Evolutionary computation is a class of global search
techniques based on the learning process of a
population of potential solutions to a given problem,
that has been successfully applied to a variety of
problems. In this paper a new approach to the
construction of neural networks based on evolutionary
computation is presented. A linear chromosome combined
to a graph representation of the network are used by
genetic operators, which allow the evolution of the
architecture and the weights simultaneously without the
need of local weight optimization. This paper describes
the approach, the operators and reports results of the
application of this technique to several binary
classification problems.
%0 Journal Article
%1 Pujol:1998:etwNNGP
%A Pujol, Joao Carlos Figueira
%A Poli, Riccardo
%D 1998
%J Applied Intelligence
%K Evolutionary PDGP algorithms, computation, genetic networks, neural programming,
%P 73--84
%R doi:10.1023/A:1008272615525
%T Evolution of the Topology and the Weights of Neural
Networks using Genetic Programming with a Dual
Representation
%U http://citeseer.ist.psu.edu/322291.html
%V 8
%X Evolutionary computation is a class of global search
techniques based on the learning process of a
population of potential solutions to a given problem,
that has been successfully applied to a variety of
problems. In this paper a new approach to the
construction of neural networks based on evolutionary
computation is presented. A linear chromosome combined
to a graph representation of the network are used by
genetic operators, which allow the evolution of the
architecture and the weights simultaneously without the
need of local weight optimization. This paper describes
the approach, the operators and reports results of the
application of this technique to several binary
classification problems.
@article{Pujol:1998:etwNNGP,
abstract = {Evolutionary computation is a class of global search
techniques based on the learning process of a
population of potential solutions to a given problem,
that has been successfully applied to a variety of
problems. In this paper a new approach to the
construction of neural networks based on evolutionary
computation is presented. A linear chromosome combined
to a graph representation of the network are used by
genetic operators, which allow the evolution of the
architecture and the weights simultaneously without the
need of local weight optimization. This paper describes
the approach, the operators and reports results of the
application of this technique to several binary
classification problems.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Pujol, Joao Carlos Figueira and Poli, Riccardo},
biburl = {https://www.bibsonomy.org/bibtex/20ea33b3ad302fe69473b06d3d7599cb6/brazovayeye},
broken = {http://www.urano.cdtn.br/~pujol/ai98.ps},
doi = {doi:10.1023/A:1008272615525},
email = {J.Pujol@cs.bham.ac.uk R.Poli@cs.bham.ac.uk},
interhash = {3d1785b3035f411f7a5873e62eec74d0},
intrahash = {0ea33b3ad302fe69473b06d3d7599cb6},
journal = {Applied Intelligence},
keywords = {Evolutionary PDGP algorithms, computation, genetic networks, neural programming,},
notes = {see also \cite{Pujol:1997:etwNNGP} XOR, 3,4,5 odd
parity, T an C character (TC) recognition problem},
pages = {73--84},
size = {12 pages},
timestamp = {2008-06-19T17:49:54.000+0200},
title = {Evolution of the Topology and the Weights of Neural
Networks using Genetic Programming with a Dual
Representation},
url = {http://citeseer.ist.psu.edu/322291.html},
volume = 8,
year = 1998
}