66
P.A. Whigharn . G.ß. Fogel
interactions. Although many models of ecosystems have been built in the past
decade, especially under the field of artificial life, the conclusions from these
models have been difficult to interpret back to real systems and give predictions
that could be verified experimentally.
More recently there has been interest in the use of exergy and other
thermodynamic measures (Jorgensen 1992; Jorgensen 1992; Jorgensen et al.
1995; Salomonsen and Jensen 1996; Svierezhev 2000) to give global descriptions
of system dynamics. Incorporating these concepts into evolutionary ecosystem
models would be a positive direction for future research.
4.5
Conclusion
The previous sections have described some of the basic applications of
evolutionary computation techniques to various aspects of ecological modelling.
Although there are many areas that have not been given adequate attention, it is
clear that the use of difference and differential equations, the modelling of
cooperation and community structure, the use of space and spatial behavior and
the construction of hierarchical organization are areas where evolutionary
computation techniques match well with ecological modelling. Models from
large-scale behavior of communities, through to the way in which genetic material
evolves in a species, can be studied using these types of models. The future is
extremely positive for these evolutionary techniques to support and extend the
current understanding of ecological processes and functions.
References
Adami C (1998) Introduction to Artificial Life. New York, Springer-Verlag
Angeline PJ, Pollack JB (1993) Competitive Environments Evolve Better Solutions for
Complex Tasks. Proceedings of the 5th International Conference on Genetic
Algorithrns. S. Forrest, San Mateo, CA. Morgan Kaufmann: 264-270
Ashlock D, Smucker MD (1996) Preferential partner selection in an evolutionary study of
prisoner's dilemma. Biosystems 37: 99-125
Bagley J (1967) The Behavior of Adaptive Systems Which Employ Genetic and
Correlation Algorithms, Univ. Michigan, Ann Arbor
Bak P (1996) How Nature Works: The Science of Self-Organized Criticality. New York,
Springer-Verlag
Barricelli NA (1954). Esempi Numerici di Processi di Evoluzione. Methodos: 45-68
Bobbin J, Recknage1 F (2001) Knowledge Discovery for Prediction and Explanation of
Biue-Green Algal Dynamies in Lakes by Evolutionary Algorithrns. Ecol. Modelling
146, 1-3,253-264
P.A. Whigharn . G.ß. Fogel
interactions. Although many models of ecosystems have been built in the past
decade, especially under the field of artificial life, the conclusions from these
models have been difficult to interpret back to real systems and give predictions
that could be verified experimentally.
More recently there has been interest in the use of exergy and other
thermodynamic measures (Jorgensen 1992; Jorgensen 1992; Jorgensen et al.
1995; Salomonsen and Jensen 1996; Svierezhev 2000) to give global descriptions
of system dynamics. Incorporating these concepts into evolutionary ecosystem
models would be a positive direction for future research.
4.5
Conclusion
The previous sections have described some of the basic applications of
evolutionary computation techniques to various aspects of ecological modelling.
Although there are many areas that have not been given adequate attention, it is
clear that the use of difference and differential equations, the modelling of
cooperation and community structure, the use of space and spatial behavior and
the construction of hierarchical organization are areas where evolutionary
computation techniques match well with ecological modelling. Models from
large-scale behavior of communities, through to the way in which genetic material
evolves in a species, can be studied using these types of models. The future is
extremely positive for these evolutionary techniques to support and extend the
current understanding of ecological processes and functions.
References
Adami C (1998) Introduction to Artificial Life. New York, Springer-Verlag
Angeline PJ, Pollack JB (1993) Competitive Environments Evolve Better Solutions for
Complex Tasks. Proceedings of the 5th International Conference on Genetic
Algorithrns. S. Forrest, San Mateo, CA. Morgan Kaufmann: 264-270
Ashlock D, Smucker MD (1996) Preferential partner selection in an evolutionary study of
prisoner's dilemma. Biosystems 37: 99-125
Bagley J (1967) The Behavior of Adaptive Systems Which Employ Genetic and
Correlation Algorithms, Univ. Michigan, Ann Arbor
Bak P (1996) How Nature Works: The Science of Self-Organized Criticality. New York,
Springer-Verlag
Barricelli NA (1954). Esempi Numerici di Processi di Evoluzione. Methodos: 45-68
Bobbin J, Recknage1 F (2001) Knowledge Discovery for Prediction and Explanation of
Biue-Green Algal Dynamies in Lakes by Evolutionary Algorithrns. Ecol. Modelling
146, 1-3,253-264