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Yes, that is workable solution as long as there are only few binary params.
But if there is say 32 binary parameters, you end up with 4 billion combinations, and
then the mapping approach suggested in the paper I mentioned is good.
Best regards,
Tomasz Janeczko
amibroker.com
----- Original Message -----
From: "Dennis Brown" <see3d@xxxxxxxxxxx>
To: <amibroker@xxxxxxxxxxxxxxx>
Sent: Wednesday, October 01, 2008 6:58 PM
Subject: Re: [amibroker] Re: CMAE behavior when optimizing control parameters?
> 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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