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

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@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