No subject

 Abstract
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This paper describes a set-based chromosome 
for describing neural networks.  The chromosome
etween
sets.  Each set is updated in order, as are the neurons in that set,
in accordance with a simple pre-specified algorithm.  This allows
all details of a neural architecture, including its learning behaviour
to be specified in a simple and purely declarative manner.
To evolve a learning behaviour for a particular  network
architecture, certain details of the architecture are
pre-specified by defining a chromosome template, with some of the
genes fixed, and others allowed to vary.  In this paper, a learning 
perceptron is evolved, by fixing the feedforward and error-computation
parts of the chromosome, then evolving the feedback part 
responsible for computing weight updates.
Using this methodology,  learning behaviours
with similar performance to the delta rule have been evolved.


This paper is available through my web page:
http://esewww.essex.ac.uk/~sml

or via anonymous ftp:

  ftp tarifa.essex.ac.uk
  cd /images/sml/reports
  get esann96.ps
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 Comments and criticisms welcome.

  Simon Lucas



------------------------------------------------
Dr. Simon Lucas
Department of Electronic Systems Engineering
University of Essex
Colchester CO4 3SQ
United Kingdom

http://esewww.essex.ac.uk/~sml
Tel:    (+44) 1206 872935
Fax:    (+44) 1206 872900
Email:  sml at essex.ac.uk
secretary:  Mrs Wendy Ryder  (+44) 1206 872437
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