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RE: [amibroker] Re: Help on automating optimization (genetic algorithms)



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[DT] <FONT 
face="Courier New" size=3>I do not think you save any time, optimization is an 
internal loop, as far as I understand...Dimitris 
Tsokakis
<FONT 
face="Courier New" color=#0000ff size=3>Dt, the procedure I outlined did not 
make any use of the AB Optimizer so its qualities have no impact on the 
application.  
<FONT 
face="Courier New" color=#0000ff size=3> 
[DT] 
The results of cascade optimizations will not give the optimal 
result...
<FONT 
face="Courier New" color=#0000ff size=3>Whether this would be an acceptable 
optimization technique would depend on the types of variables that are being 
optimized, this is a decision to be made by the user. 

<FONT 
face="Courier New"><FONT face=Arial 
size=2> 
<FONT 
face="Courier New" color=#0000ff size=3>herman


  <FONT face=Tahoma 
  size=2>-----Original Message-----From: DIMITRIS TSOKAKIS 
  [mailto:TSOKAKIS@xxxxxxxxx]Sent: November 13, 2003 1:47 
  PMTo: amibroker@xxxxxxxxxxxxxxxSubject: [amibroker] Re: 
  Help on automating optimization (genetic 
  algorithms)Gary,The procedure you describe does 
  not make any sense.In the simple 
  examplex=Optimize("x",10,10,100,10);b=Optimize("b",10,10,50,10);s=Optimize("s",50,50,90,10);Buy=Cross(StochD(x),b);Sell=Cross(s,StochD(x));the 
  optimal solution is [30,20,80]If I optimize first the x, 
  iex=Optimize("x",10,10,100,10);b=10;//Optimize("b",10,10,50,10);s=50;//Optimize("s",50,50,90,10);Buy=Cross(StochD(x),b);Sell=Cross(s,StochD(x));[optimal 
  x=80]then the b, 
  iex=80;//Optimize("x",10,10,100,10);b=Optimize("b",10,10,50,10);s=50;//Optimize("s",50,50,90,10);Buy=Cross(StochD(x),b);Sell=Cross(s,StochD(x));[optimal 
  b=10]and finally the s, 
  iex=80;//Optimize("x",10,10,100,10);b=10;//Optimize("b",10,10,50,10);s=Optimize("s",50,50,90,10);Buy=Cross(StochD(x),b);Sell=Cross(s,StochD(x));[optimal 
  s=90]I will have the [80,10,90] solution, which is far from the actual 
  optimal [30,20,80]Unfortunately, the optimal[x,b,s] *is not* the 
  optimal[optimal[optimal[x],b],s], consequently this procedure is wrong 
  from the beginning.There is no chain rule for composite optimizations and 
  you will have very few probabilities to find the optimal.Another basic 
  issue: You do not even have theoptimal[optimal[x],y] equal to 
  optimal[optimal[y],x] property, both will be far from the optimal[x,y] and 
  you can not even interchange the priorities.Herman,The results 
  of cascade optimizations will not give the optimal result, even if you 
  save some time. As for this last property, I do not think you save any 
  time, optimization is an internal loop, as far as I 
  understand...Dimitris Tsokakis--- In 
  amibroker@xxxxxxxxxxxxxxx, "Herman vandenBergen" <psytek@xxxx> 
  wrote:> You can optimize using for() loops and examples of this 
  method have been> posted on the list. This would allow you to 
  optimize one parameter first in> the first loop-optimization and 
  then go on the the next loop-optimization> using the result of the 
  first loop-optimization in your calculations. This> way you will 
  save lots of time and you can optimize an unlimited number of> 
  parameters without looking at billions of iterations.> > happy 
  tinkering,> herman.> > >   -----Original 
  Message----->   From: Gary A. Serkhoshian 
  [mailto:serkhoshian777@xxxx]>   Sent: November 13, 2003 7:00 
  AM>   To: amibroker@xxxxxxxxxxxxxxx>   
  Subject: [amibroker] Help on automating optimization (genetic 
  algorithms)> > >   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> > > 
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