ABSTRACT
This paper discusses the concept of an artificial ecosystem for use in machine-assisted creative discovery. Properties and processes from natural ecosystems are abstracted and applied to the design of creative systems, in a similar way that evolutionary computing methods use the metaphor of Darwinian evolution to solve problems in search and optimisation. The paper examines some appropriate mechanisms and metaphors when applying artificial ecosystems to problems in creative design. General properties and processes of evolutionary artificial ecosystems are presented as a basis for developing individual systems that automate the discovery of novelty without explicit teleological goals. The adaptation of species to fit their environment drives the creative solutions, so the role of the designer shifts to the design of environments. This allows a variety of creative solutions to emerge in simulation without the need for explicit or human-evaluated fitness measures, such as those used in interactive evolution. Two example creative ecosystems are described to highlight the effectiveness of the method presented.
- C. Adami. Ab initio modeling of ecosystems with artificial life. Natural Resource Modeling, 15:133--146, 2002.Google ScholarCross Ref
- W. B. Arthur, S. Durlauf, and D. A. Lane, editors. The economy as an evolving complex system II. Addison-Wesley, Reading, MA, 1997.Google Scholar
- T. Blickle and L. Thiele. A comparison of selection schemes used in genetic algorithms. Technical Report 11, Swiss Federal Institute of Technology, December 1995.Google Scholar
- M. Conrad and H. H. Pattee. Evolution experiments with an artificial ecosystem. Journal of Theoretical Biology, 28:393, 1970.Google ScholarCross Ref
- R. Dawkins. The extended phenotype: the gene as the unit of selection. Freeman, Oxford; San Francisco, 1982.Google Scholar
- A. Dorin. Aesthetic fitness and artificial evolution for the selection of imagery from the mythical infinite library. In J. Kelemen and P. Sosik (eds), Advances in Artificial Life, Proceedings of the Sixth European Conference, ECAL, LNAI 2159:659--668, 2001. Springer-Verlag. Google ScholarDigital Library
- A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Natural Computing Series. Springer, Berlin, 2003. Google ScholarDigital Library
- J. M. Epstein and R. Axtell. Growing Artificial Societies. MIT Press, Cambridge, MA, 1996. Google ScholarDigital Library
- V. Grimm and S. F. Railsback. Individual-based Modeling and Ecology. Princeton Series in Theoretical and Computational Biology. Princeton University Press, 2005.Google Scholar
- J. H. Holland. Hidden order: how adaptation builds complexity. Helix books. Addison-Wesley, Reading, MA, 1995. Google ScholarDigital Library
- S. E. Jurgensen. Integration of Ecosystem Theories: A Pattern. Kluwer Academic Publishers, Dordrecht, second revised edition, 1997.Google Scholar
- K. Kuitenbrouwer and W. Lentz. E-volver. SKOR (Stichting Kunst en Openbare Ruimte/Foundation Art and Public Space), Amsterdam (The Netherlands), 2006.Google Scholar
- T. M. Lenton and J. E. Lovelock. Daisyworld revisited: quantifying biological effects on planetary self-regulation. Tellus, 53B(3):288--305, 2001.Google Scholar
- R. M. May. Stability and Complexity in Model Ecosystems. Princeton University Press, Princeton, NJ, second edition, 2001.Google Scholar
- J. Maynard Smith. Models in Ecology. Cambridge University Press, London, 1974.Google Scholar
- J. McCormack. Eden: An evolutionary sonic ecosystem. In J. Kelemen and P. Sosik (eds), Advances in Artificial Life, Proceedings of the Sixth European Conference, ECAL, LNCS 2159:133--142, 2001. Google ScholarDigital Library
- J. McCormack. On the Evolution of Sonic Ecosystems. In A. Adamatzky and M. Komosinski (eds), Artificial Life Models in Software, pages 211--230. Springer-Verlag, London, 2005.Google ScholarCross Ref
- J. McCormack. Open problems in evolutionary music and art. In F. Rothlauf, et. al. (eds), EvoWorkshops, LNCS 3449, pages 428--436. Springer, 2005. Google ScholarDigital Library
- M. Mitchell and C. E. Taylor. Evolutionary computation: An overview. Annual Review of Ecology and Systematics, 30:593--616, 1999.Google ScholarCross Ref
- M. A. Nowak. Evolutionary Dynamics: exploring the equations of life. The Bekknap Press of Harvard University Press, Cambridge, Massachusetts, and London, England, 2006.Google Scholar
- J. Prophet and G. Selley. Technosphere: "real" time "artificial" life. Leonardo, 34:309--312, 2001.Google ScholarCross Ref
- C. Sommerer and L. Mignonneau. Art as a Living System. In C. Sommerer, and L. Mignonneau (eds), Art@Science , pages 148--161. Springer, Wein, 1998.Google Scholar
- W. Swenson, D. S. Wilson, and R. Elias. Artificial ecosystem selection. PNAS, 97(16):9110--9114, August 1 2000.Google ScholarCross Ref
- H. Takagi. Interactive evolutionary computation: Fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89:1275--1296, Sep 2001.Google ScholarCross Ref
- S. W. Wilson. State of XCS classifier system research. Technical report, Concord, MA, March 1999.Google Scholar
Index Terms
Artificial ecosystems for creative discovery
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