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.