Data-Driven, Free-Form Modeling Of Biological Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Cornforth:thesis,
  author =       "Theodore Cornforth",
  title =        "Data-Driven, Free-Form Modeling Of Biological
                 Systems",
  school =       "Cornell University",
  year =         "2014",
  address =      "USA",
  month =        "27 " # jan,
  keywords =     "genetic algorithms, genetic programming, Computational
                 Biology",
  URL =          "http://hdl.handle.net/1813/36187",
  URL =          "http://dl.acm.org/citation.cfm?id=2604769",
  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.",
  notes =        "Is this GP? Supervised by Hod Lipson",
}

Genetic Programming entries for Theodore W Cornforth

Citations