Inference of differential equation models by genetic programming
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 @Article{Iba:2008:IS,

author = "Hitoshi Iba",

title = "Inference of differential equation models by genetic
programming",

journal = "Information Sciences",

year = "2008",

volume = "178",

number = "23",

pages = "44534468",

month = "1 " # dec,

note = "Special Section: Genetic and Evolutionary Computing",

keywords = "genetic algorithms, genetic programming, Ordinary
differential equations, Genome informatics",

ISSN = "00200255",

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
timeseries 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
righthand 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
functions.",

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. Sexpression: powerlaw exponents for
terminal set. MDL. Fourth order RungeKutta. Numerical
overflow > poor fitness > weeded out.
Synthetic data.
ECELL SE, MichaelisMenten law.
LevenbergMarquardt Is genotype {"}repaired{"} or just
phenotype? p4467 considers possibility that there is
more than one solution.",
 }
Genetic Programming entries for
Hitoshi Iba
Citations