Ecological Applications of Adaptive Agents

Ecologists are constantly searching for new modelling paradigms in order to simulate realistically the distinct nature of ecosystems by computer models. The ecosystem concept as established by Forbes (1887) had the most forming influence on ecosystem modelling in the past century. It no longer bears close examination as ecosystems like lakes are known to evolve and being driven by exogenous forces rather than existing permanently and in isolation. However, the ecosystem approach resulted in valuable databases from monitoring as well as quantitative and qualitative descriptions of ecosystem dynamics and has made ecology a predictive science (Rigler and Peters 1995). Computer models resulting from the ecosystem concept were mainly based on differential equations (DE) for well-defined ecological entities and processes, adjusted by measured or estimated parameters. Radtke and Straskraba (1980) firstly tried to overcome the rigidity of such models by parameter optimization of ecological goal functions relevant to lake ecosystems as introduced by Straskraba (1977). The authors considered their results as contribution to a structural self-optimising ecosystem model but admitted that more adequate models and more suitable optimisation procedures would be needed to make it a success. In order to overcome model rigidity, Kaluzny and Swartzman (1985) suggested a library of alternative representations of ecological processes from where a simulation model picks the most relevant one for a specific ecological situation.

[1]  F. Recknagel ANNA – Artificial Neural Network model for predicting species abundance and succession of blue-green algae , 1997, Hydrobiologia.

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

[3]  Andrew Hunter,et al.  On Artificial Adaptive Agents Models of Stock Markets , 1997, Simul..

[4]  Sven Erik Jørgensen,et al.  A holistic approach to ecological modelling , 1979 .

[5]  Milan Straškraba Natural control mechanisms in models of aquatic ecosystems , 2001 .

[6]  J. Holland,et al.  Artificial Adaptive Agents in Economic Theory , 1991 .

[7]  Richard A. Park,et al.  A generalized model for simulating lake ecosystems , 1974 .

[8]  G. Booth,et al.  Modelling food web complexity: The consequences of individual-based, spatially explicit behavioural ecology on trophic interactions , 1997, Evolutionary Ecology.

[9]  Sven Erik Jørgensen,et al.  Ecological buffer capacity , 1977 .

[10]  John H. Holland,et al.  Emergence. , 1997, Philosophica.

[11]  J. Bobbin,et al.  Knowledge discovery for prediction and explanation of blue-green algal dynamics in lakes by evolutionary algorithms , 2001 .

[12]  Craig A. Stow,et al.  Science and Limnology , 1995 .

[13]  Peter A. Whigham,et al.  Predicting chlorophyll-a in freshwater lakes by hybridising process-based models and genetic algorithms , 2001 .

[14]  G. Booth Gecko: A continuous 2d world for ecological modeling , 1997 .

[15]  Steven F. Railsback,et al.  Concepts from complex adaptive systems as a framework for individual-based modelling , 2001 .

[16]  W. Silvert Ecological impact classification with fuzzy sets , 1997 .

[17]  Fu-Ren Lin,et al.  Using multi-agent simulation and learning to design new business processes , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[18]  N. Takamura,et al.  Phytoplankton species shift accompanied by transition from nitrogen dependence to phosphorus dependence of primary production in Lake Kasumigaura, Japan , 1992 .

[19]  Sven Erik Jørgensen,et al.  Structural dynamic model , 1986 .

[20]  Peter A. Whigham,et al.  An inductive approach to ecological time series modelling by evolutionary computation , 2001 .

[21]  G. Booth,et al.  BacSim, a simulator for individual-based modelling of bacterial colony growth. , 1998, Microbiology.

[22]  Milan Straškraba,et al.  Self-optimization in a phytoplankton model , 1980 .

[23]  Xin Yao,et al.  A new evolutionary system for evolving artificial neural networks , 1997, IEEE Trans. Neural Networks.

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

[25]  Peter A. Whigham,et al.  Comparative application of artificial neural networks and genetic algorithms for multivariate time-series modelling of algal blooms in freshwater lakes , 2002 .

[26]  Stephen P. Kaluzny,et al.  Simulation experiments comparing alternative process formulations using a factorial design , 1985 .

[27]  D. DeAngelis,et al.  New Computer Models Unify Ecological TheoryComputer simulations show that many ecological patterns can be explained by interactions among individual organisms , 1988 .

[28]  Albrecht Gnauck,et al.  Freshwater Ecosystems: Modelling and Simulation , 1985 .

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

[30]  Stephen Alfred Forbes,et al.  The Lake as a Microcosm , 1925 .