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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.

Notes

Acknowledgements

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

References

  1. Aunger, R. (2002). The electric meme: a new theory of how we think. New York: Free Press. Google Scholar
  2. Baluja, S., Pomerleau, D., & Jochem, T. (1994). Simulating user’s preferences: towards automated artificial evolution for computer generated images. Connection Science, 6, 325–354. CrossRefGoogle Scholar
  3. Basalla, G. (1998). The evolution of technology. Cambridge: Cambridge University Press. Google Scholar
  4. Begon, M., Townsend, C., & Harper, J. (2006). Ecology: from individuals to ecosystems. New York: Wiley-Blackwell. Google Scholar
  5. Bell, S. (1999). Landscape: pattern, perception and process. London: E & F N Spon. Google Scholar
  6. Bentley, P. J., & Corne, D. W. (Eds.) (2002). Creative evolutionary systems. London: Academic Press. Google Scholar
  7. 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
  8. Birkhoff, G. D. (1933). Aesthetic measure. Cambridge: Harvard University Press. zbMATHGoogle Scholar
  9. Boden, M. A. (2010). Creativity and art: three roads to surprise. London: Oxford University Press. Google Scholar
  10. 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/. CrossRefGoogle Scholar
  11. Brown, D. E. (1991). Human universals. New York: McGraw-Hill. Google Scholar
  12. Dahlstedt, P. (2006). A mutasynth in parameter space: interactive composition through evolution. Organised Sound, 6(2), 121–124. Google Scholar
  13. Dawkins, R. (1999). The extended phenotype: the long reach of the gene (rev. ed.). Oxford: Oxford University Press. Google Scholar
  14. De Landa, M. (2000). A thousand years of nonlinear history. Cambridge: MIT Press. Google Scholar
  15. Di Scipio, A. (2003). ‘Sound is the interface’: from interactive to ecosystemic signal processing. Organised Sound, 8(3), 269–277. CrossRefGoogle Scholar
  16. Dissanayake, E. (1995). Homo aestheticus: where art comes from and why. Seattle: University of Washington Press. Google Scholar
  17. 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. CrossRefGoogle Scholar
  18. 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. CrossRefGoogle Scholar
  19. 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
  20. Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing. Natural computing series. Berlin: Springer. zbMATHGoogle Scholar
  21. 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
  22. 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
  23. Fuller, M. (2005). Media ecologies: materialist energies in art and technoculture. Cambridge: MIT Press. Google Scholar
  24. 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
  25. 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
  26. Kaplinsky, J. (2006). Biomimicry versus humanism. Architectural Design, 76(1), 66–71. CrossRefGoogle Scholar
  27. 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
  28. Koren, L. (2010). Which “Aesthetics” do you mean?: ten definitions. Imperfect Publishing. Google Scholar
  29. Lenton, T. M., & Lovelock, J. E. (2001). Daisyworld revisited: quantifying biological effects on planetary self-regulation. Tellus, 53B(3), 288–305. Google Scholar
  30. Luke, S. (2009). Essentials of metaheuristics. Lulu Publishing, Department of Computer Science, George Mason University Google Scholar
  31. 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
  32. Machado, P., & Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence, 16(2), 101–118. zbMATHCrossRefGoogle Scholar
  33. 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
  34. 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
  35. 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
  36. 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. CrossRefGoogle Scholar
  37. 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. CrossRefGoogle Scholar
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. Murray, S. (2011). Design ecologies: editorial. Design Ecologies, 1(1), 7–9. CrossRefGoogle Scholar
  44. 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
  45. 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
  46. Ramachandran, V. S. (2003). The emerging mind. In Reith lectures; 2003. London: BBC in association with profile Books. Google Scholar
  47. 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
  48. 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
  49. Scheiner, S. M., & Willig, M. R. (2008). A general theory of ecology, Theoretical Ecology 1, 21–28. CrossRefGoogle Scholar
  50. Shapshak, T. (2011). Why Nokia got into bed with Microsoft. http://www.bizcommunity.com/Article/410/78/57030.html.
  51. Staudek, T. (2002). Exact aesthetics. Object and scene to message. PhD thesis, Faculty of Informatics, Masaryk University of Brno. Google Scholar
  52. 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
  53. Takagi, H. (2001). Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89, 1275–1296. CrossRefGoogle Scholar
  54. Tansley, A. G. (1939). British ecology during the past quarter-century: the plant community and the ecosystem. Journal of Ecology, 27(2), 513–530. CrossRefGoogle Scholar
  55. Waters, S. (2007). Performance ecosystems: ecological approaches to musical interaction. In EMS07—the ‘languages’ of electroacoustic music, Leicester. Google Scholar
  56. Willis, A. J. (1997). The ecosystem: an evolving concept viewed historically. Functional Ecology, 11(2), 268–271. MathSciNetCrossRefGoogle Scholar
  57. Wilson, S. W. (1999). State of XCS classifier system research (Technical report). Concord, MA. Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Centre for Electronic Media ArtMonash UniversityCaulfield EastAustralia

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