Multipopulation Genetic Programing Applied to Burn Diagnosing

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "F. {Fernandez de Vega} and Laura M. Roa and 
                 Marco Tomassini and J. M. Sanchez",
  title =        "Multipopulation Genetic Programing Applied to Burn
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1292--1296",
  volume =       "2",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  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",
  ISBN =         "0-7803-6375-2",
  DOI =          "doi:10.1109/CEC.2000.870800",
  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",
  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