Evolutionary synthesis of stochastic gene network models using feature-based search spaces

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@MastersThesis{Imada:mastersthesis,
  author =       "Janine Imada",
  title =        "Evolutionary synthesis of stochastic gene network
                 models using feature-based search spaces",
  school =       "Department of Computer Science, Brock University",
  year =         "2009",
  type =         "M.Sc. Computer Science",
  address =      "St. Catharines, Ontario, Canada",
  month =        "28 " # jan,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://dr.library.brocku.ca/bitstream/handle/10464/2853/Brock_Imada_Janine_2009.pdf",
  URL =          "http://hdl.handle.net/10464/2853",
  size =         "138 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. This thesis
                 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
                 symbolic regression with added noise and 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.",
  notes =        "cited by \cite{Ross:2011:GPEM}",
}

Genetic Programming entries for Janine H Imada

Citations