We propose a method of evolving designs based on the user’s personal preferences. The method works through an interaction between the user and a computer system. The method’s objective is to help the customer to set design parameters via a simple evaluation of displayed samples. An important feature is that the design attributes to which the user pays more attention (favored features) are estimated using reducts in rough set theory and reflected when refining the design. New design candidates are generated by the user’s evaluation of design samples generated at random. The values of attributes estimated as favored features are fixed in the refined samples, while other attributes are generated at random. This interaction continues until the samples converge to a satisfactory design. In this manner, the design process efficiently evaluates personal and subjective preferences. The method is applied to design a 3D cylinder model such as a cup or vase. The method is then compared with an Interactive GA.

1.
Gulliksen
,
J.
,
Lantz
,
A.
, and
Boivie
,
I.
,
1999
, “
User Centered Design in Practice: Problems and Possibilities
,”
SIGCHI Bull.
,
31
(
2
), pp.
25
35
.
2.
Pawlak
,
Z.
,
1984
, “
Rough Classification
,”
Int. J. Man-Mach. Stud.
,
20
, pp.
469
483
.
3.
Nagamichi
,
M.
,
1995
, “
Kansei Engineering: A New Ergonomic Consumer-Oriented Technology for Product Development
,”
Int. J. Ind. Ergonom.
,
15
(
1
), pp.
3
11
.
4.
Osgood, C. G., Suci, G. J., and Tannenbaum, P., 1957, The Measurement of Meaning, University of Illinois, pp. 71–75.
5.
Breemen van, E. J. J., Sudijono, S., and Horvath, I., 1999, “A Contribution to Finding the Relationship Between Shape Characteristics and Aesthetic Appreciation of Selected Products,” in Proceedings of the International Conference on Engineering Design ICED 99, August 24–26, 1999, Munich, Germany, pp. 1765–1768.
6.
Cappadona, F., Goussard, J., and Sutra, L., 2003, “FIORES-II: A Quantitative Approach of Aesthetic Notions,” in Collaborative Design-MICAD Conference 2003, April 1–3, Paris.
7.
Yamada, R., Nagai, K., Onishi, H., and Kishimoto, K., 1999, “A Method of KANSEI Acquisition to 3D Shape Design and Its Application,” Proc. of the 1999 IEEE International Workshop on Robot and Human Interaction, Pisa, Italy, pp. 369–374.
8.
Takagi, H., 1998, “Interactive Evolutionary Computing—Cooperation of Computational Intelligence and Human Kansei,” in Proc. of 5th International Conference on Soft Computing and Information/Intelligent System.
9.
Smyth, S. N., and Wallace D. R., 2000, “Toward the Synthesis of Aesthetic Product Form,” Proc. of ASME DETEC’00 Computers and Information in Engineering Conference.
10.
Nakanishi, Y., 1996, “Applying Evolutionary System to Design Aid System,” in ALIFE V, Poster Presentation, PP-25, pp. 147–157.
11.
Graf, J., and Banzhaf, W., 1995, “Interactive Evolution of Images,” 4th Annual Conference on Evolutionary Programming, San Diego, CA., pp. 53–65.
12.
Pawlak
,
Z.
,
1982
, “
Rough Sets
,”
Int. J. Compute Inf. Sci.
,
11
, pp.
341
356
.
13.
Pawlak
,
Z.
,
Grzymaa-Busse
,
J. W.
,
Sowinski
,
R.
, and
Ziarko
,
W.
,
1995
, “
Rough Sets
,”
Commun. ACM
,
38
, pp.
89
95
.
14.
Mori
,
N.
, and
Takanashi
,
R.
,
2000
, “
Knowledge Acquisition From the Data Consisting of Categories Added With Degrees of Conformity—Proposal of Extended Reduct Calculation in Rough Set Theory
,”
Kansei Eng. Int.
,
1
(
4
), pp.
19
24
.
15.
Dubois, D., and Prade, H., 1992, “Putting Rough Sets and Fuzzy Sets Together,” in R. Slowinski (Ed.), Intelligent Decision Support—Handbook of Applications and Advances of Rough Sets Theory, Kluwer Academic, Dordrecht, pp. 203–232.
16.
Pawlak, Z., and Skowron, A., 1994, “Rough Membership Function,” in R. R. Yager, M. Fedrizzi, and J. Kacprzyk (eds.), Advances in the Dempster Shafer Theory of Evidence, Wiley, New York, pp. 251–271.
17.
Yanagisawa
,
H.
, and
Fukuda
,
S.
,
2004
, “
Development of Industrial Design Support System Considering Customers’ Subjective Evaluation
,”
JSME Int. J., Ser. C
,
47
(
2
), pp.
762
769
.
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