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From: notredame@ebi.ac.uk
Subject: large random space
Message-ID: <1994Oct27.163228@ebi.ac.uk>
Lines: 22
Sender: news@ebi.ac.uk (Mr news)
Organization: European BioInformatics Institute
Date: Thu, 27 Oct 1994 15:32:28 GMT

Hi,
I am trying to apply GA to the search of a huge space, obviously non
linear. 
Initialy, I am dealing with an object which is made of X subobjects, each of 
them can be in Xi configurations. Thus the total of possible configurations 
for my object is the product ( Xi). Given a set of Xi, it is possible to 
generate the object and then to score its fitness by an objective function.
I have designed a very basic GA, where each individual is made of X genes, each
coding for the value of one subobject, and thus, each chromosome coding for one
object only ( the subojects cannot be permuted).
Of course, this doesn't work. The space is huge and the size of each gene is
about 200 bits, for ten subobjects ( 2000 bits for the whole chromosome).
Practicly, I cannot handle more than a hundred individuals per Generation.
So I d like to know if anyone has had the same kind of problem. I beleve that a
poor coding ( N-> one state) is responsible of the pour efficiency. Are there
any systematic ways, for making a clever coding, and try to bring the problem
in a more linear space ( at least less stochastic)?
Any idea is welcome especialy if you have a good reason to tell me that this
kind of approach is hopeless for a space too large and too random.
Thanks very much.
please, reply directly to 
NOTREDAME@ebi.ac.uk
