Genetic Algorithms

[Chris Brobeck]

Dear Colleagues;
	We're currently in the process of building a relatively large net
and were looking at of using a genetic algorithm to optimize the network 
structure.
	The question is as follows. Early forms of genetic algorithms seemed
to rely on reading the gene once, linearly, in the construction process,
whereas a number of more recent algorithms allow the reading to start anywhere
along the gene, and continue to read (construct rules) until some stopping
criteria is met.
	In the former, it seems reasonable then for one organism to compete
against the other in a winner-take-all sort of way. On the other hand, the
rigidity of the genetic structure makes it very sensitive to mutation.
	In the latter case the gene may be thought of as a generator for
a process (randomly) creating rules of a variety of lengths. If one assumes
that individual rules are much shorter than the entire gene this method
becomes less sensitive to mutation,crossover,etc (both the beneficial
and not so beneficial aspects). In this case it seems that competition among
species would be as critical as competion among individuals, with the
interspecies competion perhaps representing a fast way to remove ineffective
rule sets, and individual competion more of a way of fine-tuning a distribution.
The upshot would be (one assumes) slower but more robust convergence. 
	In any case, if there is anyone out there who can point us in the
direction of some good references let us know - particularly ones that might
be available via ftp. Thanks,

	Chris Brobeck.