Subject: FW: Proposed 615 Final Project

From: Quant Marjorie V 2dLt AFRL/IFSB <[email-masked]>
Date: Mon, 10 Apr 2000 13:37:34 -0400

I was not aware we needed to link these proposals.  Will this email suffice,
or do you still want us to submit this to our homepages?

Marjorie Quant
Steve Alexander

-----Original Message-----
From: Quant Marjorie V 2dLt AFRL/IFSB 
Sent: Monday, March 20, 2000 10:09 AM
To: [email-masked]' <mailto:[email-masked]'> 
Cc: Alexander Steven M 2dLt AFRL/IFSB; Quant Marjorie V 2dLt AFRL/IFSB
Subject: Proposed 615 Final Project

Professor Fox,  
 
We wish to write a  genetic algorithm  using the advantages of parallel
computing.  Whatever the genetic algorithm, the algorithm will be copied to
each of the N processors (N is determined by the user).  There may be one
overall goal or evaluation criteria for the genetic algorithm.  Each
processor will randomly alter one factor in the environment of the genetic
algorithm.  Essentially there will be N copies of the same program running
with slightly different environments.  After X generations, each processor
will produce the best individual for the overall goal.  These best
individual will then be compared against the other processor's best
individuals and the overall best individual will be copied into each
processor, replacing the worst individual in each environment.  Again the
algorithm will run, and after so many generations another check will be
performed.  This process will go on until one of two things happens:  if the
genetic algorithm is an optimization function, the algorithm will halt when
the optimum has been reached; or the algorithm will halt after so many
generations and the best individuals will be evaluated.
 
Marjorie Quant
and 
Steve Alexander
CIS 615 -- Spring 00

_________________________________
Marjorie V Quant, Lt, USAF
AFRL/IFSB
525 Brooks Road
Rome NY, 13441

EMail:  [email-masked]
DSN:  587-4679  Comm:  (315) 330-4679

---

From: Saleh Elmohamed <[email-masked]>
Date: Mon, 10 Apr 2000 14:12:49 -0400 (EDT)

Hi,

> or do you still want us to submit this to our homepages?

This email would suffice... If you'd like to link it to your page, go
ahead so that to keep a record of things related to the project and make
it somewhat easier to review during grading times.

See some comments below...

> generations and the best individuals will be evaluated.

I'd say it is an interesting project. You need to be aware of a number of
things even if the method tackled in a parallel fashion. There are three
main functions that you'd to take of and define and those are: 
'selection', 'fitness', and 'crossover'. The intuitive idea behind it is
simple: use a population of solutions and let them reproduce according to
a process of selection and recombination (sexual reproduction). Depending
on the construction of your algorithm, you could perhaps select a
sub-population 'S' from the entire population 'P' (maybe biasing the
selection to those members of the population which are fitter), and then
generate a new-population (perhaps all the old members die) via sexual
reproduction between pairs of individuals from 'S', using the 'crossover'
function (usually mutation is also applied at a very low background rate).
To be able to do this, you must be able to represent any solution as a
collection of genes with particular values. There is always a huge choice
for the manner in which this 'representation' is constructed. 

NOTE: The ability of the algorithm to generate good solutions depends
almost completely upon the representation chosen. A poor representation
will result in the search being effectively random (or maybe even worse,
guiding the search to poor solutions!), whereas an appropriate
representation may produce stunning performance.

Despite a huge literature on GAs and their many variants, there is no real
rigorous theory describing when such algorithms will perform well and when
they will perform poorly.

Good luck.