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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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