Inference of hidden variables in systems of differential equations with genetic programming

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

  author =       "Theodore W. Cornforth and Hod Lipson",
  title =        "Inference of hidden variables in systems of
                 differential equations with genetic programming",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2013",
  volume =       "14",
  number =       "2",
  pages =        "155--190",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Ordinary
                 differential equations, Hidden variables, Modelling,
                 Symbolic identification",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-012-9175-4",
  size =         "36",
  abstract =     "The data-driven modelling of dynamical systems is an
                 important scientific activity, and many studies have
                 applied genetic programming (GP) to the task of
                 automatically constructing such models in the form of
                 systems of ordinary differential equations (ODEs).
                 These previous studies assumed that data measurements
                 were available for all variables in the system, whereas
                 in real-world settings, it is typically the case that
                 one or more variables are unmeasured or hidden. Here,
                 we investigate the prospect of automatically
                 constructing ODE models of dynamical systems from time
                 series data with GP in the presence of hidden
                 variables. Several examples with both synthetic and
                 physical systems demonstrate the unique challenges of
                 this problem and the circumstances under which it is
                 possible to reverse-engineer both the form and
                 parameters of ODE models with hidden variables.",
  notes =        "Error = normalised scaled mean absolute error (sum
                 absolute error divided by observed standard deviation).
                 GP individual = n trees (for n 'equations and dynamic
                 variables'). Trees limited to depth five. Pop=96.
                 Steady state population but (multi-objective) selection
                 against old guys 'Age fitness Pareto Algorithm'
                 \cite{Schmidt:2010:GPTP} \cite{Schmidt:2010:gecco}.
                 Hill climbing to choose constants. 'Pareto hall of
                 fame'. Evolved GP trees simplified by Matlab symbolic
                 math toolbox.",

Genetic Programming entries for Theodore W Cornforth Hod Lipson