Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces

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

@Article{Imada:2011:NGC,
  author =       "Janine Imada and Brian J. Ross",
  title =        "Evolutionary Synthesis of Stochastic Gene Network
                 Models Using Feature-based Search Spaces",
  journal =      "New Generation Computing",
  publisher =    "Ohmsha, Ltd. and Springer",
  year =         "2011",
  pages =        "365--390",
  volume =       "29",
  issue =        "4",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Stochastic,
                 Statistical Features, Gene Regulatory Networks, Time
                 Series",
  ISSN =         "0288-3635",
  DOI =          "doi:10.1007/s00354-009-0115-7",
  size =         "26 pages",
  abstract =     "A feature-based fitness function is applied in a
                 genetic programming system to synthesise stochastic
                 gene regulatory network models whose behaviour is
                 defined by a time course of protein expression levels.
                 Typically, when targeting time series data, the fitness
                 function is based on a sum-of-errors involving the
                 values of the fluctuating signal. While this approach
                 is successful in many instances, its performance can
                 deteriorate in the presence of noise and/or stochastic
                 behaviour. This paper explores a fitness measure
                 determined from a set of statistical features
                 characterising the time series' sequence of values,
                 rather than the actual values themselves. Through a
                 series of experiments involving modular gene regulatory
                 network models based on the stochastic pi-calculus, it
                 is shown to successfully target oscillating and
                 non-oscillating signals. This practical and versatile
                 fitness function offers an alternate approach, worthy
                 of consideration for use in algorithms that evaluate
                 noisy or stochastic behaviour.",
  affiliation =  "Brock University, 500 Glenridge Ave., St. Catharines,
                 ON, Canada L2S 3A1",
}

Genetic Programming entries for Janine H Imada Brian J Ross

Citations