Computational Aesthetic Evaluation: Past and Future

  • Philip Galanter


Human creativity typically includes a self-critical aspect that guides innovation towards a productive end. This chapter offers a brief history of, and outlook for, computational aesthetic evaluation by digital systems as a contribution towards potential machine creativity. First, computational aesthetic evaluation is defined and the difficult nature of the problem is outlined. Next, a brief history of computational aesthetic evaluation is offered, including the use of formulaic and geometric theories; design principles; evolutionary systems including extensions such as coevolution, niche construction, agent swarm behaviour and curiosity; artificial neural networks and connectionist models; and complexity models. Following this historical review, a number of possible contributions towards future computational aesthetic evaluation methods are noted. Included are insights from evolutionary psychology; models of human aesthetics from psychologists such as Arnheim, Berlyne, and Martindale; a quick look at empirical studies of human aesthetics; the nascent field of neuroaesthetics; new connectionist computing models such as hierarchical temporal memory; and computer architectures for evolvable hardware. Finally, it is suggested that the effective complexity paradigm is more useful than information or algorithmic complexity when thinking about aesthetics.


Artificial Neural Network Fitness Function Field Programmable Gate Array Dynamic Time Warping Niche Construction 
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.



My interest in writing this chapter began at the “Computational Creativity: An Interdisciplinary Approach” seminar in July of 2009 at the Schloss Dagstuhl—Leibniz Center for Informatics. I would like to thank Margaret Boden, Mark d’Inverno and Jon McCormack for organising the seminar. In addition my thanks go to my fellow members of the “Evaluation” discussion group at the seminar including Margaret Boden, David Brown, Paul Brown, Harold Cohen, and Oliver Deussen. Finally I enjoyed and appreciated the lively post-seminar e-mail discussion of related topics with David Brown, Paul Brown, Harold Cohen, Jon McCormack, and Frieder Nake. Please note, however, that any matters of opinion or error in this chapter are purely my own.


  1. Aguilar, C., & Lipson, H. (2008). A robotic system for interpreting images into painted artwork. In C. Soddu (Ed.), International conference on generative art (Vol. 11). Generative Design Lab, Milan Polytechnic. Google Scholar
  2. Aldiss, B. (2002). The mechanical turk—the true story of the chess-playing machine that changed the world. TLS-the Times Literary Supplement, 5170, 33. Google Scholar
  3. Alsing, R. (2008). Genetic programming: evolution of Mona Lisa. Accessed 7/21/2011.
  4. Arnheim, R. (1974). Art and visual perception: a psychology of the creative eye (new, expanded and revised ed.) Berkeley: University of California Press. Google Scholar
  5. Atiyeh, B., & Hayek, S. (2008). Numeric expression of aesthetics and beauty. Aesthetic Plastic Surgery, 32(2), 209–216. CrossRefGoogle Scholar
  6. Axelsson, O. (2007). Individual differences in preferences to photographs. Psychology of Aesthetics, Creativity, and the Arts, 1(2), 61–72. CrossRefGoogle Scholar
  7. Baluja, S., Pomerleau, D., & Jochem, T. (1994). Towards automated artificial evolution for computer-generated images. Connection Science, 6(1), 325–354. CrossRefGoogle Scholar
  8. Bense, M. (1965). Aesthetica; Einfhrung in die neue Aesthetik. Baden-Baden: Agis-Verlag. Google Scholar
  9. Bentley, P., & Corne, D. (2002). An introduction to creative evolutionary systems. In P. Bentley & D. Corne (Eds.), Creative evolutionary systems (pp. 1–75). San Francisco/San Diego: Morgan Kaufmann/Academic Press. CrossRefGoogle Scholar
  10. Berlyne, D. E. (1960). Conflict, arousal, and curiosity. New York: McGraw-Hill. CrossRefGoogle Scholar
  11. Berlyne, D. E. (1971). Aesthetics and psychobiology. New York: Appleton-Century-Crofts. Google Scholar
  12. Birkhoff, G. D. (1933). Aesthetic measure. Cambridge: Harvard University Press. zbMATHGoogle Scholar
  13. Boselie, F., & Leeuwenberg, E. (1985). Birkhoff revisited: beauty as a function of effect and means. The American Journal of Psychology, 98(1), 1–39. CrossRefGoogle Scholar
  14. Carroll, N. (1999). Philosophy of art: a contemporary introduction, Routledge contemporary introductions to philosophy. London: Routledge. Google Scholar
  15. Casti, J. L. (1994). Complexification: explaining a paradoxical world through the science of surprise (1st ed.). New York: HarperCollins. Google Scholar
  16. Chaitin, G. J. (1966). On the length of programs for computing finite binary sequences. Journal of the ACM, 13(4), 547–569. MathSciNetzbMATHCrossRefGoogle Scholar
  17. Ciesielski, V. (2007). Evolution of animated photomosaics. In Lecture notes in computer science (vol. 4448, pp. 498–507). Google Scholar
  18. Collier, G. L. (2002). Why does music express only some emotions? A test of a philosophical theory. Empirical Studies of the Arts, 20(1), 21–31. CrossRefGoogle Scholar
  19. Cupchik, G. C. (2007). A critical reflection on Arnheim’s gestalt theory of aesthetics. Psychology of Aesthetics, Creativity, and the Arts, 1(1), 16–24. CrossRefGoogle Scholar
  20. Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2006). Studying aesthetics in photographic images using a computational approach. In Proceedings: Vol. 3953. ECCV 2006 (Pt. 3, pp. 288–301). Google Scholar
  21. Datta, R., Li, J., & Wang, J. Z. (2007). Learning the consensus on visual quality for next-generation image management. In Proceedings of the ACM multimedia conference (pp. 533–536). New York: ACM. Google Scholar
  22. Davis, T., & Rebelo, P. (2007). Environments for sonic ecologies. In Applications of evolutionary computing (pp. 508–516). Berlin: Springer. Google Scholar
  23. De Prisco, R., & Zaccagnino, R. (2009). An evolutionary music composer algorithm for bass harmonization. In Applications of evolutionary computing (Vol. 5484, pp. 567–572). Berlin: Springer. CrossRefGoogle Scholar
  24. Dorin, A. (2005). Enriching aesthetics with artificial life. In A. Adamatzky & M. Komosinski (Eds.), Artificial life models in software (pp. 415–431). London: Springer. Chap. 14. Google Scholar
  25. Draves, S. (2005). The electric sheep screen-saver: A case study in aesthetic evolution. In Lecture notes in computer science: Vol. 3449. Evo workshops (pp. 458–467). Google Scholar
  26. Dutton, D. (2009). The art instinct: beauty, pleasure, and human evolution (1st U.S. ed.). New York: Bloomsbury Press. Google Scholar
  27. Elzenga, R. N., & Pontecorvo, M. S. (1999). Arties: meta-design as evolving colonies of artistic agents. Generative Design Lab. Google Scholar
  28. De Felice, F., & Fabio Abbattista, F. S. (2002). Genorchestra: an interactive evolutionary agent for musical composition. In C. Soddu (Ed.), International conference on generative art (Vol. 5). Generative Design Lab, Milan Polytechnic. Google Scholar
  29. Feldman, D. P., & Crutchfield, J. (1998). A survey of complexity measures. Santa Fe Institute. Google Scholar
  30. Ficici, S., & Pollack, J. (1998). Challenges in co-evolutionary learning; arms-race dynamics, open-endedness, and mediocre stable states. In C. Adami (Ed.), Artificial life VI: proceedings of the sixth international conference on artificial life (pp. 238–247). Cambridge: MIT Press. Google Scholar
  31. Fogel, L. J. (1999). Intelligence through simulated evolution: forty years of evolutionary programming. Wiley series on intelligent systems. New York: Wiley. zbMATHGoogle Scholar
  32. Fornari, J. (2007). Creating soundscapes using evolutionary spatial control. In Lecture notes in computer science (Vol. 4448, pp. 517–526). Google Scholar
  33. Galanter, P. (2010). The problem with evolutionary art is. In C. DiChio, A. Brabazon, G. A. DiCaro, M. Ebner, M. Farooq, A. Fink, J. Grahl, G. Greenfield, P. Machado, M. O’Neill, E. Tarantino, & N. Urquhart (Eds.), Lecture notes in computer science: Vol. 6025. Applications of evolutionary computation, pt. II, proceedings (pp. 321–330). Berlin: Springer. CrossRefGoogle Scholar
  34. Gartland-Jones, A. (2002). Can a genetic algorithm think like a composer? In C. Soddu (Ed.), International conference on generative art (Vol. 5). Generative Design Lab, Milan Polytechnic. Google Scholar
  35. Gedeon, T. (2008). Neural network for modeling esthetic selection. In Lecture notes in computer science (Vol. 4985(2), pp. 666–674). Google Scholar
  36. Gell-Mann, M. (1995). What is complexity? Complexity, 1(1), 16–19. MathSciNetzbMATHGoogle Scholar
  37. Glette, K., Torresen, J., & Yasunaga, M. (2007). An online EHW pattern recognition system applied to face image recognition. In Applications of evolutionary computing (pp. 271–280). Berlin: Springer. Google Scholar
  38. Greenfeld, G. R. (2003). Evolving aesthetic images using multiobjective optimization. In CEC: 2003 congress on evolutionary computation (pp. 1903–1909). CrossRefGoogle Scholar
  39. Greenfield, G. (2005a). Evolutionary methods for ant colony paintings. In Lecture notes in computer science: Vol. 3449. Evo workshops (pp. 478–487). Google Scholar
  40. Greenfield, G. (2005b). On the origins of the term computational aesthetics. In Computational aesthetics 2005: Eurographics workshop on computational aesthetics in graphics, visualization and imaging, Girona, Spain, 18–20 May, 2005. Eurographics. Google Scholar
  41. Greenfield, G. (2008a). Evolved diffusion limited aggregation compositions. In Applications of evolutionary computing (pp. 402–411). New York: Springer. CrossRefGoogle Scholar
  42. Greenfield, G. R. (2004). The void series—generative art using regulatory genes. In C. Soddu (Ed.), International conference on generative art (Vol. 7). Generative Design Lab, Milan Polytechnic. Google Scholar
  43. Greenfield, G. R. (2008b). Co-evolutionary methods in evolutionary art. In J. Romero & P. Machado (Eds.), Natural computing series. The art of artificial evolution (pp. 357–380). Berlin: Springer. CrossRefGoogle Scholar
  44. Hawkins, J., & Blakeslee, S. (2004). On intelligence (1st ed.). New York: Times Books. Google Scholar
  45. Hazan, A., Ramirez, R., Maestre, E., Perez, A., & Pertusa, A. (2006). Modelling expressive performance: a regression tree approach based on strongly typed genetic programming. In Applications of evolutionary computing (pp. 676–687). Berlin: Springer. CrossRefGoogle Scholar
  46. Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review, E(51), 4282–4286. Google Scholar
  47. Helbing, D., & Molnar, P. (1997). Self-organization phenomena in pedestrian crowds. In F. Schweitzer (Ed.), Self-organization of complex structures: from individual to collective dynamics (pp. 569–577). London: Gordon and Breach. Google Scholar
  48. Hoenig, F. (2005). Defining computational aesthetics. In L. Neumann, M. Sbert & B. Gooch (Eds.), Computational aesthetics in graphics, visualization and imaging, Girona, Spain. Google Scholar
  49. Holger, H. (1997). Why a special issue on the golden section hypothesis? An introduction. Empirical Studies of the Arts, 15. Google Scholar
  50. Hönn, M., & Göz, G. (2007). The ideal of facial beauty: a review. Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopdie, 68(1), 6–16. CrossRefGoogle Scholar
  51. Hornby, G. S., & Pollack, J. B. (2001). The advantages of generative grammatical encodings for physical design. In Proceedings of the 2001 congress on evolutionary computation (Vol. 601, pp. 600–607). Google Scholar
  52. Jaskowski, W. (2007). Learning and recognition of hand-drawn shapes using generative genetic programming. In Lecture notes in computer science (Vol. 4448, pp. 281–290). Google Scholar
  53. Khalifa, Y., & Foster, R. (2006). A two-stage autonomous evolutionary music composer. In Lecture notes in computer science: Vol. 3907. Evo workshops (pp. 717–721). Google Scholar
  54. Kolmogorov, A. N. (1965). Three approaches to the quantitative definition of information. Problems in Information Transmission, 1, 1–7. MathSciNetGoogle Scholar
  55. Komar, V., Melamid, A., & Wypijewski, J. (1997). Painting by numbers: Komar and Melamid’s scientific guide to art (1st ed.). New York: Farrar Straus Giroux. Google Scholar
  56. Konečni, V. J. (1978). Daniel E. Berlyne: 1924–1976. The American Journal of Psychology, 91(1), 133–137. Google Scholar
  57. Koob, A. (2009). The root of thought: what do glial cells do? Accessed 11/29/09.
  58. Koza, J. R., Bennett, F. H. I., Andre, D., & Keane, M. A. (2002). Genetic programming: biologically inspired computation that exhibits creativity in producing human-competitive results. In P. Bentley & D. Corne (Eds.), Creative evolutionary systems (pp. 275–298). San Francisco/San Diego: Morgan Kaufmann/Academic Press. CrossRefGoogle Scholar
  59. Kozbelt, A. (2006). Dynamic evaluation of Matisse’s 1935 large reclining nude. Empirical Studies of the Arts, 24(2), 119–137. CrossRefGoogle Scholar
  60. Law, E., & Phon-Amnuaisuk, S. (2008). Towards music fitness evaluation with the hierarchical SOM. In Applications of evolutionary computing (pp. 443–452). Berlin: Springer. CrossRefGoogle Scholar
  61. Li, Y.-F., & Zhang, X.-R. (2004). Quantitative and rational research for the sense quantum—research of the order factors for color harmony aesthetic. Journal of Shanghai University (English Edition), 8(2), 203–207. CrossRefGoogle Scholar
  62. Livio, M. (2003). The golden ratio: the story of phi, the world’s most astonishing number (1st ed.). New York: Broadway Books. Google Scholar
  63. Machado, P. (1998) Computing aesthetics. In Lecture notes in artificial intelligence: Vol. 1515. Google Scholar
  64. Machado, P., & Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence, 16(2), 101–118. zbMATHCrossRefGoogle Scholar
  65. Machado, P., & Cardoso, A. (2003). NEvAr system overview. Generative design lab, Milan Polytechnic. Google Scholar
  66. Machado, P., Romero, J., Cardoso, A., & Santos, A. (2005). Partially interactive evolutionary artists. New Generation Computing, 23(2), 143–155. CrossRefGoogle Scholar
  67. Machado, P., Romero, J., & Manaris, B. (2008). Experiments in computational aesthetics—an iterative approach to stylistic change in evolutionary art. In J. Romero & P. Machado (Eds.), The art of artificial evolution: a handbook on evolutionary art and music (pp. 311–332). Berlin: Springer. Google Scholar
  68. Machado, P., Romero, J., Santos, A., Cardoso, A., & Pazos, A. (2007). On the development of evolutionary artificial artists. Computers and Graphics, 31(6), 818–826. CrossRefGoogle Scholar
  69. Machado, P., Romero, J., Santos, M. L., Cardoso, A., & Manaris, B. (2004). Adaptive critics for evolutionary artists. In Lecture notes in computer science. Applications of evolutionary computing (pp. 437–446). Berlin: Springer. CrossRefGoogle Scholar
  70. Machwe, A. T. (2007). Towards an interactive, generative design system: integrating a ‘build and evolve’ approach with machine learning for complex freeform design. In Lecture notes in computer science (Vol. 4448, pp. 449–458). Google Scholar
  71. Magnus, C. (2006). Evolutionary musique concrete. In F. Rothlauf & J. Branke (Eds.), Applications of evolutionary computing, EvoWorkshops 2006 (pp. 688–695). Berlin: Springer. Google Scholar
  72. Manaris, B., Machado, P., McCauley, C., Romero, J., & Krehbiel, D. (2005). Developing fitness functions for pleasant music: Zipf’s law and interactive evolution systems. In Lecture notes in computer science: Vol. 3449. Evo workshops (pp. 498–507). Google Scholar
  73. Manaris, B., Vaughan, D., Wagner, C., Romero, J., & Davis, R. B. (2003). Evolutionary music and the Zipf-Mandelbrot law: developing fitness functions for pleasant music. Applications of Evolutionary Computing, 2611, 522–534. CrossRefGoogle Scholar
  74. Martindale, C. (1981). Cognition and consciousness. The Dorsey series in psychology. Homewood: Dorsey Press. Google Scholar
  75. Martindale, C. (1984). The pleasures of thought: a theory of cognitive hedonics. Journal of Mind and Behavior, 5(1), 49–80. MathSciNetGoogle Scholar
  76. Martindale, C. (1988a). Cognition, psychobiology, and aesthetics. In F. H. Farley & R. W. Neperud (Eds.), The foundations of aesthetics, art, and art education (pp. 7–42). New York: Praeger Publishers. Google Scholar
  77. Martindale, C. (1988b). Relationship of preference judgements to typicality, novelty, and mere exposure. Empirical Studies of the Arts, 6(1), 79–96. CrossRefGoogle Scholar
  78. Martindale, C. (1991). Cognitive psychology: a neural-network approach. Pacific Grove: Brooks/Cole Publishing Company. Google Scholar
  79. Martindale, C. (2007). A neural-network theory of beauty. In C. Martindale, P. Locher & V. Petrov (Eds.), Evolutionary and neurocognitive approaches to aesthetics, creativity, and the arts (pp. 181–194). Amityville: Baywood. Google Scholar
  80. Martindale, C., Moore, K., & Anderson, K. (2005). The effect of extraneous stimulation on aesthetic preference. Empirical Studies of the Arts, 23(2), 83–91. CrossRefGoogle Scholar
  81. Martindale, C., Moore, K., & Borkum, J. (1990). Aesthetic preference: anomalous findings for Berlyne’s psychobiological theory. The American Journal of Psychology, 103(1), 53–80. CrossRefGoogle Scholar
  82. Maxwell, J. B., Pasquier, P., & Eigenfeldt, A. (2009). Hierarchical sequential memory for music: a cognitive model. In International society for music information retrieval. Google Scholar
  83. McCormack, J. (2005) Open problems in evolutionary music and art. In Lecture notes in computer science: Vol. 3449. Evo workshops (pp. 428–436). Google Scholar
  84. McCormack, J. (2008). Facing the future: evolutionary possibilities for human-machine creativity. In J. Romero & P. Machado (Eds.), The art of artificial evolution: a handbook on evolutionary art and music (pp. 417–451). Berlin: Springer. Google Scholar
  85. McCormack, J., & Bown, O. (2009) Life’s what you make: Niche construction and evolutionary art. In Lecture notes in computer science: Vol. 5484. Evo workshops (pp. 528–537). Google Scholar
  86. McDermott, J., Griffith, N. J. L., & O’Neill, M. (2005). Toward user-directed evolution of sound synthesis parameters. In Lecture notes in computer science: Vol. 3449. Evo workshops (pp. 517–526). Google Scholar
  87. Minsky, M. L., & Papert, S. (1969). Perceptrons; an introduction to computational geometry. Cambridge: MIT Press. zbMATHGoogle Scholar
  88. Mitchell, T. J., & Pipe, A. G. (2005). Convergence synthesis of dynamic frequency modulation tones using an evolution strategy. In Applications on evolutionary computing (pp. 533–538). Berlin: Springer. Google Scholar
  89. Moles, A. A. (1966). Information theory and esthetic perception. Urbana: University of Illinois Press. Google Scholar
  90. Monmarché, N., Aupetit, S., Bordeau, V., Slimane, M., & Venturini, G. (2003). Interactive evolution of ant paintings. In B. McKay et al. (Eds.), Congress on evolutionary computation (Vol. 2, pp. 1376–1383). New York: IEEE Press. Google Scholar
  91. Mori, T., Endou, Y., & Nakayama, A. (1996). Fractal analysis and aesthetic evaluation of geometrically overlapping patterns. Textile Research Journal, 66(9), 581–586. CrossRefGoogle Scholar
  92. Neufeld, C., Ross, B. J., & Ralph, W. (2008). The evolution of artistic filters. In J. Romero & P. Machado (Eds.), The art of artificial evolution: a handbook on evolutionary art and music (pp. 335–356). Berlin: Springer. Google Scholar
  93. North, A. C., & Hargreaves, D. J. (2000). Collative variables versus prototypically. Empirical Studies of the Arts, 18(1), 13–17. CrossRefGoogle Scholar
  94. Numenta (2008). Advanced nupic programming. Accessed 16/04/10.
  95. Oelmann, H., & Laeng, B. (2009). The emotional meaning of harmonic intervals. Cognitive Processing, 10(2), 113–131. CrossRefGoogle Scholar
  96. Parker, S., Bascom, J., Rabinovitz, B., & Zellner, D. (2008). Positive and negative hedonic contrast with musical stimuli. Psychology of Aesthetics, Creativity, and the Arts, 2(3), 171–174. CrossRefGoogle Scholar
  97. Peitgen, H.-O., Jürgens, H., & Saupe, D. (1992). Chaos and fractals: new frontiers of science. New York: Springer. Google Scholar
  98. Phon-Amnuaisuk, S. (2007). Evolving music generation with SOM-fitness genetic programming. In Lecture notes in computer science (Vol. 4448, pp. 557–566). Google Scholar
  99. Pinker, S. (1994). The language instinct (1st ed.). New York: Morrow. Google Scholar
  100. Poon, J., & Maher, M. L. (1997). Co-evolution and emergence in design. Artificial Intelligence in Engineering, 11(3), 319–327. CrossRefGoogle Scholar
  101. Reddin, J., McDermott, J., & O’Neill, M. (2009). Elevated pitch: automated grammatical evolution of short compositions. In Lecture notes in computer science: Vol. 5484. EvoWorkshops 2009 (pp. 579–584). Google Scholar
  102. Resnick, M. (1994). Complex adaptive systems. Turtles, termites, and traffic jams: explorations in massively parallel microworlds. Cambridge: MIT Press. Google Scholar
  103. Reynolds, C. (1987). Flocks, herds, and schools: a distributed behavioural model. Computer Graphics, 21(4), 25–34. CrossRefGoogle Scholar
  104. Romero, J., Machado, P., & Santos, M. L. (2003). Artificial music critics. Generative Design Lab, Milan Polytechnic. Google Scholar
  105. Rosenblatt, F. (1962). Principles of neurodynamics; perceptrons and the theory of brain mechanisms. Washington: Spartan Books. zbMATHGoogle Scholar
  106. Ross, A. (1995). Poll stars. ArtForum, 33(5), 72–77. Google Scholar
  107. Ross, B. J., & Zhu, H. (2004). Procedural texture evolution using multi-objective optimization. New Generation Computing, 22(3), 271–293. zbMATHCrossRefGoogle Scholar
  108. Saunders, R. (2002). Curious design agents and artificial creativity. PhD thesis, University of Sydney. Google Scholar
  109. Saunders, R., & Gero, J. S. (2004). Curious agents and situated design evaluations. AI Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing, 18(2), 153–161. Google Scholar
  110. Scha, R., & Bod, R. (1993). Computationele esthetica. Informatie en Informatiebeleid, 11(1), 54–63. Google Scholar
  111. Schimmel, K., & Forster, J. (2008). How temporal distance changes novices’ attitudes towards unconventional arts. Psychology of Aesthetics, Creativity, and the Arts, 2(1), 53–60. CrossRefGoogle Scholar
  112. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. MathSciNetzbMATHGoogle Scholar
  113. Sims, K. (1991). Artificial evolution for computer-graphics. Siggraph ’91 Proceedings 25, 319–328. CrossRefGoogle Scholar
  114. Sims, K. (1994). Evolving virtual creatures. Siggraph ’94 Proceedings, 28, 15–22. CrossRefGoogle Scholar
  115. Sims, K. (1997). Galapagos interactive exhibit. Accessed 11/16/2010.
  116. Skov, M., & Vartanian, O. (2009a). Introduction—what is neuroaesthetics? In M. Skov & O. Vartanian (Eds.), Neuroaesthetics—foundations and frontiers in aesthetics (pp. iv, 302 p.). Amityville: Baywood. Google Scholar
  117. Skov, M., & Vartanian, O. (2009b). Neuroaesthetics, foundations and frontiers in aesthetics, Amityville: Baywood. Google Scholar
  118. Solomonoff, R. J. (1964). A formal theory of inductive inference, part I and part II. Information and Control, 7, 1–22. 224–254. MathSciNetzbMATHCrossRefGoogle Scholar
  119. Standage, T. (2002). The mechanical turk: the true story of the chess-playing machine that fooled the world. London: Allen Lane. Google Scholar
  120. Staudek, T. (1999). On Birkhoff’s aesthetic measure of vases (Vol. 2009). Faculty of Informatics, Masaryk University. Google Scholar
  121. Stewart, M. (2008). Launching the imagination: a comprehensive guide to basic design (3rd ed.). Boston: McGraw-Hill Higher Education. Google Scholar
  122. Sullivan, L. H. (1896). The tall office building artistically considered. Lippincott’s Magazine, 57, 403–409. Google Scholar
  123. Takagi, H. (2001). Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proceedings of the IEEE, 89(9), 1275–1296. CrossRefGoogle Scholar
  124. Taylor, R. P. (2006). Chaos, fractals, nature: a new look at Jackson Pollock. Eugene: Fractals Research. Google Scholar
  125. Todd, P. M. (1989). A connectionist approach to algorithmic composition. Computer Music Journal, 13(4), 27–43. CrossRefGoogle Scholar
  126. Todd, P., & Werner, G. (1998). Frankensteinian methods for evolutionary music composition. In N. Griffith & P. Todd (Eds.), Musical networks: parallel distributed perception and performance. Cambridge: MIT Press/Bradford Books. Google Scholar
  127. Todd, S., & Latham, W. (1992). Evolutionary art and computers. London: Academic Press. zbMATHGoogle Scholar
  128. Tsai, H.-C., Hung, C.-Y., & Hung, F.-K. (2007). Automatic product color design using genetic searching. In Computer-aided architectural design futures (CAADFutures) 2007 (pp. 513–524). Berlin: Springer. CrossRefGoogle Scholar
  129. Tufte, G., & Gangvik, E. (2008). Transformer #13: exploration and adaptation of evolution expressed in a dynamic sculpture. In Applications of evolutionary computing (pp. 509–514). Berlin: Springer. CrossRefGoogle Scholar
  130. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460. MathSciNetCrossRefGoogle Scholar
  131. Turing, A. M. (1952). The chemical basis of morphogenesis. Philosophical transactions—Royal Society. Biological Sciences, 237(641), 37–72. CrossRefGoogle Scholar
  132. Urbano, P. (2006) Consensual paintings. In Lecture notes in computer science: Vol. 3907. Evo workshops (pp. 622–632). Google Scholar
  133. Verstegen, I. (2007). Rudolf Arnheim’s contribution to gestalt psychology. Psychology of Aesthetics, Creativity, and the Arts, 1(1), 8–15. CrossRefGoogle Scholar
  134. Von Neumann, J., & Burks, A. W. (1966). Theory of self-reproducing automata. Urbana: University of Illinois Press. Google Scholar
  135. Voss, R. F., & Clarke, J. (1975). 1/F-noise in music and speech. Nature, 258(5533), 317–318. CrossRefGoogle Scholar
  136. Watanabe, S. (2009). Pigeons can discriminate “good” and “bad” paintings by children. Animal Cognition, 13(1). Google Scholar
  137. Weinberg, G., Godfrey, M., Rae, A., & Rhoads, J. (2009). A real-time genetic algorithm in human-robot musical improvisation. In Computer music modeling and retrieval. Sense of sounds (pp. 351–359). Berlin: Springer. Google Scholar
  138. Wertheimer, M. (2007). Rudolf Arnheim: an elegant artistic gestalt. Psychology of Aesthetics, Creativity, and the Arts, 1(1), 6–7. CrossRefGoogle Scholar
  139. Whitelaw, M. (2003). Morphogenetics: generative processes in the work of driessens and verstappen. Digital Creativity, 14(1), 43–53. CrossRefGoogle Scholar
  140. Whitfield, T. W. A. (2000). Beyond prototypicality: toward a categorical-motivation model of aesthetics. Empirical Studies of the Arts, 18(1), 1–11. CrossRefGoogle Scholar
  141. Wilson, D. J. (1939). An experimental investigation of Birkhoff’s aesthetic measure. The Journal of Abnormal and Social Psychology, 34(3), 390–394. CrossRefGoogle Scholar
  142. Wu, Y.-F., & Chien, S.-F. (2005). Enemy character design in computer games using generative approach. Generative Design Lab, Milan Polytechnic. Google Scholar
  143. Yao, X., & Higuchi, T. (1997). Promises and challenges of evolvable hardware. In T. Higuchi (Ed.), Evolvable systems: from biology to hardware (Vol. 1259, pp. 55–78). Berlin: Springer. CrossRefGoogle Scholar
  144. Yee-King, M. (2007). An automated music improviser using a genetic algorithm driven synthesis engine. In M. Giacobini (Ed.), Proceedings of the 2007 EvoWorkshops (pp. 567–576). Berlin: Springer. Google Scholar
  145. Yuan, J. (2008). Large population size IGAs with individuals’ fitness not assigned by user. In Lecture notes in computer science (Vol. 5227, pp. 267–274). Google Scholar
  146. Zipf, G. K. (1949). Human behavior and the principle of least effort: an introduction to human ecology. Cambridge: Addison-Wesley. Google Scholar

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Authors and Affiliations

  1. 1.Department of VisualizationTexas A&M UniversityTexasUSA

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