[Beowulf] Re: Beowulf Digest, Vol 15, Issue 16

David Mathog mathog at mendel.bio.caltech.edu
Mon May 9 09:49:51 PDT 2005

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> It's a design problem ripe for interaction too.  There's a lot of
> parameters I can change (size and shape of the patches, segmentation,
> spacing, etc.), so running a "try all possible values of all variables
> overnight" strategy won't work.  Equally poor would be a "submit massive
> batch job to the JPL DELL 1024 processor cluster", mostly because the
> design space probably spans several thousand parameter combinations.  I
> want to try a few things, then try some more, and use my experience to
> guide the process, not depend on a optimizing program, for which I'd have
> to come up with a goal function that is sort of ill-defined.

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.

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.

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
better.

Regards,

David Mathog
mathog at caltech.edu
Manager, Sequence Analysis Facility, Biology Division, Caltech

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