Data-Driven, Free-Form Modeling Of Biological Systems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8154
- @PhdThesis{Cornforth:thesis,
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author = "Theodore Cornforth",
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title = "Data-Driven, Free-Form Modeling Of Biological
Systems",
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school = "Cornell University",
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year = "2014",
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address = "USA",
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month = "27 " # jan,
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keywords = "genetic algorithms, genetic programming, Computational
Biology",
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URL = "http://hdl.handle.net/1813/36187",
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URL = "http://dl.acm.org/citation.cfm?id=2604769",
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abstract = "The quantity of data available to scientists in all
disciplines is increasing at an exponential rate, yet
the insight necessary to distil data into scientific
knowledge must still be supplied by human experts. This
widening gap between data and insight can be bridged
with data-driven modelling, in which computational
methods shift much of the work in creating models from
humans to computers. Traditional approaches to
data-driven modeling require that the form of the model
be fixed in advance, which requires substantial human
effort and limits the complexity of problems that can
be addressed. In contrast, a newer approach to
automated modelling based on evolutionary computation
(EC) removes such restrictions on the form of models.
This free-form modelling has the potential both to
reduce human effort for routine modelling and to make
complex problems more tractable. Although major
advances in EC-based modelling have been made in recent
years, many challenges remain. These challenges include
three features often seen in biological systems:
complex nonlinear behaviour, multiple time scales, and
hidden variables. This work addresses these challenges
by developing new approaches to EC based modelling,
with applications to neuroscience, systems biology,
ecology, and other fields. The contributions of this
work consist of three primary lines of research. In the
first line of research, EC-based methods for the
automated design of analogue electrical circuits are
adapted for the modelling of electrical systems studied
in neurophysiology that display complex, nonlinear
behavior, such as ion channels. In the second line of
research, EC-based methods for symbolic modelling are
extended to facilitate the modelling of dynamical
systems with multiple time scales, such as those found
throughout ecology and other fields. Finally, in the
third line of research, established EC-based algorithms
are extended with the capability to model dynamical
systems as systems of differential equations with
hidden variables, which can contribute in an essential
way to the observed dynamics of a physical system yet
historically have presented a particularly difficult
challenge to automated modelling.",
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notes = "Is this GP? Supervised by Hod Lipson",
- }
Genetic Programming entries for
Theodore W Cornforth
Citations