%0 Conference Paper %1 1274050 %A Khoury, Mehdi %A Guerin, Frank %A Coghill, George M. %B Genetic and Evolutionary Computation Conference (GECCO2007) workshop program %C London, United Kingdom %D 2007 %E Yu, Tina %I ACM Press %K S-system, algorithms, biological compartmental dynamic envisionment, fuzzy genetic measurement, metabolic model, modelling, pathways, programming, regression, semi-quantitative symbolic u-tube vector %P 2769--2776 %T Learning dynamic models of compartment systems by combining symbolic regression with fuzzy vector envisionment %U http://doi.acm.org/10.1145/1274000.1274050 %X 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.

@inproceedings{1274050, 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {London, United Kingdom}, author = {Khoury, Mehdi and Guerin, Frank and Coghill, George M.}, biburl = {https://www.bibsonomy.org/bibtex/24a802c1923d03d1bcfbe9de50519675c/brazovayeye}, booktitle = {Genetic and Evolutionary Computation Conference {(GECCO2007)} workshop program}, editor = {Yu, Tina}, interhash = {1e19b09ff291726cc47bd64402f874c5}, intrahash = {4a802c1923d03d1bcfbe9de50519675c}, isbn13 = {978-1-59593-698-1}, keywords = {S-system, algorithms, biological compartmental dynamic envisionment, fuzzy genetic measurement, metabolic model, modelling, pathways, programming, regression, semi-quantitative symbolic u-tube vector}, month = {7-11 July}, notes = {Distributed on CD-ROM at GECCO-2007 ACM Order No. 910071}, pages = {2769--2776}, publisher = {ACM Press}, publisher_address = {New York, NY, USA}, timestamp = {2008-06-19T17:43:14.000+0200}, title = {Learning dynamic models of compartment systems by combining symbolic regression with fuzzy vector envisionment}, url = {http://doi.acm.org/10.1145/1274000.1274050}, year = 2007 }