Learning dynamic models of compartment systems by combining symbolic regression with fuzzy vector envisionment

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@InProceedings{1274050,
  author =       "Mehdi Khoury and Frank Guerin and George M. Coghill",
  title =        "Learning dynamic models of compartment systems by
                 combining symbolic regression with fuzzy vector
                 envisionment",
  booktitle =    "Genetic and Evolutionary Computation Conference
                 {(GECCO2007)} workshop program",
  year =         "2007",
  month =        "7-11 " # jul,
  editor =       "Tina Yu",
  isbn13 =       "978-1-59593-698-1",
  pages =        "2769--2776",
  address =      "London, United Kingdom",
  keywords =     "genetic algorithms, genetic programming, dynamic
                 biological model, dynamic compartmental model, fuzzy
                 vector envisionment, measurement, metabolic pathways,
                 semi-quantitative modelling, S-system, symbolic
                 regression, u-tube",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p2769.pdf",
  DOI =          "doi:10.1145/1274000.1274050",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper is concerned with the learning of dynamic
                 models of compartmental systems visualised as networks
                 of interconnected tanks. This is intended as an
                 intermediary step to learn more complex dynamic
                 biological systems such as metabolic pathways. Our
                 present aim is to learn systems of differential
                 equations from time series data to capture physical
                 models of increasing complexity (u-tube, cascaded
                 tanks, and coupled tanks). To do so, we use Symbolic
                 Regression in Genetic Programming and combine it with a
                 fuzzy representation which has inherent differential
                 capabilities (Fuzzy Vector Envisionment). We use the
                 ECJ framework to implement the learner. Present results
                 show that the system can approximate the target models
                 and that the use of a weighted fitness function seems
                 to accelerate the learning process.",
  notes =        "Distributed on CD-ROM at GECCO-2007 ACM Order No.
                 910071",
}

Genetic Programming entries for Mehdi Khoury Frank Guerin George M Coghill

Citations