better minima).
> You might perhaps appear to converge very quickly, but if it is a
> black box problem with no known constraints on its internal contents,
> then you never know when to stop evaluating to be sure you had the
> global minima.
> --
>   "You can't hit what you can't see."  -- Walter "The Big Train" Johnson

Hi,

Evaluate my function at all 4,000,000? Now I am scared ... :-)

  How could I create such a script that would make it possible for me
to evaluate all the solutions.
  I need a flexible approach because MyFun can have 3 or MORE inputs
with their constraints.
  It would be great if I could use this in different functions.

It seems that the function being a Black Box is also a problem.

A new simplification which I hope it helps.

Basically, inside the Black Box I have a set or rules that:

  Decide when to Buy/Sell components for company's factory.
  Decide the sizes components for Buy/Sell.
  ...

The rules are based in Moving Averages, Oscillators maybe a few Fuzzy
rules.

So what I need is to "tune" the input parameters to get the optimal
value of my output.

When I said I didn't know what was going on in my function I meant
that inside my function I could have 3 or 10 rules and the rules could
include a simple Moving Average (Rule would use cross), an Oscillator
(rule would use cross a certain level), ...

The calculations inside the function are not very complex ...

Can't I do this with GA?
Enumeration would be a good solution to confirm the solution obtained
with GA on the first simulations.

Does this help?

Thanks,
Miguel