Multipopulation Genetic Programing Applied to Burn Diagnosing
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{vega:2000:mgpabd,
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author = "F. {Fernandez de Vega} and Laura M. Roa and
Marco Tomassini and J. M. Sanchez",
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title = "Multipopulation Genetic Programing Applied to Burn
Diagnosing",
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booktitle = "Proceedings of the 2000 Congress on Evolutionary
Computation CEC00",
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year = "2000",
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pages = "1292--1296",
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volume = "2",
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address = "La Jolla Marriott Hotel La Jolla, California, USA",
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publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
08855-1331, USA",
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month = "6-9 " # jul,
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organisation = "IEEE Neural Network Council (NNC), Evolutionary
Programming Society (EPS), Institution of Electrical
Engineers (IEE)",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming, novel
applications, burn diagnosis, decision support system,
decision trees, explicit information, input parameter,
learning classifier system, medical decision making,
multipopulation genetic programming, optimization,
software tools, decision support systems, decision
trees, medical diagnostic computing, optimisation",
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ISBN = "0-7803-6375-2",
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DOI = "doi:10.1109/CEC.2000.870800",
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abstract = "Genetic programming (GP) has proved useful in
optimisation problems. The way of representing
individuals in this methodology is particularly good
when we want to construct decision trees. Decision
trees are well suited to representing explicit
information and relationships among parameters studied.
A set of decision trees could make up a decision
support system. In this paper we set out a methodology
for developing decision support systems as an aid to
medical decision making. Above all, we apply it to
diagnosing the evolution of a burn, which is a really
difficult task even for specialists. A learning
classifier system is developed by means of
multipopulation genetic programming (MGP). It uses a
set of parameters, obtained by specialist doctors, to
predict the evolution of a burn according to its
initial stages. The system is first trained with a set
of parameters and results of evolutions which have been
recorded over a set of clinic cases. Once the system is
trained, it is useful for deciding how new cases will
probably evolve. Thanks to the use of GP, an explicit
expression of the input parameter is provided. This
explicit expression takes the form of a decision tree
which will be incorporated into software tools that
help physicians In their everyday work",
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notes = "CEC-2000 - A joint meeting of the IEEE, Evolutionary
Programming Society, Galesia, and the IEE.
IEEE Catalog Number = 00TH8512,
Library of Congress Number = 00-018644",
- }
Genetic Programming entries for
Francisco Fernandez de Vega
Laura M Roa
Marco Tomassini
Juan Manuel Sanchez Perez
Citations