Finding Semi-Quantitative Physical Models Using
Genetic Programming
M. Khoury, F. Guerin, and G. Coghill. The 6th annual UK Workshop on Computational
Intelligence, page 245--252. Leeds, UK, (4-6 September 2006)
Abstract
Model learning often implies exploring a vast search
space of possible hypotheses in the hope of finding a
solution. Qualitative model learners are mostly based
on Inductive Logic Programming (ILP), which is a
systematic method which tends to be well fitted for
exploring solutions in a narrow search space. We
present a semi-quantitative model learner that uses
Genetic Programming (GP), which is well suited for
exploring a broad search space. We learn simple
physical systems based on a formalism involving both
crisp numbers and fuzzy quantity spaces. We use the ECJ
framework,1 and the fitness of a model is set to be
optimal when it covers all positive examples. Several
experiments are performed to learn and reuse models of
physical systems of increasing complexity; firstly a
u-tube, then coupled tanks, and finally cascading
tanks. Results show that the system can approximate the
target models in reasonably good conditions, and that
there is still scope for optimisation.
%0 Conference Paper
%1 khoury:2006:UKCI
%A Khoury, Mehdi
%A Guerin, Frank
%A Coghill, George Macleod
%B The 6th annual UK Workshop on Computational
Intelligence
%C Leeds, UK
%D 2006
%E Wang, Xue Z.
%E Li, Rui Fa
%K algorithms, fuzzy, genetic modelling modelling, programming, qualitative quantitative semi
%P 245--252
%T Finding Semi-Quantitative Physical Models Using
Genetic Programming
%U http://www.csd.abdn.ac.uk/~mkhoury/fuzzy%20evolution2.pdf
%X Model learning often implies exploring a vast search
space of possible hypotheses in the hope of finding a
solution. Qualitative model learners are mostly based
on Inductive Logic Programming (ILP), which is a
systematic method which tends to be well fitted for
exploring solutions in a narrow search space. We
present a semi-quantitative model learner that uses
Genetic Programming (GP), which is well suited for
exploring a broad search space. We learn simple
physical systems based on a formalism involving both
crisp numbers and fuzzy quantity spaces. We use the ECJ
framework,1 and the fitness of a model is set to be
optimal when it covers all positive examples. Several
experiments are performed to learn and reuse models of
physical systems of increasing complexity; firstly a
u-tube, then coupled tanks, and finally cascading
tanks. Results show that the system can approximate the
target models in reasonably good conditions, and that
there is still scope for optimisation.
@inproceedings{khoury:2006:UKCI,
abstract = {Model learning often implies exploring a vast search
space of possible hypotheses in the hope of finding a
solution. Qualitative model learners are mostly based
on Inductive Logic Programming (ILP), which is a
systematic method which tends to be well fitted for
exploring solutions in a narrow search space. We
present a semi-quantitative model learner that uses
Genetic Programming (GP), which is well suited for
exploring a broad search space. We learn simple
physical systems based on a formalism involving both
crisp numbers and fuzzy quantity spaces. We use the ECJ
framework,1 and the fitness of a model is set to be
optimal when it covers all positive examples. Several
experiments are performed to learn and reuse models of
physical systems of increasing complexity; firstly a
u-tube, then coupled tanks, and finally cascading
tanks. Results show that the system can approximate the
target models in reasonably good conditions, and that
there is still scope for optimisation.},
added-at = {2008-06-19T17:35:00.000+0200},
address = {Leeds, UK},
author = {Khoury, Mehdi and Guerin, Frank and Coghill, George Macleod},
biburl = {https://www.bibsonomy.org/bibtex/2ab0059d65aed9f6734ae146140019b47/brazovayeye},
booktitle = {The 6th annual UK Workshop on Computational
Intelligence},
editor = {Wang, Xue Z. and Li, Rui Fa},
interhash = {d19a434c141b5f15d9309439efb04b55},
intrahash = {ab0059d65aed9f6734ae146140019b47},
keywords = {algorithms, fuzzy, genetic modelling modelling, programming, qualitative quantitative semi},
month = {4-6 September},
pages = {245--252},
size = {8 pages},
timestamp = {2008-06-19T17:43:13.000+0200},
title = {Finding Semi-Quantitative Physical Models Using
Genetic Programming},
url = {http://www.csd.abdn.ac.uk/~mkhoury/fuzzy%20evolution2.pdf},
year = 2006
}