TJ,

 

IO, which was preceded by PSO, was initially an experiment to determine
whether or not it could even be done and then whether or not it was a
worthwhile tool to have.

 

Following that it was and is for the most part a give back to the community
as most of the bells and whistles are FREEWARE in a user friendly format.
Stating that it is a black box is absurd as it uses fairly standard simple
algorithms with some tweaks that after tons of experimentation I know to be
of benefit and users have control over all aspects of how the algorithms
work from their AFL if they choose to use them without having to research
them on the internet as there's 60+ pages of documentation about what has
been implemented, how it works and the associated feature/functions .

 

Frankly I could care less if anyone ever bought a copy with the more
advanced features as the fees associated with those features were put on
simply to reduce the amount of support that would no doubt be required if
the entire community used them.  

 

What I want to compare is the usefulness of the different engines for
different types of problems and how long they take to arrive at relatively
decent results to solve problems that can not be solved by exhaustive search
and to that end I have already asked several straight forward questions that
for whatever reason you have chosen to ignore . So I'll try them again .

 

-          How does one intelligently decide how many runs and tests to use
for PSO & Tribes based on differing number of variables to be optimized ?

 

-          What happens differently for these two engines when one specifies
5 runs of 1000 tests versus 1 run of 5000 tests ?

 

-          How should one set up CMA-ES so that it produces superior results
in less time for problems like the one I outlined i.e. that are of a type
that can not be solved by exhaustive search ?  

 

These are basic questions about the use of the intelligent optimization
engines that you have chosen to include in the product which I would think
lots of folks would want the answers to without having to search the
internet.

 

Personally I've already read way beyond my share of scientific papers on
intelligent optimization.

 

  _____  

From: [email protected] [mailto:[EMAIL PROTECTED] On Behalf
Of Tomasz Janeczko
Sent: Saturday, June 28, 2008 3:59 AM
To: [email protected]
Subject: Re: [amibroker] Re: The EASIEST way to use new optimizer engines

 

Fred,

 

I don't know why you took some kind of mission on criticizing last
developments  maybe this is because

you are selling IO while AB optimizer is offered as free upgrade and that
makes you angry.

I don't know why this is so, because actually you can benefit from that too
- I have provided

full source code so everything is open for innovation and improvement,
unlike black box IO.

 

The fact is that you are comparing APPLES TO ORANGES.

 

You should really READ the documentation I have provided and visit links I
have provided.

 

CMA-ES DEFAULTS are well suited for tests that are replacement of exhaustive
searches.

 

They are however too large for 15 variables. For example CMO by default will
use

900 * (N + 3 ) * (N+3 ) max evaluations. It converges much quicker therefore
estimate

displayed in the progress bar is calculated as follows 30 * (N+3) *  (N+3)

 

You are comparing 1000 evaluations of PSO with CONSTANT population size

to 10000+ evaluations of CMAE with GROWING population size default settings.

 

You are comparing elephant to an ant.

 

If you want to COMPARE things you need to set up IDENTICAL conditions.

That would be:

 

OptimizerSetOption("Runs", 1 );

OptimizerSetOption("MaxEval", 10000 );

 

With *IDENTICAL* conditions, CMA-ES will run faster.


Best regards,
Tomasz Janeczko
amibroker.com

----- Original Message ----- 

From: Fred <mailto:[EMAIL PROTECTED]>  Tonetti 

To: [EMAIL PROTECTED] <mailto:[email protected]> ps.com 

Sent: Saturday, June 28, 2008 7:15 AM

Subject: RE: [amibroker] Re: The EASIEST way to use new optimizer engines

 

It is somewhat meaningless to compare intelligent optimizers with exhaustive
search due to the fact that for most real world problems exhaustive search
would need more time than the universe has been around to solve them . It is
also somewhat meaningless to compare intelligent optimizers with each other
based on problems that are solvable by exhaustive search.

 

In regards to the imbedded PSO & Tribes algorithms you state .

 

"You should increase the number of evaluations with increasing number of
dimensions.  The default 1000 is good for 2 or maximum 3 dimensions" .

 

Can you provide any guidance as to what relationship should exist between
the number of dimensions and the number of tests ? i.e. what's a reasonable
number of tests for 5 dimensions, 10, 100 ?

 

Can you explain the difference between 1 run with 5000 tests and 5 runs with
1000 tests ?

 

 

As far as CMAE is concerned . Maybe I'm missing something but it doesn't
seem that CMAE has anything in terms of speed over AB's PSO or Tribes .

 

I tried CMAE out on a real world intelligent optimization problem with 15
variables trading 100 symbols by adding the required statement to the AFL .

 

Run time for CMAE to complete was 459 minutes .

 

 

Run times for AB's PSO and Tribes to complete with 5 runs and 1000 tests was
in the neighborhood of 75 minutes each with results being the sane as CMAE. 

 

 

As an FYI .

 

Run times for IO's DE and PS to complete via their own internal decision
making process w/o the help of additional cores ( servers ) was in the same
neighborhood with times of 72 and 53 minutes respectively.

 

With the help of additional cores ( 7 ) IO's DE and PSO ran to completion in
11 and 8 minutes respectively .

 

 


  _____  


From: [email protected] [mailto:[EMAIL PROTECTED] On Behalf
Of Tomasz Janeczko
Sent: Friday, June 27, 2008 8:05 PM
To: [email protected]
Subject: Re: [amibroker] Re: The EASIEST way to use new optimizer engines

 

FYI: using new optimizer engine (cmae) to optimize seemingly
simple 3 parameter (ranging 1..100) system gives speed up
of more than 1000 times, as cmae optimizer is able to find best
value in less than 1000 backtests compared to one million backtests
using exhaustive search. It also outperforms PSO usually by factor of 10.

That is 500 times faster than you would get from exhaustive opt using your
dual core
and 5 times faster than PSO on dual core.

CMA-ES delivers MORE in terms of speed with LESS development time.

Best regards,
Tomasz Janeczko
amibroker.com

 


  _____  


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