Evolutionary Synthesis of Stochastic Gene Network Models Using Feature-based Search Spaces
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Imada:2011:NGC,
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author = "Janine Imada and Brian J. Ross",
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title = "Evolutionary Synthesis of Stochastic Gene Network
Models Using Feature-based Search Spaces",
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journal = "New Generation Computing",
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publisher = "Ohmsha, Ltd. and Springer",
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year = "2011",
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pages = "365--390",
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volume = "29",
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issue = "4",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Stochastic,
Statistical Features, Gene Regulatory Networks, Time
Series",
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ISSN = "0288-3635",
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DOI = "doi:10.1007/s00354-009-0115-7",
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size = "26 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 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.",
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affiliation = "Brock University, 500 Glenridge Ave., St. Catharines,
ON, Canada L2S 3A1",
- }
Genetic Programming entries for
Janine H Imada
Brian J Ross
Citations