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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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