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Hello,
This works for smooth functions. But when function to be optimized is choppy problems start.
Best regards,
Tomasz Janeczko
amibroker.com
----- Original Message ----- 
From: "Joseph Landry" <jelandry@xxxxxxxxxxxxx>
To: <amibroker@xxxxxxxxxxxxxxx>
Sent: Thursday, November 13, 2003 2:11 PM
Subject: [amibroker] Re: Help on automating optimization (genetic algorithms)
> I like what Fred suggests about working from coarse to 
> fine..mathematical programming systems(what we called the products 
> that IBM and others sold then) that perform optimizations on general 
> functions proceed that way, except they do it with a little more 
> sophistication...Back in the 60s when studying Chemical Plant 
> optimizations (where your excursions and boundaries were limited by 
> a physical plant process)... I first heard about Fibonacci as a 
> search technique (this has probably been replaced by newer methods 
> say in database searches, or Google searches).  Like a binary table 
> search but more advanced because it takes fewer of steps when you 
> proceed say with Fibonacci numbers to step across a range of say 0 
> to 100. Of course with all the computing power now a days, we could 
> do brute force optimizations without much loss in time by hitting 
> every step. (;>.
> 
> We also had an saying  that one should get the chemical plant to 
> yield information beside it's main objective, which were products. I 
> can see the steps in optimizations as being a study where you learn 
> the how the function(your system) responds as you work it from 
> coarse to fine, to find the global optimum. 
> Best regards
> Joe   
> 
> 
> --- In amibroker@xxxxxxxxxxxxxxx, "Fred" <fctonetti@xxxx> wrote:
> > One way, not necessarily the best way to do this is to work from 
> > coarse to fine ...
> > 
> > For example lets assume you have 4 variables that could have 
> values 
> > of 0 to 100 which you'd like to simultaneously optimize based on 
> > something to achieve the best results yielding an integer for each 
> > variable.  100^4 of course might be able to be done but you might 
> > have to take a week off to have the answer.  But you might also be 
> > able to get at an answer more quickly without overlooking 
> potentially 
> > good results by first running the optimizations as 0 to 100 by 25, 
> > seeing where the best results are and then scaling down the range 
> and 
> > the increment for the next pass i.e. if the first run shows 50 as 
> the 
> > best place to be then on pass 2 you might want to set the values 
> as 
> > 10 to 90 by 20 etc.  If you are careful how you write the code for 
> > the optimization statements this can be done in one pass from an 
> > AB/AA/AFL perspective.
> > 
> > --- In amibroker@xxxxxxxxxxxxxxx, "Gary A. Serkhoshian" 
> > <serkhoshian777@xxxx> wrote:
> > > Hi all,
> > >  
> > > I should preface this e-mail by saying that I'm not trying to 
> > create a Rube Goldberg as I can go through the process ourlined 
> below 
> > manually.  So, if what I'm suggesting requires a Rube Goldberg 
> type 
> > solution let me know, and I'll let it go.
> > >  
> > > Essentially what I'm doing with optimizations over multiple 
> > variables is prioritizing variables on importance, and optimizing 
> one 
> > variable at a time leaving the others static.  I just keep 
> iterating 
> > through all the variables until things stabilize in terms of 
> standard 
> > measures.  The cocktail party term is genetic algorithms, but 
> between 
> > us it's necesary as optimizing over multiple variables (4+)  will 
> > either take too long or worse cause AmiBroker to crash due to 
> memory 
> > issues.
> > >  
> > > Here's the punchline.  Can you all give some input on ways to 
> > program this in afl so as to automate the process outlined above 
> > rather than me manually iterating through as it is labor intensive.
> > >  
> > > Many thanks,
> > > Gary
> > > 
> > > 
> > > ---------------------------------
> > > Do you Yahoo!?
> > > Protect your identity with Yahoo! Mail AddressGuard
> 
> 
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