The evolution of higher-level biochemical reaction models

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Ross:2011:GPEM,
  author =       "Brian J. Ross",
  title =        "The evolution of higher-level biochemical reaction
                 models",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2012",
  volume =       "13",
  number =       "1",
  pages =        "3--31",
  month =        mar,
  note =         "Special Section on Evolutionary Algorithms for Data
                 Mining",
  keywords =     "genetic algorithms, genetic programming,
                 Grammar-guided, Biochemical modelling, Time series,
                 Statistical features, Process algebra",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-011-9144-3",
  size =         "29 pages",
  abstract =     "Computational tools for analysing biochemical
                 phenomena are becoming increasingly important.
                 Recently, high-level formal languages for modeling and
                 simulating biochemical reactions have been proposed.
                 These languages make the formal mode ling of complex
                 reactions accessible to domain specialists outside of
                 theoretical computer science. This research explores
                 the use of genetic programming to automate the
                 construction of models written in one such language.
                 Given a description of desired time-course data, the
                 goal is for genetic programming to construct a model
                 that might generate the data. The language investigated
                 is Kahramanogullari's and Cardelli's Programming
                 Interface for Modelling (PIM) language. The PIM syntax
                 is defined in a grammar-guided genetic programming
                 system. All time series generated during simulations
                 are described by statistical feature tests, and the
                 fitness evaluation compares feature proximity between
                 the target and candidate solutions. PIM models of
                 varying complexity were used as target expressions for
                 genetic programming, and were successfully
                 reconstructed in all cases. This shows that the
                 compositional nature of PIM models is amenable to
                 genetic program search.",
  affiliation =  "Department of Computer Science, Brock University, 500
                 Glenridge Ave., St. Catharines, ON L2S 3A1, Canada",
  notes =        "DCTG-GP written in Prolog. No genetic repair. MOGP (3
                 objectives: Mean, sd and skew) selection
                 \cite{Bentley97}. Phagocytosis. All individuals unique
                 (not equivalent to all models (phenotypes) being
                 unique. PIM and SPiM written in OCAML.",
}

Genetic Programming entries for Brian J Ross

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