skip to main content
10.1145/1276958.1277017acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

Artificial ecosystems for creative discovery

Published:07 July 2007Publication History

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.

References

  1. C. Adami. Ab initio modeling of ecosystems with artificial life. Natural Resource Modeling, 15:133--146, 2002.Google ScholarGoogle ScholarCross RefCross Ref
  2. W. B. Arthur, S. Durlauf, and D. A. Lane, editors. The economy as an evolving complex system II. Addison-Wesley, Reading, MA, 1997.Google ScholarGoogle Scholar
  3. 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 ScholarGoogle Scholar
  4. M. Conrad and H. H. Pattee. Evolution experiments with an artificial ecosystem. Journal of Theoretical Biology, 28:393, 1970.Google ScholarGoogle ScholarCross RefCross Ref
  5. R. Dawkins. The extended phenotype: the gene as the unit of selection. Freeman, Oxford; San Francisco, 1982.Google ScholarGoogle Scholar
  6. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. Natural Computing Series. Springer, Berlin, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. M. Epstein and R. Axtell. Growing Artificial Societies. MIT Press, Cambridge, MA, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. V. Grimm and S. F. Railsback. Individual-based Modeling and Ecology. Princeton Series in Theoretical and Computational Biology. Princeton University Press, 2005.Google ScholarGoogle Scholar
  10. J. H. Holland. Hidden order: how adaptation builds complexity. Helix books. Addison-Wesley, Reading, MA, 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. S. E. Jurgensen. Integration of Ecosystem Theories: A Pattern. Kluwer Academic Publishers, Dordrecht, second revised edition, 1997.Google ScholarGoogle Scholar
  12. K. Kuitenbrouwer and W. Lentz. E-volver. SKOR (Stichting Kunst en Openbare Ruimte/Foundation Art and Public Space), Amsterdam (The Netherlands), 2006.Google ScholarGoogle Scholar
  13. T. M. Lenton and J. E. Lovelock. Daisyworld revisited: quantifying biological effects on planetary self-regulation. Tellus, 53B(3):288--305, 2001.Google ScholarGoogle Scholar
  14. R. M. May. Stability and Complexity in Model Ecosystems. Princeton University Press, Princeton, NJ, second edition, 2001.Google ScholarGoogle Scholar
  15. J. Maynard Smith. Models in Ecology. Cambridge University Press, London, 1974.Google ScholarGoogle Scholar
  16. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  17. 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 ScholarGoogle ScholarCross RefCross Ref
  18. J. McCormack. Open problems in evolutionary music and art. In F. Rothlauf, et. al. (eds), EvoWorkshops, LNCS 3449, pages 428--436. Springer, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. M. Mitchell and C. E. Taylor. Evolutionary computation: An overview. Annual Review of Ecology and Systematics, 30:593--616, 1999.Google ScholarGoogle ScholarCross RefCross Ref
  20. M. A. Nowak. Evolutionary Dynamics: exploring the equations of life. The Bekknap Press of Harvard University Press, Cambridge, Massachusetts, and London, England, 2006.Google ScholarGoogle Scholar
  21. J. Prophet and G. Selley. Technosphere: "real" time "artificial" life. Leonardo, 34:309--312, 2001.Google ScholarGoogle ScholarCross RefCross Ref
  22. 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 ScholarGoogle Scholar
  23. W. Swenson, D. S. Wilson, and R. Elias. Artificial ecosystem selection. PNAS, 97(16):9110--9114, August 1 2000.Google ScholarGoogle ScholarCross RefCross Ref
  24. 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 ScholarGoogle ScholarCross RefCross Ref
  25. S. W. Wilson. State of XCS classifier system research. Technical report, Concord, MA, March 1999.Google ScholarGoogle Scholar

Index Terms

  1. Artificial ecosystems for creative discovery

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
          July 2007
          2313 pages
          ISBN:9781595936974
          DOI:10.1145/1276958

          Copyright © 2007 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 7 July 2007

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • Article

          Acceptance Rates

          GECCO '07 Paper Acceptance Rate266of577submissions,46%Overall Acceptance Rate1,669of4,410submissions,38%

          Upcoming Conference

          GECCO '24
          Genetic and Evolutionary Computation Conference
          July 14 - 18, 2024
          Melbourne , VIC , Australia

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader