Learning dynamic models of compartment systems by combining symbolic regression with fuzzy vector envisionment
Created by W.Langdon from
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- @InProceedings{1274050,
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author = "Mehdi Khoury and Frank Guerin and George M. Coghill",
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title = "Learning dynamic models of compartment systems by
combining symbolic regression with fuzzy vector
envisionment",
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booktitle = "Genetic and Evolutionary Computation Conference
{(GECCO2007)} workshop program",
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year = "2007",
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month = "7-11 " # jul,
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editor = "Tina Yu",
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isbn13 = "978-1-59593-698-1",
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pages = "2769--2776",
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address = "London, United Kingdom",
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keywords = "genetic algorithms, genetic programming, dynamic
biological model, dynamic compartmental model, fuzzy
vector envisionment, measurement, metabolic pathways,
semi-quantitative modelling, S-system, symbolic
regression, u-tube",
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URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2769.pdf",
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DOI = "doi:10.1145/1274000.1274050",
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publisher = "ACM Press",
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publisher_address = "New York, NY, USA",
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abstract = "This paper is concerned with the learning of dynamic
models of compartmental systems visualised as networks
of interconnected tanks. This is intended as an
intermediary step to learn more complex dynamic
biological systems such as metabolic pathways. Our
present aim is to learn systems of differential
equations from time series data to capture physical
models of increasing complexity (u-tube, cascaded
tanks, and coupled tanks). To do so, we use Symbolic
Regression in Genetic Programming and combine it with a
fuzzy representation which has inherent differential
capabilities (Fuzzy Vector Envisionment). We use the
ECJ framework to implement the learner. Present results
show that the system can approximate the target models
and that the use of a weighted fitness function seems
to accelerate the learning process.",
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notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No.
910071",
- }
Genetic Programming entries for
Mehdi Khoury
Frank Guerin
George M Coghill
Citations