[Beowulf] Re: Beowulf Digest, Vol 15, Issue 16
mwill at penguincomputing.com
Mon May 9 13:23:48 PDT 2005
Jim Lux wrote:
>> So you have a calculation problem that's embarrassingly parallel
>> but an infinite parameter space to search. Seems to me that if
>> this process is to be automated you will need to define a goal
>> function, presumably based primarily on the far field results,
>> and then use some search strategy or other to try to find at
>> least a local "best" design in your parameter space. For instance,
>> this probem might be amenable to a genetic algorithm approach.
> Actually, the ideal "goal evaluator" is me, looking at the results of
> several runs and comparing them, then telling the "box" which way to
> go next. As you say, if you could define a goal function with
> sufficient clarity, then any manner of optimizers could grind away on
> the problem overnight. Unfortunately, most real design problems have
> requirements that are a bit fuzzy: Don't make it "too big" or "too
> flimsy". terms like "flimsy" are hard to encapsulate succinctly in a
> mathematical formulation (although, gosh, we certainly try, by
> requiring certain mechanical resonance properties and failure
> strengths). Much like other things, you know them when you see them.
Which is excactly when you start to engage the well studied
There are some people combining those with neuronal networks, but I
why it should not be used for any adaptive search. I don't know what the
of the art is with mapping that on a cluster though...
digging through comp.ai.fuzzy could be a starting point, but I only saw one
question without answer to the topic there from albertau at h02.vol.net, maybe
contact him about it if his email address is still valid 10 years after ;-)
>> I know essentially nothing about antenna design so take the following
>> suggestion with the requisite large crystal of salt. Can you
>> subdivide the available (flat?) radiating area into a grid of
>> identical squares which are classified as antenna/non-antenna?
>> At that point your parameters may reduce to: 1) number of squares,
>> 2) their distribution. The first is a single integer and the second
>> is a bit vector (ie, MxN bits, 1 for cells that are
>> antenna, 0 for cells that are not.) This is a simple enough
>> parameter space that a genetic algorithm should be relatively
>> simple to implement. Hopefully you can make this work with so
>> many itty bitty squares that the little squares are much smaller
>> than the shortest wavelength so that the jaggedy edges won't
>> change the results significantly.
> Aha... your idea has been anticipated! Several people have done just
> this (using a Beowulf, even, for the optimizing). Randy Haupt did a
> fair amount of it with wire antennas (and others, I'm sure). There
> was also someone at UCLA who designed wireless antennas using just
> what you describe (adding and removing small patches of conductive
> surface). They then fabricated the antennas and tested them.
>> You can employ your design expertise by starting the genetic
>> algorithm with a few designs that you have reason to think might
>> work reasonably well. Also a bunch of random ones. Then let
>> the software mutate and recombine to see if it can do any
>> David Mathog
>> mathog at caltech.edu
>> Manager, Sequence Analysis Facility, Biology Division, Caltech
> James Lux, P.E.
> Spacecraft Radio Frequency Subsystems Group
> Flight Communications Systems Section
> Jet Propulsion Laboratory, Mail Stop 161-213
> 4800 Oak Grove Drive
> Pasadena CA 91109
> tel: (818)354-2075
> fax: (818)393-6875
> Beowulf mailing list, Beowulf at beowulf.org
> To change your subscription (digest mode or unsubscribe) visit
Penguin Computing Corp.
mwill at penguincomputing.com
Visit us at the following Linux Shows!
Bio-IT World Conference and Expo '05
Hynes Convention Center, Boston, MA
May 17th-19th, 2005
More information about the Beowulf