The Development of a Tool for the Preference Assessment of the Visual Aesthetics of an Object Using Interactive Genetic Algorithms

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche

Reference

Jarod Kelly, Panos Papalambros, Gregory Wakefield: The Development of a Tool for the Preference Assessment of the Visual Aesthetics of an Object Using Interactive Genetic Algorithms. In: Generative Art 2006.

DOI

Abstract

Interactive evolutionary algorithms (IEAs) have been proposed as creativity aids to designers with regard to visual aesthetics. Algorithms have been developed to expose the designer to new designs and thus spark imagination and improve creativity. The present paper focuses on utilizing interactive genetic algorithms (IGAs), a subset of IEAs, to understand the visual aesthetic preferences of the user. A design concept is input to the IGA program, which utilizes information gathered from the user to create new designs. The program collects preferences from the user in a simulated “marketplace” setting. These preferences inform the evolution of the object’s design over several generations until a final “ideal design" is reached. This "ideal design" is considered to be the most preferred visual aesthetics for that user within the specified design space. Monte Carlo simulations indicate that the IGA can locate such ideal designs with high probability. Human studies suggest that users can express their visual aesthetic preferences for a design with the IGA. This is important because few scientific tools exist for acquiring such preference information reliably and efficiently. Design decisions regarding visual aesthetics of a product often interact with its technical attributes. The proposed preference assessment tool aims to facilitate such interactions in a more analytical manner than is currently available.

Extended Abstract

Bibtex

Used References

[1] Holland, J.H. 1975. Adaptation in Natural and Artificial System. Ann Arbor, Michigan, USA: The University of Michigan Press.

[2] Green, P. E., & Srinivasan, V. 1990. “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice”. Journal of Marketing, 54(4), 3-19.

[3] Chang, J. J., & Carroll, J. D. 1969. How to use MDPREF, a computer program for multidimensional analysis of preference data. Bell Telephone Laboratories, Murray Hill, MJ.

[4] Coxon, A. P. M., Jackson, J. E., Davies, P. M., Smith, H. V., Sachs, L., & Schmee, J. 1985. “The Users Guide to Multidimensional Scaling”. TECHNOMETRICS, 27(1).

[5] Young, N. D., Drake, M., Lopetcharat, K., & McDaniel, M. R. 2004. “Preference mapping of Cheddar cheese with varying maturity levels”. Journal of dairy science, 87(1), 11-19.

[6] Chang, J. J., & Carroll, J. D. 1972. How to use PREFMAP and PREFMAP-2: Programs which relate preference data to multidimensional scaling solutions. Unpublished manuscript, Bell Telephone Labs, Murray Hill, NJ.

[7] Petiot, J. F., & Chablat, D. 2003 (November 5-7, 2003). “Subjective Evaluation of Forms in an Immersive Environment”. In: Proceedings of Virtual Concept - 2003.

[8] Cho, S.B. 2002. Towards creative evolutionary systems with interactive genetic algorithm. Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies, 16(2), 129{38.

[9] Nishino, H., Takagi, H., Cho, S.B., & Utsumiya, K. 2001. “A 3D modelling system for creative design”. Pages 479-86 of: Proceedings of 15th International Conference on Information Networking, 31 Jan.-2 Feb. 2001.

[10] Durant, E.A., Wakefield, G.H., Van Tasell, D.J., & Rickert, M.E. 2004. “Efficient Perceptual Tuning of Hearing Aids With Genetic Algorithms”. IEEE Transactions on Speech and Audio Processing, 12(2), 144-155.

[11] Kamalian, R., Zhang, Y., Takagi, H., & Agogino, A. M. 2005. “Reduced Human Fatigue Interactive Evolutionary Computation for Micromachine Design”. Proceedings of 2005 International Conference on Machine Learning and Cybernetics.

[12] Dawkins, R. 1986. The blind watchmaker. Harlow : Longman Scientific and Technical.

[13] Smyth, S. N., & Wallace, D. R. 2000. “Towards the synthesis of aesthetic product form”. Proc.DETC2000/DTM-14554, ASME, New York.

[14] Whitley, L. D. 2001. “An overview of evolutionary algorithms: practical issues and common pitfalls”. Information Software Technology, 43(14), 817{831.


Links

Full Text

http://www.generativeart.com/on/cic/papersGA2006/28.htm

intern file

Sonstige Links