Learning dynamic models of compartment systems by combining symbolic regression with fuzzy vector envisionment

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@InProceedings{1274050,

• author = "Mehdi Khoury and Frank Guerin and George M. Coghill",
• title = "Learning dynamic models of compartment systems by combining symbolic regression with fuzzy vector envisionment",
• booktitle = "Genetic and Evolutionary Computation Conference {(GECCO2007)} workshop program",
• year = "2007",
• month = "7-11 " # jul,
• editor = "Tina Yu",
• isbn13 = "978-1-59593-698-1",
• pages = "2769--2776",
• address = "London, United Kingdom",
• 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",
• URL = "http://gpbib.cs.ucl.ac.uk/gecco2007/docs/p2769.pdf",
• DOI = "doi:10.1145/1274000.1274050",
• publisher = "ACM Press",
• publisher_address = "New York, NY, USA",
• 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.",
• notes = "Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071",

}

Genetic Programming entries for Mehdi Khoury Frank Guerin George M Coghill

Citations