Skip to main content

Creative Ecosystems

  • Chapter

Abstract

Traditional evolutionary approaches to computer creativity focus on optimisation, that is they define some criteria that allows the ranking of individuals in a population in terms of their suitability for a particular task. The problem for creative applications is that creativity is rarely thought of as a single optimisation. For example, could you come up with an algorithm for ranking music or painting? The difficulty is that these broad categories are shifting and subjective: I might argue that Mozart is more musically creative than Lady Gaga, but others may disagree. Objective, fine-grained ranking of all possible music is impossible, even for humans. I will show how reconceptualising the exploration of a creative space using an “ecosystemic” approach can lead to more open and potentially creative possibilities. For explanatory purposes, I will use some successful examples that are simple enough to explain succinctly, yet still exhibit the features necessary to demonstrate the advantages of this approach.

Keywords

  • Niche Construction
  • Conceptual Space
  • Evolutionary Computing
  • Aesthetic Judgement
  • Density Preference

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-642-31727-9_2
  • Chapter length: 22 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   99.00
Price excludes VAT (USA)
  • ISBN: 978-3-642-31727-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   129.00
Price excludes VAT (USA)
Hardcover Book
USD   139.99
Price excludes VAT (USA)
Fig. 2.1
Fig. 2.2
Fig. 2.3
Fig. 2.4
Fig. 2.5
Fig. 2.6
Fig. 2.7
Fig. 2.8

Notes

  1. 1.

    By my estimates, about 5×10−1444925 % for images of modest dimensions, far beyond astronomically small.

  2. 2.

    By “generative mechanism” I am technically referring to the genotype and the mechanism that expresses it into a phenotype.

  3. 3.

    The mechanism can include the ability to self-modify, change, or learn.

  4. 4.

    We might think of “viable” as meaning being able to effectively express a living organism from a zygote or through mitosis of a parent cell. But this is problematic for many reasons, most of which are too tangential to the argument to list here.

  5. 5.

    This issue is a topic of discussion in Chap. 4.

  6. 6.

    Although there are exceptions where the IGA has proved useful to expert users as well, e.g. Dahlstedt (2006), McCormack (2008a).

  7. 7.

    Danish biologist Eugen Warming is also attributed as the founder of the science of Ecology.

  8. 8.

    Autotrophs, such as plants, produce organic substances from simpler inorganic substances, such as carbon dioxide; heterotrophs unable to perform such conversions, require organic substances as a source of energy.

  9. 9.

    See their website at: http://www.xs4all.nl/~notnot/index.html.

  10. 10.

    Which has included over the last few years: Oliver Bown, Palle Dahlstedt, Alan Dorin, Alice Eldridge, Taras Kowaliw, Aidan Lane, Gordon Monro, Ben Porter and Mitchell Whitelaw.

  11. 11.

    Chapter 4 discusses this issue in more detail.

  12. 12.

    Reminiscent of Kodak founder George Eastman’s famous tag line of 1888 for the Kodak No. 1 camera: “You press the button, we do the rest”.

References

  • Aunger, R. (2002). The electric meme: a new theory of how we think. New York: Free Press.

    Google Scholar 

  • Baluja, S., Pomerleau, D., & Jochem, T. (1994). Simulating user’s preferences: towards automated artificial evolution for computer generated images. Connection Science, 6, 325–354.

    CrossRef  Google Scholar 

  • Basalla, G. (1998). The evolution of technology. Cambridge: Cambridge University Press.

    Google Scholar 

  • Begon, M., Townsend, C., & Harper, J. (2006). Ecology: from individuals to ecosystems. New York: Wiley-Blackwell.

    Google Scholar 

  • Bell, S. (1999). Landscape: pattern, perception and process. London: E & F N Spon.

    Google Scholar 

  • Bentley, P. J., & Corne, D. W. (Eds.) (2002). Creative evolutionary systems. London: Academic Press.

    Google Scholar 

  • Bird, J., Husbands, P., Perris, M., Bigge, B., & Brown, P. (2008). Implicit fitness functions for evolving a drawing robot. In M. Giacobini et al. (Eds.), Lecture notes in computer science: Vol. 4974. Applications of evolutionary computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Proceedings, Naples, Italy, March 26–28, 2008 (pp. 473–478). Berlin: Springer.

    Google Scholar 

  • Birkhoff, G. D. (1933). Aesthetic measure. Cambridge: Harvard University Press.

    MATH  Google Scholar 

  • Boden, M. A. (2010). Creativity and art: three roads to surprise. London: Oxford University Press.

    Google Scholar 

  • Bown, O., & McCormack, J. (2010). Taming nature: tapping the creative potential of ecosystem models in the arts. Digital Creativity, 21(4), 215–231. http://www.csse.monash.edu.au/~jonmc/resources/DC2010/.

    CrossRef  Google Scholar 

  • Brown, D. E. (1991). Human universals. New York: McGraw-Hill.

    Google Scholar 

  • Dahlstedt, P. (2006). A mutasynth in parameter space: interactive composition through evolution. Organised Sound, 6(2), 121–124.

    Google Scholar 

  • Dawkins, R. (1999). The extended phenotype: the long reach of the gene (rev. ed.). Oxford: Oxford University Press.

    Google Scholar 

  • De Landa, M. (2000). A thousand years of nonlinear history. Cambridge: MIT Press.

    Google Scholar 

  • Di Scipio, A. (2003). ‘Sound is the interface’: from interactive to ecosystemic signal processing. Organised Sound, 8(3), 269–277.

    CrossRef  Google Scholar 

  • Dissanayake, E. (1995). Homo aestheticus: where art comes from and why. Seattle: University of Washington Press.

    Google Scholar 

  • Dorin, A. (2001). Aesthetic fitness and artificial evolution for the selection of imagery from the mythical infinite library. In J. Kelemen & P. Sosík (Eds.), LNAI: Vol. 2159. Advances in artificial life (pp. 659–668). Prague: Springer. http://www.csse.monash.edu.au/~aland/PAPERS/aestheticFitness_ECAL2001.pdf.

    CrossRef  Google Scholar 

  • Driessens, E., & Verstappen, M. (2008). Natural processes and artificial procedures. In P. F. Hingston, L. C. Barone & Z. Michalewicz (Eds.), Natural computing series. Design by evolution: advances in evolutionary design (pp. 101–120). Berlin: Springer.

    CrossRef  Google Scholar 

  • Dutton, D. (2002). Aesthetic universals. In B. Gaut & D. M. Lopes (Eds.), The Routledge companion to aesthetics. London: Routledge. http://www.denisdutton.com/universals.htm.

    Google Scholar 

  • Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing. Natural computing series. Berlin: Springer.

    MATH  Google Scholar 

  • Eldridge, A. C., & Dorin, A. (2009). Filterscape: energy recycling in a creative ecosystem. In M. Giacobini et al. (Eds.), Lecture notes in computer science: Vol. 5484. Applications of evolutionary computing, EvoWorkshops 2009: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Proceedings, Tübingen, Germany, April 15–17, 2009 (pp. 508–517). Berlin: Springer.

    Google Scholar 

  • Eldridge, A. C., Dorin, A., & McCormack, J. (2008). Manipulating artificial ecosystems. In M. Giacobini et al. (Eds.), Lecture notes in computer science: Vol. 4974. Applications of evolutionary computing,EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Proceedings, Naples, Italy, March 26–28, 2008 (pp. 392–401). Berlin: Springer.

    Google Scholar 

  • Fuller, M. (2005). Media ecologies: materialist energies in art and technoculture. Cambridge: MIT Press.

    Google Scholar 

  • Gamma, E., Helm, R., Johnson, R., & Vlissides, J. M. (1995). Design patterns: elements of reusable object-oriented software. Addison-Wesley professional computing series. Reading: Addison-Wesley.

    Google Scholar 

  • Harvey, I. (2004). Homeostasis and rein control: from daisyworld to active perception. In J. B. Pollack, M. A. Bedau, P. Husbands, T. Ikegami & R. A. Watson (Eds.), Ninth international conference on artificial life (pp. 309–314). Cambridge: MIT Press.

    Google Scholar 

  • Kaplinsky, J. (2006). Biomimicry versus humanism. Architectural Design, 76(1), 66–71.

    CrossRef  Google Scholar 

  • Keane, A. J., & Brown, S. M. (1996). The design of a satellite boom with enhanced vibration performance using genetic algorithm techniques. In I. C. Parmee (Ed.), Conference on adaptive computing in engineering design and control 96, P.E.D.C. (pp. 107–113).

    Google Scholar 

  • Koren, L. (2010). Which “Aesthetics” do you mean?: ten definitions. Imperfect Publishing.

    Google Scholar 

  • Lenton, T. M., & Lovelock, J. E. (2001). Daisyworld revisited: quantifying biological effects on planetary self-regulation. Tellus, 53B(3), 288–305.

    Google Scholar 

  • Luke, S. (2009). Essentials of metaheuristics. Lulu Publishing, Department of Computer Science, George Mason University

    Google Scholar 

  • Lumsden, C. J. (1999). Evolving creative minds: stories and mechanisms. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 153–169). Cambridge: Cambridge University Press. Chap. 8.

    Google Scholar 

  • Machado, P., & Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence, 16(2), 101–118.

    MATH  CrossRef  Google Scholar 

  • Machado, P., Romero, J., & Manaris, B. (2008). Experiments in computational aesthetics. In J. Romero & P. Machado (Eds.), The art of artificial evolution: a handbook on evolutionary art and music (pp. 381–415). Berlin: Springer.

    Google Scholar 

  • Martindale, C. (1999). Biological bases of creativity. In R. J. Sternberg (Ed.), Handbook of creativity (pp. 137–152). Cambridge: Cambridge University Press. Chap. 7.

    Google Scholar 

  • McCormack, J. (2001). Eden: an evolutionary sonic ecosystem. In Lecture notes in computer science: Vol. 2159. Advances in artificial life, proceedings of the sixth European conference, ECAL (pp. 133–142).

    Google Scholar 

  • McCormack, J. (2005). On the evolution of sonic ecosystems. In Artificial life models in software (pp. 211–230). London: Springer. http://www.springeronline.com/sgw/cda/frontpage/0,11855,5-40007-22-39144451-0,00.html.

    CrossRef  Google Scholar 

  • McCormack, J. (2007a). Artificial ecosystems for creative discovery. In Proceedings of the 9th annual conference on genetic and evolutionary computation (GECCO 2007) (pp. 301–307). New York: ACM.

    CrossRef  Google Scholar 

  • McCormack, J. (2007b). Creative ecosystems. In A. Cardoso & G. Wiggins (Eds.), Proceedings of the 4th international joint workshop on computational creativity (pp. 129–136).

    Google Scholar 

  • McCormack, J. (2008a). Evolutionary L-systems. In P. F. Hingston, L. C. Barone & Z. Michalewicz (Eds.), Natural computing series. Design by evolution: advances in evolutionary design (pp. 168–196). Berlin: Springer.

    Google Scholar 

  • McCormack, J. (2008b). Facing the future: evolutionary possibilities for human-machine creativity. In P. Machado & J. Romero (Eds.), The art of artificial evolution: a handbook on evolutionary art and music (pp. 417–451). Berlin: Springer.

    Google Scholar 

  • McCormack, J. (2010). Enhancing creativity with niche construction. In H. Fellerman et al. (Eds.), Artificial life XII (pp. 525–532). Cambridge: MIT Press.

    Google Scholar 

  • McCormack, J., Dorin, A., & Innocent, T. (2004). Generative design: a paradigm for design research. In J. Redmond, D. Durling, & A. de Bono (Eds.), Futureground, vol. 1: abstracts, 2: proceedings (p. 156). Melbourne: Design Research Society.

    Google Scholar 

  • Murray, S. (2011). Design ecologies: editorial. Design Ecologies, 1(1), 7–9.

    CrossRef  Google Scholar 

  • Odling-Smee, F. J., Laland, K. N., & Feldman, M. W. (2003). Niche construction: the neglected process in evolution. Monographs in population biology. Princeton: Princeton University Press.

    Google Scholar 

  • Perkins, D. N. (1996). Creativity: beyond the Darwinian paradigm. In M. Boden (Ed.), Dimensions of creativity (pp. 119–142). Cambridge: MIT Press. Chap. 5.

    Google Scholar 

  • Ramachandran, V. S. (2003). The emerging mind. In Reith lectures; 2003. London: BBC in association with profile Books.

    Google Scholar 

  • Ramachandran, V. S., & Hirstein, W. (1999). The science of art: a neurological theory of aesthetic experience. Journal of Consciousness Studies, 6, 15–51.

    Google Scholar 

  • Romero, J., & Machado, P. (Eds.) (2008). The art of artificial evolution: a handbook on evolutionary art and music. Natural computing series. Berlin: Springer.

    Google Scholar 

  • Scheiner, S. M., & Willig, M. R. (2008). A general theory of ecology, Theoretical Ecology 1, 21–28.

    CrossRef  Google Scholar 

  • Shapshak, T. (2011). Why Nokia got into bed with Microsoft. http://www.bizcommunity.com/Article/410/78/57030.html.

  • Staudek, T. (2002). Exact aesthetics. Object and scene to message. PhD thesis, Faculty of Informatics, Masaryk University of Brno.

    Google Scholar 

  • Svangåard, N., & Nordin, P. (2004). Automated aesthetic selection of evolutionary art by distance based classification of genomes and phenomes using the universal similarity metric. In G. R. Raidl, S. Cagnoni, J. Branke, D. Corne, R. Drechsler, Y. Jin, C. G. Johnson, P. Machado, E. Marchiori, F. Rothlauf, G. D. Smith & G. Squillero (Eds.), Lecture notes in computer science: Vol. 3005. EvoWorkshops 2004 (pp. 447–456). Berlin: Springer.

    Google Scholar 

  • Takagi, H. (2001). Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89, 1275–1296.

    CrossRef  Google Scholar 

  • Tansley, A. G. (1939). British ecology during the past quarter-century: the plant community and the ecosystem. Journal of Ecology, 27(2), 513–530.

    CrossRef  Google Scholar 

  • Waters, S. (2007). Performance ecosystems: ecological approaches to musical interaction. In EMS07—the ‘languages’ of electroacoustic music, Leicester.

    Google Scholar 

  • Willis, A. J. (1997). The ecosystem: an evolving concept viewed historically. Functional Ecology, 11(2), 268–271.

    MathSciNet  CrossRef  Google Scholar 

  • Wilson, S. W. (1999). State of XCS classifier system research (Technical report). Concord, MA.

    Google Scholar 

Download references

Acknowledgements

This research was supported by Australian Research Council Discovery Grants DP0877320 and DP1094064.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jon McCormack .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

McCormack, J. (2012). Creative Ecosystems. In: McCormack, J., d’Inverno, M. (eds) Computers and Creativity. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31727-9_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-31727-9_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31726-2

  • Online ISBN: 978-3-642-31727-9

  • eBook Packages: Computer ScienceComputer Science (R0)