An inductive approach to ecological time series modelling by evolutionary computation

Building time series models for ecological systems that can be physically interpreted is important both for understanding the dynamics of these natural systems and the development of decision support systems. This work describes the application of an evolutionary computation framework for the discovery of predictive equations and rules for phytoplankton abundance in freshwater lakes from time series data. The suggested framework evolves several different equations and rules, based on limnological and climate variables. The results demonstrate that non-linear processes in natural systems may be successfully modelled through the use of evolutionary computation techniques. Further, it shows that a grammar based genetic programming system may be used as a tool for exploring the driving processes underlying freshwater system dynamics.

[1]  G. Harris.  Phytoplankton Ecology: Structure, Function and Fluctuation , 1986 .

[2]  Colin S. Reynolds,et al.  The ecology of freshwater phytoplankton , 1984 .

[3]  John R. Koza,et al.  Concept Formation and Decision Tree Induction Using the Genetic Programming Paradigm , 1990, PPSN.

[4]  Peter A. Whigham,et al.  Induction of a marsupial density model using genetic programming and spatial relationships , 2000 .

[5]  Peter A. Whigham,et al.  Time series modeling using genetic programming: an application to rainfall-runoff models , 1999 .

[6]  F. Recknagel,et al.  Artificial neural network approach for modelling and prediction of algal blooms , 1997 .

[7]  Peter A. Whigham.  Inductive bias and genetic programming , 1995 .

[8]  Saso Dzeroski,et al.  Declarative Bias in Equation Discovery , 1997, ICML.

[9]  Eamonn J. Keogh,et al.  Relevance feedback retrieval of time series data , 1999, SIGIR '99.

[10]  Friedrich Recknagel,et al.  Modelling and prediction of phyto‐ and zooplankton dynamics in Lake Kasumigaura by artificial neural networks , 1998 .

[11]  Sašo Džeroski,et al.  Modelling and prediction of phytoplankton growth with equation discovery , 1998 .

[12]  Gerald P. Roston,et al.  Using the Genetic Design Methodology for Structure Configuration , 1996 .

[13]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[14]  Xin Yao,et al.  Time Series Prediction by Using Negatively Correlated Neural Networks , 1998, SEAL.

[15]  Peter A. Whigham,et al.  Evolving difference equations to model freshwater phytoplankton , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[16]  Joseph Shapiro,et al.  Blue-Green Algae: Why They Become Dominant , 1973, Science.

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[18]  Peter A. Whigham,et al.  Predictive modelling of plankton dynamics in freshwater lakes using genetic programming , 1999 .

[19]  J. K. Kinnear,et al.  Advances in Genetic Programming , 1994 .

[20]  A. E. Irish,et al.  Modelling freshwater phytoplankton communities: an exercise in validation , 2000 .