Inference of differential equation models by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4394

  author =       "Hitoshi Iba",
  title =        "Inference of differential equation models by genetic
  journal =      "Information Sciences",
  year =         "2008",
  volume =       "178",
  number =       "23",
  pages =        "4453--4468",
  month =        "1 " # dec,
  note =         "Special Section: Genetic and Evolutionary Computing",
  keywords =     "genetic algorithms, genetic programming, Ordinary
                 differential equations, Genome informatics",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2008.07.029",
  size =         "16 pages",
  abstract =     "This paper describes an evolutionary method for
                 identifying a causal model from the observed
                 time-series data. We use a system of ordinary
                 differential equations (ODEs) as the causal model. This
                 approach is known to be useful for practical
                 applications, e.g., bioinformatics, chemical reaction
                 models, control theory, etc. To explore the search
                 space more effectively in the course of evolution, the
                 right-hand sides of ODEs are inferred by genetic
                 programming (GP) and the least mean square (LMS) method
                 is used along with the ordinary GP. We apply our method
                 to several target tasks and empirically show how
                 successfully GP infers the systems of ODEs. We also
                 describe an extension of the approach to the inference
                 of differential equation systems with transcendental
  notes =        "The reaction between formaldehyde and carbamide in the
                 aqueous solution gives methylol urea which continues to
                 react with carbamide and form methylene urea. GP with
                 LMS. Forced vibration with damping. ODE. Penalty
                 against bloat. S-expression: power-law exponents for
                 terminal set. MDL. Fourth order Runge-Kutta. Numerical
                 overflow -> poor fitness -> weeded out.

                 Synthetic data.

                 E-CELL SE, Michaelis-Menten law.

                 Levenberg-Marquardt Is genotype {"}repaired{"} or just
                 phenotype? p4467 considers possibility that there is
                 more than one solution.",

Genetic Programming entries for Hitoshi Iba