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

Authors:
Mehdi Khoury;Frank Guerin;George M. Coghill
Affiliations:
University of Aberdeen;University of Aberdeen;University of Aberdeen
Venue:
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Year:
2007

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The Knowledge Engineering Review

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Abstract

This paper is concerned with the learning of dynamic models of compartmental systems visualized 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 ECJ1 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.