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Aesthetic evolutionary algorithm for fractal-based user-centered jewelry design

Published online by Cambridge University Press:  12 December 2007

Somlak Wannarumon ,
Erik L.J. Bohez  and
Kittinan Annanon
Somlak Wannarumon
Affiliation:
Asian Institute of Technology, Pathumthani, Thailand
Erik L.J. Bohez
Affiliation:
Asian Institute of Technology, Pathumthani, Thailand
Kittinan Annanon
Affiliation:
National Science and Technology Development Agency, Ministry of Science and Technology, Thailand
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Abstract

This paper proposes an aesthetic-driven evolutionary algorithm for user-centered design. The evolutionary algorithm is based on a genetic algorithm (GA). It is developed to work as an art form generator that enhances user's productivity and creativity through reproduction, evaluation, and selection. Users can input their preferences and guide the generating direction to the system. A two-step fitness function is developed to evaluate morphology and aesthetics of the generated art forms. Fractals created by an iterated function system are used for representing art forms in our process. Algorithmic aesthetics are developed based on the aesthetic measure theory, surveys of human preferences, and popular long-lasting symbols. The algorithmic aesthetics is used for evaluating aesthetics of art forms together with subjective nonquantifiable aspects, and placed in the fitness function. The GA basically creates two-dimensional art forms. However, any two-dimensional image can be included through the property of a condensation set of fractals. The proposed GA can increase design productivity by about 80%. Examples of jewelry designs and physical prototypes created by the proposed system are included.

Keywords

Computational AestheticsEvolutionary ArtInteractive Evolutionary DesignIterated Function System FractalJewelry Design
Type
Research Article
Information
AI EDAM , Volume 22 , Issue 1 , February 2008 , pp. 19 - 39
Copyright
Copyright © Cambridge University Press 2008

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References

REFERENCES

Aks, D.J., & Sprott, J.C. (1996). Quantifying aesthetic preference for chaotic patterns. Empirical Studies of the Arts 14(1), 1–16.CrossRefGoogle Scholar
Arnheim, R. (1969). Visual Thinking. Berkeley, CA: University of California Press.Google Scholar
Bäck, T., & Hoffmeister, F. (1991). Extended selection mechanisms in genetic algorithms. In Proc. 4th Int. Conf. Genetic Algorithms (Belew, R., & Booker, L. Eds.), pp. 92–99. San Mateo, CA: Morgan Kaufmann.Google Scholar
Barnsley, M. (1993). Fractals Everywhere, 2nd ed.San Francisco, CA: Academic Press.Google Scholar
Bentley, K., Cox, E.J., & Bentley, P. (2005). Nature's batik: a computer evolution model of diatom valve morphogenesis. Journal of Nanoscience and Nanotechnology 5(1), 1–10.CrossRefGoogle Scholar
Bentley, P. (1999). An introduction to evolutionary design by computers. In Evolutionary Design by Computer Bentley, P., Ed.) pp. 1–73. San Francisco, CA: Morgan Kaufmann.Google Scholar
Bentley, P., & Wakefield, J. (1996). The evolution of solid object designs using genetic algorithms. In Modern Heuristic Search Methods (Rayward-Smith, V., Ed.), pp. 199–215. New York: Wiley.Google Scholar
Berlyne, D. (1974). Studies in the New Experimental Aesthetics: Steps toward an Objective Psychology of Aesthetic Appreciation. New York: Halsted Press.Google Scholar
Birkhoff, G.D. (1933). Aesthetic Measure. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Browne, C., & Wamelen, P. (2006). Spiral packing. Computers & Graphics 30(5), 834–842.CrossRefGoogle Scholar
Cho, S.-B. (2002). Towards creative evolutionary systems in interactive genetic algorithm. Applied Intelligence 16(3), 129–138.CrossRefGoogle Scholar
Cross, N. (1997). Descriptive models of creative design: Application to an example. Design Studies 18(4), 427–440.CrossRefGoogle Scholar
Cross, N. (2001). Can a machine design? Design Issues 17(4), 44–50.CrossRefGoogle Scholar
Dawkins, R. (1986). The Blind Watchmaker. London: Penguin Books.Google Scholar
Eckert, C., Kelly, I., & Stacey, M. (1999). Interactive generative systems for conceptual design: An empirical perspective. AI EDAM 13(4), 303–320.Google Scholar
Elam, K. (2001). Geometry of Design. New York: Princeton Architectural Press.Google Scholar
Fett, B. (2006). An in-depth investigation of the divine ratio. The Montana Mathematics Enthusiast 3(2), 157–175.CrossRefGoogle Scholar
Field, M. (2001). Designer chaos. Computer-Aided Design 33(5), 349–365.CrossRefGoogle Scholar
Galanter, P., & Levy, E.K. (2003). Complexity, the Leonardo gallery. Leonardo 36(4), 259–267.CrossRefGoogle Scholar
Gero, J.S. (1994). Computational models of creative design processes. In Artificial Intelligence and Creativity Dartnall, T., Ed.), pp. 269–281. Dordrecht: Kluwer.CrossRefGoogle Scholar
Gero, J.S. (1996). Creativity, emergence and evolution in design: concepts and framework. Knowledge-Based Systems 9(7), 435–448.CrossRefGoogle Scholar
Gonzalez, R., & Woods, R. (2001). Digital Image Processing, 2nd ed.Englewood Cliffs, NJ: Prentice–Hall.Google Scholar
Goldman, R., Schaefer, S., & Ju, T. (2003). Turtle geometry in computer graphics and computer-aided design. Computer-Aided Design 36(14), 1471–1482.CrossRefGoogle Scholar
Grassberger, P., & Procaccia, I. (1983). Measuring the strangeness of strange attractors. Physica D 9, 189–208.CrossRefGoogle Scholar
Green, C.D. (1995). All that glitters: A review of psychological research on the aesthetics of the golden section. Perception 24, 937–968.CrossRefGoogle ScholarPubMed
Greenfield, G.R. (2005). Computational aesthetics as a tool for creativity. Proc. 5th Int. Conf. Creativity & Cognition (C&C'05), 232–235, London, April 12–15. New York: ACM Press.Google Scholar
Grundler, D., & Rolich, T. (2003). Evolutionary algorithms aided textile design. International Journal of Clothing Science and Technology 15(3–4), 295–304.CrossRefGoogle Scholar
Hoenig, F. (2005). Defining computational aesthetics. Proc. Computational Aesthetics in Graphics, Visualization and Imaging Neumann, L., Sbert, M., Gooch, B., & Purgathofer, W., Eds.), pp. 13–18. New York: ACM SIGGRAPH.Google Scholar
Huntley, H.E. (1970). The Divine Proportion. New York: Dover.Google Scholar
Joye, Y. (2005). Evolutionary and cognitive motivations for fractal art in art and design education. The International Journal of Art & Design Education 24(2), 175–185.CrossRefGoogle Scholar
Koile, K. (2006). Formalizing abstract characteristics of style. AI EDAM 20(3), 267–285.Google Scholar
Machado, P., & Cardoso, A. (1998). Computing aesthetics. Lecture Notes in Computer Science, Vol. 1515 (pp. 219–229). Berlin: Springer–Verlag.Google Scholar
Machado, P., & Cardoso, A. (2002). All the truth about NEvAr. Applied Intelligence 16(2), 101–118.CrossRefGoogle Scholar
Machado, P., Romero, J., Cardoso, A., & Santos, A. (2005). Partially interactive evolutionary artists. New Generation Computing 23(2), 143–155.CrossRefGoogle Scholar
Mahon, E.J., & Battin-Mahon, D. (1984). A note on the golden section. The Psychoanalytic Study of the Child 39, 549–560.CrossRefGoogle ScholarPubMed
Mainzer, K. (2005). Symmetry and Complexity: The Spirit and Beauty of Nonlinear Science. Hackensack, NJ: World Scientific.CrossRefGoogle Scholar
McCormack, J. (2006). New challenges for evolutionary music and art. SIGEVOlution 1(1), 5–11.CrossRefGoogle Scholar
Merkwirth, C., Parlitz, U., & Lauterborn, W. (2000). Fast nearest-neighbor searching for nonlinear signal processing. Physical Review E 62(2), 2089–2096.Google ScholarPubMed
Merkwirth, C., Parlitz, U., Wedekind, I., & Lauterborn, W. (2002). OpenTSTOOL. MatLAB toolbox. Accessed at http://www.phsik3.gwdg.de/tstool/Google Scholar
Michalewicz, Z. (1996). Genetic Algorithms + Data Structure = Evolution Programs, 3rd ed.New York: Springer.CrossRefGoogle Scholar
Mitina, O.V., & Abraham, F.D. (2003). The use of fractals for the study of the psychology of perception: psychophysics and personality factors, a brief report. International Journal of Modern Physics C 14(8), 1047–1060.CrossRefGoogle Scholar
Moles, A. (1966). Information Theory and Esthetic Perception Cohen, J.E., Trans.), p. 217. Urbana, IL: University of Illinois Press.Google Scholar
Montogomery, D.C. (2001). Design and Analysis of Experiments, 5th ed.Hoboken, NJ: Wiley.Google Scholar
Nelson, D. (2004). Fractal Designer [Software]. Accessed at http://archives.math.utk.edu/software/msdos/fractals/fdesign/.htmlGoogle Scholar
Neves, J., Neves, M., & Janssens, K. (1994). Fractal geometry: a new tool for textile design development applications in printing. International Journal of Clothing Science and Technology 6(1), 28–36.CrossRefGoogle Scholar
Niimi, M., Noda, H., & Kawaguch, E. (1997). An image embedding in image by a complexity based region segmentation method. Int. Conf. Image Processing (ICIP'97), 3, pp. 1026–1030.Google Scholar
Nikiel, S. (2006). Integration of iterated function systems and vector graphics for aesthetics. Computers & Graphics 30(2), 277–283.CrossRefGoogle Scholar
Pantoja, M.F., Ruiz, F.G., Bretones, A.R., Martín, R.G., & Romeu, J. (2003). GA design of wire pre-fractal antennas and comparison with other Euclidean geometries. IEEE Antennas and Wireless Propagation Letters 2(15), 238–241.CrossRefGoogle Scholar
Parlitz, U. (1998). Nonlinear time-series analysis. In Nonlinear Modeling: Advanced Black-Box Techniques Suykens, J.A.K., & Vandewalle, J., Eds.), pp. 209–239. Boston: Kluwer.CrossRefGoogle Scholar
Poirson, E., Dépincé, P., & Petiot, J.-F. (2006). User-centered design by genetic algorithms: application to brass musical instrument optimization. Engineering Applications of Artificial Intelligence 20(4), 511–518.CrossRefGoogle Scholar
Remko, S., & Rens, B. (1993). Computational esthetics. Informatie en Informatiebeleid 11(1), 54–63.Google Scholar
Rooke, S. (2002). Evolutionary art. In Creative Evolutionary Systems Bentley, P.L., & Corne, D.W., Eds.), pp. 337–365. San Francisco, CA: Morgan Kaufmann.Google Scholar
Rosen, J. (1998). Symmetry Discovered: Concepts and Applications in Nature and Science, 2nd ed.New York: Dover.Google Scholar
Rosenman, M.A. (1996). The generation of form using an evolutionary approach. In Evolutionary Algorithms in Engineering Applications Dasgupta, D., & Michalewicz, Z., Eds.), pp. 69–85. Southampton: Springer.Google Scholar
Rosenman, M.A., & Gero, J.S. (1993). Creativity in design using a design prototype approach. In Modeling Creativity and Knowledge-based Creative Design Gero, J. & Maher, M.L., Eds.) pp. 119–148. Hillsdale, NJ: Erlbaum.Google Scholar
Rowbottom, A. (1999). Evolutionary art and form. In Evolutionary Design by Computers (Bentley, P., Ed.), pp. 261–278. San Francisco, CA: Morgan Kaufmann.Google Scholar
Rowland, D., & Biocca, F. (2000). Evolutionary co-operative design between human and computer: implementation of “the genetic sculpture park.” Proc. 5th Symp. Virtual Reality Modeling Language (Web3d-Vrml), VRML ‘00, pp. 75–79. New York: ACM Press.CrossRefGoogle Scholar
Rowley, H. (1999). Henry Rowley's genetic art. In Evolutionary Design by Computers Bentley, P., Ed.), p. 267. San Francisco, CA: Morgan Kaufmann.Google Scholar
Sato, S., Sano, M., & Sawada, Y. (1987). Practical methods of measuring the generalized dimension and the largest Lyapunov exponent in high dimensional chaotic systems. Progress of Theoretical Physics 77(1), 1–5.CrossRefGoogle Scholar
Schaefer, S., Levin, D., & Goldman, R. (2005). Subdivision schemes and attractors. Proc. Eurographics Symp. Geometry Processing 2005 (Desbrun, M., & Pottmann, H., Eds.), pp. 171–180. Aire-la-Ville, Switzerland: Eurographics Association.Google Scholar
Sim, K. (1991). Artificial evolution for computer graphics. Computer Graphics 25(4), 319–328.Google Scholar
Soo, S.C., Yu, K.M., & Chiu, W.K. (2006). Modeling and fabrication of artistic products based on IFS fractal representation. Computer-Aided Design 38(7), 755–769.CrossRefGoogle Scholar
Spehar, B., Clifford, C.W.G., Newell, B.R., & Taylor, R.P. (2003). Universal aesthetic of fractals. Computer & Graphics 27(5), 813–820.CrossRefGoogle Scholar
Sprott, J.C. (1994). Automatic generation of iterated function systems. Computer & Graphics 18(3), 417–425.CrossRefGoogle Scholar
Sprott, J.C. (2004). Can a monkey with a computer create art? Nonlinear Dynamics, Psychology, and Life Sciences 8(1), 103–114.Google ScholarPubMed
Staudek, T. (2003). Computer-aided aesthetic evaluation of visual patterns. ISAMA-BRIDGES Conf. Proc., pp. 143–149.Google Scholar
Sudweeks, F., & Simoff, S.J. (1999). Quantifying beauty: an interformation system for evaluating universal aesthetics. Proc. Western Australian Workshop on Information Systems Research (WAWISR) (Gammack, J., Ed.), pp. 262–267. Perth: Murdoch University.Google Scholar
Tacha, A. (2002). Chaos and form: a sculptor's sources in science, artist's article. Leonardo 35(3), 239–245.CrossRefGoogle Scholar
Takagi, H. (1998). Interactive evolutionary computation: system optimization based on human subjective evaluation. Proc. IEEE Int. Conf. Intelligent Engineering Systems (INES 1998), pp. 1–6. Vienna, Austria.Google Scholar
Takagi, H. (2001). Interactive evolutionary computation: fusion of the capacities of EC optimization and human evaluation. Proceedings of IEEE 89(9), 1275–1296.CrossRefGoogle Scholar
Thomas, D. (2003). Aesthetic selection of morphogenetic art forms. Kybernetes: The International Journal of Systems & Cybernetics 32 (1/2), 144–155.CrossRefGoogle Scholar
Todd, S., & Latham, W. (1992). Evolutionary Art and Computers. London: Academic Press.Google Scholar
Todd, S., & Latham, W. (1999). The mutation and growth of art by computers. In Evolutionary Design by Computers (Bentley, P., Ed.), pp. 221–250. San Francisco, CA: Morgan Kaufmann.Google Scholar
Unemi, T. (2003). Simulated breeding—a framework of breeding artifacts on the computer. Kybernetes: The International Journal of Systems & Cybernetics 32(1/2), 203–220.CrossRefGoogle Scholar
Wannarumon, S., & Bohez, E.L.J. (2004). Rapid prototyping and tooling technology in jewelry CAD. Computer-Aided Design & Applications 1(4), 569–575.CrossRefGoogle Scholar
Wannarumon, S., & Bohez, E.L.J. (2006). A new aesthetic evolutionary approach for jewelry design. Computer-Aided Design & Applications 3(4), 385–394.CrossRefGoogle Scholar
Wannarumon, S., Unnanon, K., & Bohez, E.L.J. (2004). Intelligent computer system for jewelry design support. Computer-Aided Design & Applications 1(4), 551–558.CrossRefGoogle Scholar
Weyl, H. (1952). Symmetry. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Wiens, A.L., & Ross, B.J. (2002). Gentropy: evolving 2D textures. Computers & Graphics 26(1), 75–88.CrossRefGoogle Scholar
Witbrock, M., & Neil-Reilly, S. (1999). Evolving genetic art. In Evolutionary Design by Computers (Bentley, P., Ed.), pp. 251–260. San Francisco, CA: Morgan Kaufmann.Google Scholar
Wloch, K., & Bentley, P.J. (2004). Optimising the performace of a formula one car using a genetic algorithm. Proc. Genetic and Evolutionary Computation Conf. (GECCO 2004), Seattle, WA, June 26–30.Google Scholar