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dc.contributor.authorImada, Janine.en_US
dc.date.accessioned2010-01-28T15:55:20Z
dc.date.available2010-01-28T15:55:20Z
dc.date.issued2009-01-28T15:55:20Z
dc.identifier.urihttp://hdl.handle.net/10464/2853
dc.description.abstractA feature-based fitness function is applied in a genetic programming system to synthesize 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 characterizing 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 'if-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.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectComputational biology--Methodology.en_US
dc.subjectStochastic processes--Computer simulation.en_US
dc.titleEvolutionary synthesis of stochastic gene network models using feature-based search spacesen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-08-07T02:24:13Z


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