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Hello,
I sounds to me like the approach that needs to be taken is modal
parameters must be part of an exhaustive group and the continuous
parameters can be intelligently optimized for each mode. This really
means running a separate optimization for each different mode. Since
a different operating mode is really a different algorithmic "system"
this makes sense. Finding the best combination of operating mode and
optimum parameters for each mode in one optimization setup would
require a hybrid approach.
BR,
Dennis
On Oct 1, 2008, at 12:10 PM, Tomasz Janeczko wrote:
> Hello,
>
> There are few scientific papers that suggest workarounds to problem
> of optimizing
> binary parameter spaces.
> For example this one:
> http://ieeexplore.ieee.org/iel5/11108/35623/01688535.pdf?tp=&isnumber=&arnumber=1688535
>
> As we can read in the abstract:
> "The ability of Differential Evolution (DE) to perform well in
> CONTINUOUS-valued search spaces is well documented.
>
> The arithmetic reproduction operator used by differential evolution
> is simple, however, the manner in which
>
> the operator is defined, makes it **PRACTICALLY IMPOSSIBLE** to
> effectively apply the standard DE to other problem spaces."
>
> (emphasis is from me).
>
> Authors of the article suggest that DE can only be used if
> appropriate MAPPING of
> binary space into continuous space is applied.
>
> You really need to be carefull with *ALL* non-exhaustive optimizers,
> as
> they are NOT suited for binary parameters.
>
> Best regards,
> Tomasz Janeczko
> amibroker.com
> ----- Original Message -----
> From: "Steve Davis" <_sdavis@xxxxxxxxx>
> To: <amibroker@xxxxxxxxxxxxxxx>
> Sent: Wednesday, October 01, 2008 5:56 PM
> Subject: [amibroker] Re: CMAE behavior when optimizing control
> parameters?
>
>
>> Interesting. If meta-data existed to indicate which parameters are
>> continuous and which are discreet, could a future optimization
>> algorithm use that information to improve the optimization process?
>>
>> --- In amibroker@xxxxxxxxxxxxxxx, "Tomasz Janeczko" <groups@xxx>
>> wrote:
>>>
>>> Differential Evolution is also for CONTINUOUS functions, see
>>> the AUTHORS' page:
>>> http://www.icsi.berkeley.edu/~storn/code.html
>>>
>>> All those methods use gradient of fitness function change
>>> to decide in which direction they should move. For binary (0 or 1)
>> parameters
>>> gradients make no sense.
>>>
>>> Best regards,
>>> Tomasz Janeczko
>>> amibroker.com
>>> ----- Original Message -----
>>> From: "Steve Davis" <_sdavis@xxx>
>>> To: <amibroker@xxxxxxxxxxxxxxx>
>>> Sent: Wednesday, October 01, 2008 4:30 PM
>>> Subject: [amibroker] Re: CMAE behavior when optimizing control
>> parameters?
>>>
>>>
>>>> Thanks Paul and Tomasz,
>>>>
>>>> I have also used IO for many years and consulted with Fred on this
>>>> issue. Fred suggested using the Differential Evolution algorithm
>>>> rather than Particle Swarm when a system has many non-continuous
>>>> parameters.
>>>>
>>>> In any case, Tomasz gave me the answer I needed regarding CMAE.
>>>>
>>>> Thanks again,
>>>> Steve
>>>>
>>>> --- In amibroker@xxxxxxxxxxxxxxx, "Paul Ho" <paul.tsho@> wrote:
>>>>>
>>>>> Tomasz
>>>>>
>>>>> What you said and what I said can co-exist quite happily if you
>>>>> want
>>>> to read
>>>>> it again, and want to read it that way!
>>>>> It is not a debate that I want to enter into with you. I am just
>>>> sharing my
>>>>> experience - it is "possible" to do it.
>>>>> All of these IO used simulated "Continuous" parameters, which by
>> its own
>>>>> nature are discrete, and it is the job of the user to get the
>>>>> best
>>>> use out
>>>>> of it.
>>>>>
>>>>> Finally, I have done tens of thousands of optimizations, lost of
>>>> them with
>>>>> success, so its about making your own luck in this game.
>>>>>
>>>>> for example consider this statement
>>>>> xyz = m1 * (MA(C, pds) > C) + (!m1) * (ma(c,pds) <= C);
>>>>> where m1 is a control parameters that decides whether xyz = ma(c,
>>>> pds) > C
>>>>> or the other way around, and pds is the period of ma, as it stands
>>>> it wont
>>>>> be get much "luck" as you say. because, pds that is optimimum in
>>>>> the
>>>> case of
>>>>>> is probably very different than in the case of <=.
>>>>> so by making xyz = m1 * (ma(c, pds1) > C) + (!m1) * (ma(c, pds2)
>>>>> <=
>>>> C); and
>>>>> optimize pds1, m1 and pds2 separately, you will get pds1 and pds2
>>>> gathering
>>>>> around a cluster of value closer to its optiminum, and m1 has own
>>>> value of 0
>>>>> or 1 which sort out what way is better.
>>>>>
>>>>> I hope this will be useful those who wants to use it.
>>>>>
>>>>>
>>>>> _____
>>>>>
>>>>> From: amibroker@xxxxxxxxxxxxxxx [mailto:amibroker@xxxxxxxxxxxxxxx]
>>>> On Behalf
>>>>> Of Tomasz Janeczko
>>>>> Sent: Wednesday, 1 October 2008 7:18 PM
>>>>> To: amibroker@xxxxxxxxxxxxxxx
>>>>> Subject: Re: [amibroker] Re: CMAE behavior when optimizing control
>>>>> parameters?
>>>>>
>>>>>
>>>>>
>>>>> Paul,
>>>>>
>>>>> I don't want to enter into yet another useless debate, but if you
>> learn
>>>>> about
>>>>> *MATHEMATICAL* background of
>>>>> Particle Swarm Optimizers you will
>>>>> know that they are all designed to be used for CONTINUOUS
>>>>> parameter
>>>> spaces.
>>>>>
>>>>> The fact that non-exhaustive methods like CMAE, PSO, etc *may*
>>>>> work
>>>> in some
>>>>> cases for discrete spaces
>>>>> is more a question of luck and relative simplicity (or more or
>>>>> less
>>>>> "smoothness") of the problem
>>>>> being optimized than anything else.
>>>>>
>>>>> Best regards,
>>>>> Tomasz Janeczko
>>>>> amibroker.com
>>>>> ----- Original Message -----
>>>>> From: "Paul Ho" <paul.tsho@xxxxxx <mailto:paul.tsho%40gmail.com>
>>>>> com>
>>>>> To: <amibroker@xxxxxxxxx <mailto:amibroker%40yahoogroups.com>
>>>>> ps.com>
>>>>> Sent: Wednesday, October 01, 2008 11:03 AM
>>>>> Subject: [amibroker] Re: CMAE behavior when optimizing control
>>>> parameters?
>>>>>
>>>>>> Talking from personal experience - and I've been using
>>>>>> intelligent
>>>>>> Optimizers for quite a number of years optimizing combinations of
>>>>>> continuous and "discrete" control parameters. Fred's IO has
>>>>>> worked
>>>>>> extremely well - in that I'm able to find optiminiums
>> successfully,
>>>>>> it may be a little more tricky, but not impossible. There are
>> things
>>>>>> that would help to IO work better. Nevertheless, I do have more
>>>>>> problems with cmae with a lot of discrete parameters. But I
>> suspect
>>>>>> that's more to do with configuration of cmae rather than the
>> ability
>>>>>> of cmae itself.
>>>>>>
>>>>>> --- In amibroker@xxxxxxxxx <mailto:amibroker%40yahoogroups.com>
>>>> ps.com,
>>>>> "Tomasz Janeczko" <groups@>
>>>>>> wrote:
>>>>>>>
>>>>>>> No, CMAE, PSO and most other non-exhaustive methods
>>>>>>> are best for continuous parameter spaces. Discrete spaces
>>>>>>> where adjacent param values result in wild changes in fitness
>>>>>>> tend to be very difficult to optimize in "intelligent" manner.
>>>>>>>
>>>>>>> Best regards,
>>>>>>> Tomasz Janeczko
>>>>>>> amibroker.com
>>>>>>> ----- Original Message -----
>>>>>>> From: "Steve Davis" <_sdavis@>
>>>>>>> To: <amibroker@xxxxxxxxx <mailto:amibroker%40yahoogroups.com>
>> ps.com>
>>>>>>> Sent: Wednesday, October 01, 2008 1:19 AM
>>>>>>> Subject: [amibroker] CMAE behavior when optimizing control
>>>>>> parameters?
>>>>>>>
>>>>>>>
>>>>>>>> Does anyone know if the CMAE algorithm can be used
>> effectively to
>>>>>>>> optimize a system containing control parameters? By this I mean
>>>>>>>> optimizable parameters that do not measure a quantity, but are
>>>>>> instead
>>>>>>>> used to control the flow of execution of the program. In this
>>>>>> sort of
>>>>>>>> system, adjacent parameter values could result in wildly
>>>>>> different
>>>>>>>> system fitness.
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Steve
>>>>>>>>
>>>>>>>>
>>>>>>>>
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>>>>>>>
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>>>>>>
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>>>>>
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>>>
>>
>>
>>
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