Computational aesthetic evaluation: Past and future

Computers and Creativity

Volumn 9783642317279, Issue , 2012, Pages 255-293

  Galanter, Philip ^a  

^a TEXAS A AND M UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; COMPUTATIONAL COMPLEXITY; COMPUTER HARDWARE; NEURAL NETWORKS; PARALLEL PROCESSING SYSTEMS;

AGENT SWARM; CO-EVOLUTION; COMPUTATIONAL AESTHETICS; DESIGN PRINCIPLES; DIGITAL SYSTEM; EVOLUTIONARY SYSTEM; GEOMETRIC THEORY; HUMAN CREATIVITY; MACHINE CREATIVITY; SWARM BEHAVIOR;

COMPUTER ARCHITECTURE;

EID: 84887486133     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-642-31727-9_10     Document Type: Chapter

References (146)

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.
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.
3. Alsing, R. (2008). Genetic programming: evolution of Mona Lisa. http://rogeralsing.com/2008/12/07/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.
5. Atiyeh, B., & Hayek, S. (2008). Numeric expression of aesthetics and beauty. Aesthetic Plastic Surgery, 32(2), 209-216.
6. Axelsson, O. (2007). Individual differences in preferences to photographs. Psychology of Aesthetics, Creativity, and the Arts, 1(2), 61-72.
7. Baluja, S., Pomerleau, D., & Jochem, T. (1994). Towards automated artificial evolution for computer-generated images. Connection Science, 6(1), 325-354.
8. Bense, M. (1965). Aesthetica; Einfhrung in die neue Aesthetik. Baden-Baden: Agis-Verlag.
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.
10. Berlyne, D. E. (1960). Conflict, arousal, and curiosity. New York: McGraw-Hill.
11. Berlyne, D. E. (1971). Aesthetics and psychobiology. New York: Appleton-Century-Crofts.
12. Birkhoff, G. D. (1933). Aesthetic measure. Cambridge: Harvard University Press.
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.
14. Carroll, N. (1999). Philosophy of art: a contemporary introduction, Routledge contemporary introductions to philosophy. London: Routledge.
15. Casti, J. L. (1994). Complexification: explaining a paradoxical world through the science of surprise (1st ed.). New York: HarperCollins.
16. Chaitin, G. J. (1966). On the length of programs for computing finite binary sequences. Journal of the ACM, 13(4), 547-569.
17. Ciesielski, V. (2007). Evolution of animated photomosaics. In Lecture notes in computer science (vol. 4448, pp. 498-507).
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.
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.
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).
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.
22. Davis, T., & Rebelo, P. (2007). Environments for sonic ecologies. In Applications of evolutionary computing (pp. 508-516). Berlin: Springer.
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.
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.
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).
26. Dutton, D. (2009). The art instinct: beauty, pleasure, and human evolution (1st U.S. ed.). New York: Bloomsbury Press.
27. Elzenga, R. N., & Pontecorvo, M. S. (1999). Arties: meta-design as evolving colonies of artistic agents. Generative Design Lab.
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.
29. Feldman, D. P., & Crutchfield, J. (1998). A survey of complexity measures. Santa Fe Institute.
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.
31. Fogel, L. J. (1999). Intelligence through simulated evolution: forty years of evolutionary programming. Wiley series on intelligent systems. New York: Wiley.
32. Fornari, J. (2007). Creating soundscapes using evolutionary spatial control. In Lecture notes in computer science (Vol. 4448, pp. 517-526).
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.
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.
35. Gedeon, T. (2008). Neural network for modeling esthetic selection. In Lecture notes in computer science (Vol. 4985(2), pp. 666-674).
36. Gell-Mann, M. (1995). What is complexity? Complexity, 1(1), 16-19.
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.
38. Greenfeld, G. R. (2003). Evolving aesthetic images using multiobjective optimization. In CEC: 2003 congress on evolutionary computation (pp. 1903-1909).
39. Greenfield, G. (2005a). Evolutionary methods for ant colony paintings. In Lecture notes in computer science: Vol. 3449. Evo workshops (pp. 478-487).
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.
41. Greenfield, G. (2008a). Evolved diffusion limited aggregation compositions. In Applications of evolutionary computing (pp. 402-411). New York: Springer.
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.
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.
44. Hawkins, J., & Blakeslee, S. (2004). On intelligence (1st ed.). New York: Times Books.
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.
46. Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review, E(51), 4282-4286.
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.
48. Hoenig, F. (2005). Defining computational aesthetics. In L. Neumann, M. Sbert & B. Gooch (Eds.), Computational aesthetics in graphics, visualization and imaging, Girona, Spain.
49. Holger, H. (1997). Why a special issue on the golden section hypothesis? An introduction. Empirical Studies of the Arts, 15.
50. Hönn, M., & Göz, G. (2007). The ideal of facial beauty: a review. Journal of Orofacial Orthopedics/Fortschritte der Kieferorthopdie, 68(1), 6-16.
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).
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).
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).
54. Kolmogorov, A. N. (1965). Three approaches to the quantitative definition of information. Problems in Information Transmission, 1, 1-7.
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.
56. Konečni, V. J. (1978). Daniel E. Berlyne: 1924-1976. The American Journal of Psychology, 91(1), 133-137.
57. Koob, A. (2009). The root of thought: what do glial cells do? http://www.scientificamerican.com/article.cfm?id=the-root-of-thought-what. 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.
59. Kozbelt, A. (2006). Dynamic evaluation of Matisse's 1935 large reclining nude. Empirical Studies of the Arts, 24(2), 119-137.
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.
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.
62. Livio, M. (2003). The golden ratio: the story of phi, the world's most astonishing number (1st ed.). New York: Broadway Books.
63. Machado, P. (1998) Computing aesthetics. In Lecture notes in artificial intelligence: Vol. 1515.
64. Machado, P., & Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence, 16(2), 101-118.
65. Machado, P., & Cardoso, A. (2003). NEvAr system overview. Generative design lab, Milan Polytechnic.
66. Machado, P., Romero, J., Cardoso, A., & Santos, A. (2005). Partially interactive evolutionary artists. New Generation Computing, 23(2), 143-155.
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.
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.
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.
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).
71. Magnus, C. (2006). Evolutionary musique concrete. In F. Rothlauf & J. Branke (Eds.), Applications of evolutionary computing, EvoWorkshops 2006 (pp. 688-695). Berlin: Springer.
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).
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.
74. Martindale, C. (1981). Cognition and consciousness. The Dorsey series in psychology. Homewood: Dorsey Press.
75. Martindale, C. (1984). The pleasures of thought: a theory of cognitive hedonics. Journal of Mind and Behavior, 5(1), 49-80.
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.
77. Martindale, C. (1988b). Relationship of preference judgements to typicality, novelty, and mere exposure. Empirical Studies of the Arts, 6(1), 79-96.
78. Martindale, C. (1991). Cognitive psychology: a neural-network approach. Pacific Grove: Brooks/Cole Publishing Company.
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.
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.
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.
82. Maxwell, J. B., Pasquier, P., & Eigenfeldt, A. (2009). Hierarchical sequential memory for music: a cognitive model. In International society for music information retrieval.
83. McCormack, J. (2005) Open problems in evolutionary music and art. In Lecture notes in computer science: Vol. 3449. Evo workshops (pp. 428-436).
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.
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).
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).
87. Minsky, M. L., & Papert, S. (1969). Perceptrons; an introduction to computational geometry. Cambridge: MIT Press.
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.
89. Moles, A. A. (1966). Information theory and esthetic perception. Urbana: University of Illinois Press.
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.
91. Mori, T., Endou, Y., & Nakayama, A. (1996). Fractal analysis and aesthetic evaluation of geometrically overlapping patterns. Textile Research Journal, 66(9), 581-586.
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.
93. North, A. C., & Hargreaves, D. J. (2000). Collative variables versus prototypically. Empirical Studies of the Arts, 18(1), 13-17.
94. Numenta (2008). Advanced nupic programming. http://www.numenta.com/for-developers/software/pdf/nupic-prog-guide.pdf. Accessed 16/04/10.
95. Oelmann, H., & Laeng, B. (2009). The emotional meaning of harmonic intervals. Cognitive Processing, 10(2), 113-131.
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.
97. Peitgen, H.-O., Jürgens, H., & Saupe, D. (1992). Chaos and fractals: new frontiers of science. New York: Springer.
98. Phon-Amnuaisuk, S. (2007). Evolving music generation with SOM-fitness genetic programming. In Lecture notes in computer science (Vol. 4448, pp. 557-566).
99. Pinker, S. (1994). The language instinct (1st ed.). New York: Morrow.
100. Poon, J., & Maher, M. L. (1997). Co-evolution and emergence in design. Artificial Intelligence in Engineering, 11(3), 319-327.
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).
102. Resnick, M. (1994). Complex adaptive systems. Turtles, termites, and traffic jams: explorations in massively parallel microworlds. Cambridge: MIT Press.
103. Reynolds, C. (1987). Flocks, herds, and schools: a distributed behavioural model. Computer Graphics, 21(4), 25-34.
104. Romero, J., Machado, P., & Santos, M. L. (2003). Artificial music critics. Generative Design Lab, Milan Polytechnic.
105. Rosenblatt, F. (1962). Principles of neurodynamics; perceptrons and the theory of brain mechanisms. Washington: Spartan Books.
106. Ross, A. (1995). Poll stars. ArtForum, 33(5), 72-77.
107. Ross, B. J., & Zhu, H. (2004). Procedural texture evolution using multi-objective optimization. New Generation Computing, 22(3), 271-293.
108. Saunders, R. (2002). Curious design agents and artificial creativity. PhD thesis, University of Sydney.
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.
110. Scha, R., & Bod, R. (1993). Computationele esthetica. Informatie en Informatiebeleid, 11(1), 54-63.
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.
112. Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423.
113. Sims, K. (1991). Artificial evolution for computer-graphics. Siggraph '91 Proceedings 25, 319-328.
114. Sims, K. (1994). Evolving virtual creatures. Siggraph '94 Proceedings, 28, 15-22.
115. Sims, K. (1997). Galapagos interactive exhibit. http://www.karlsims.com/galapagos/index.html. 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.
117. Skov, M., & Vartanian, O. (2009b). Neuroaesthetics, foundations and frontiers in aesthetics, Amityville: Baywood.
118. Solomonoff, R. J. (1964). A formal theory of inductive inference, part I and part II. Information and Control, 7, 1-22. 224-254.
119. Standage, T. (2002). The mechanical turk: the true story of the chess-playing machine that fooled the world. London: Allen Lane.
120. Staudek, T. (1999). On Birkhoff 's aesthetic measure of vases (Vol. 2009). Faculty of Informatics, Masaryk University.
121. Stewart, M. (2008). Launching the imagination: a comprehensive guide to basic design (3rd ed.). Boston: McGraw-Hill Higher Education.
122. Sullivan, L. H. (1896). The tall office building artistically considered. Lippincott's Magazine, 57, 403-409.
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.
124. Taylor, R. P. (2006). Chaos, fractals, nature: a new look at Jackson Pollock. Eugene: Fractals Research.
125. Todd, P. M. (1989). A connectionist approach to algorithmic composition. Computer Music Journal, 13(4), 27-43.
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.
127. Todd, S., & Latham, W. (1992). Evolutionary art and computers. London: Academic Press.
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.
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.
130. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460.
131. Turing, A. M. (1952). The chemical basis of morphogenesis. Philosophical transactions-Royal Society. Biological Sciences, 237(641), 37-72.
132. Urbano, P. (2006) Consensual paintings. In Lecture notes in computer science: Vol. 3907. Evo workshops (pp. 622-632).
133. Verstegen, I. (2007). Rudolf Arnheim's contribution to gestalt psychology. Psychology of Aesthetics, Creativity, and the Arts, 1(1), 8-15.
134. Von Neumann, J., & Burks, A. W. (1966). Theory of self-reproducing automata. Urbana: University of Illinois Press.
135. Voss, R. F., & Clarke, J. (1975). 1/F-noise in music and speech. Nature, 258(5533), 317-318.
136. Watanabe, S. (2009). Pigeons can discriminate "good" and "bad" paintings by children. Animal Cognition, 13(1).
137. Weinberg, G., Godfrey, M., Rae, A., & Rhoads, J. (2009). A real-time genetic algorithm in humanrobot musical improvisation. In Computer music modeling and retrieval. Sense of sounds (pp. 351-359). Berlin: Springer.
138. Wertheimer, M. (2007). Rudolf Arnheim: an elegant artistic gestalt. Psychology of Aesthetics, Creativity, and the Arts, 1(1), 6-7.
139. Whitelaw, M. (2003). Morphogenetics: generative processes in the work of driessens and verstappen. Digital Creativity, 14(1), 43-53.
140. Whitfield, T. W. A. (2000). Beyond prototypicality: toward a categorical-motivation model of aesthetics. Empirical Studies of the Arts, 18(1), 1-11.
141. Wilson, D. J. (1939). An experimental investigation of Birkhoff's aesthetic measure. The Journal of Abnormal and Social Psychology, 34(3), 390-394.
142. Wu, Y.-F., & Chien, S.-F. (2005). Enemy character design in computer games using generative approach. Generative Design Lab, Milan Polytechnic.
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.
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.
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).
146. Zipf, G. K. (1949). Human behavior and the principle of least effort: an introduction to human ecology. Cambridge: Addison-Wesley.