Genetic Generation of Both the Weights and
Architecture for a Neural Network
J. Koza, and J. Rice. International Joint Conference on Neural Networks,
IJCNN-91, II, page 397--404. Washington State Convention and Trade Center, Seattle,
WA, USA, IEEE Computer Society Press, (8-12 July 1991)
Abstract
This paper shows how to find both the weights and
architecture for a neural network (including the number
of layers, the number of processing elements per layer,
and the connectivity between processing elements). This
is accomplished using a recently developed extension to
the genetic algorithm which genetically breeds a
population of LISP symbolic expressions (S-expressions)
of varying size and shape until the desired performance
by the network is successfully evolved. The new genetic
programming paradigm is applied to the problem of
generating a neural network for the one-bit adder.
%0 Conference Paper
%1 Koza91
%A Koza, John R.
%A Rice, James P.
%B International Joint Conference on Neural Networks,
IJCNN-91
%C Washington State Convention and Trade Center, Seattle,
WA, USA
%D 1991
%I IEEE Computer Society Press
%K adder, algorithms, cogann connectionism, genetic one-bit programming, ref
%P 397--404
%T Genetic Generation of Both the Weights and
Architecture for a Neural Network
%U http://www.genetic-programming.com/jkpdf/ijcnn1991.pdf
%V II
%X This paper shows how to find both the weights and
architecture for a neural network (including the number
of layers, the number of processing elements per layer,
and the connectivity between processing elements). This
is accomplished using a recently developed extension to
the genetic algorithm which genetically breeds a
population of LISP symbolic expressions (S-expressions)
of varying size and shape until the desired performance
by the network is successfully evolved. The new genetic
programming paradigm is applied to the problem of
generating a neural network for the one-bit adder.
%@ 0-7803-0164-1
@inproceedings{Koza91,
abstract = {This paper shows how to find both the weights and
architecture for a neural network (including the number
of layers, the number of processing elements per layer,
and the connectivity between processing elements). This
is accomplished using a recently developed extension to
the genetic algorithm which genetically breeds a
population of LISP symbolic expressions (S-expressions)
of varying size and shape until the desired performance
by the network is successfully evolved. The new genetic
programming paradigm is applied to the problem of
generating a neural network for the one-bit adder.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Washington State Convention and Trade Center, Seattle,
WA, USA},
author = {Koza, John R. and Rice, James P.},
bibdate = {Wed Jan 15 14:07:16 1997},
biburl = {https://www.bibsonomy.org/bibtex/2e8c41a5aa200ebea6f17c61f095be8af/brazovayeye},
booktitle = {International Joint Conference on Neural Networks,
IJCNN-91},
interhash = {bf8c9814cc4972e15ce04693e31a1aba},
intrahash = {e8c41a5aa200ebea6f17c61f095be8af},
isbn = {0-7803-0164-1},
keywords = {adder, algorithms, cogann connectionism, genetic one-bit programming, ref},
lccn = {QA76.87.I57 1991b},
month = {8-12 July},
notes = {Two volumes. IEEE catalog number: 91CH3049-4.},
pages = {397--404},
publisher = {IEEE Computer Society Press},
publisher_address = {1109 Spring Street, Suite 300, Silver Spring, MD
20910, USA},
timestamp = {2008-06-19T17:43:55.000+0200},
title = {Genetic Generation of Both the Weights and
Architecture for a Neural Network},
url = {http://www.genetic-programming.com/jkpdf/ijcnn1991.pdf},
volume = {II},
year = 1991
}