Evolutionary synthesis of stochastic gene network models using feature-based search spaces
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @MastersThesis{Imada:mastersthesis,
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author = "Janine Imada",
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title = "Evolutionary synthesis of stochastic gene network
models using feature-based search spaces",
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school = "Department of Computer Science, Brock University",
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year = "2009",
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type = "M.Sc. Computer Science",
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address = "St. Catharines, Ontario, Canada",
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month = "28 " # jan,
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keywords = "genetic algorithms, genetic programming",
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URL = "http://dr.library.brocku.ca/bitstream/handle/10464/2853/Brock_Imada_Janine_2009.pdf",
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URL = "http://hdl.handle.net/10464/2853",
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size = "138 pages",
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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.",
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notes = "cited by \cite{Ross:2011:GPEM}",
- }
Genetic Programming entries for
Janine H Imada
Citations