Conference article

An Interactive Genetic Algorithm for the Study of Product Semantics

Jean-François Petiot
LUNAM Universitå, Ecole Centrale de Nantes, IRCCyN, Nantes, France

Francisco Cervantes
LUNAM Universitå, Ecole Centrale de Nantes, IRCCyN, Nantes, France

Ludivine Boivin
Technocentre Renault, Guyancourt, France

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Published in: KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13

Linköping Electronic Conference Proceedings 100:89, p. 1067-1080

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Published: 2014-06-11

ISBN: 978-91-7519-276-5

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

This study uses an Interactive Genetic Algorithm (IGA) for eliciting users’ perceptions of products. It aims to understand perceptions; and to determine the product’s attributes that contribute to reinforce - or inhibit - a given semantic dimension. The product proposed to illustrate the study; defined in collaboration with the Renault Company; is a digital instrument panel; integrated in a car dashboard; and the semantic dimension considered is its “sportiness”. After a parameterization of the instrument cluster; an interactive assessment test; based on IGA; is conducted on different products’ pictures with a panel of 30 participants. The IGA test proposes a set of 8 parameterized designs; which are iteratively presented to each participant with their pictures via an interactive interface. From these designs; the user has to select the most representative ones according to the considered semantic dimension (sportiness). From iterative choices of the user; the results show that the design of the product converges toward representative designs of the semantic dimension. We present in the paper the analysis of the results of the IGA test; using Hierarchical Ascendant Classification (HAC); univariate and multivariate analysis. The effect of the design variables and their different modalities on the sportiness of the product is discussed. The agreement between the different participants is also studied; to uncover different typologies of products and identify design trends. The results show that IGA can be an interesting alternative to classical rating tests on experimental designs; used in conjoint analysis or in Kansei Engineering.

Keywords

Interactive genetic algorithms; product semantics; user centered design; multivariate analysis

References

Artacho M.A.; Ballester A.; Alcantara E. (2010). Analysis of the impact of slight changes in product formal attributes on user’s emotions and configuration of an emotional space for successful design. Journal of Engineering Design. Vol. 21; No. 6; 693–705.

Barnes C.; Lillford P.L. (2009). Decision support for the design of affective products; Journal of Engineering Design; Vol. 20; No. 5; 477-492.

Giordano G.; Gettler-Summa M.; Verde R. (2000). Symbolic interpretation in a clustering strategy on multiattribute preference data. Stat. Appl. Ital. J. Appl. Stat.; 4; 473-495.

Goldberg; D.E. (1989). Genetic Algorithms in search; optimization & machine learning; Addison-Wesley Publishing Company.

Gu Z.; Tang M.X.; Frazer J.H. (2006). Capturing aesthetics intention during interactive evolution. Computer Aided Design; 38; 224-237.

Hair J.F.; Tatham R.L.; Anderson R.E.; Black W. (1998). Multivariate Data Analysis (5th Edition); Prentice Hall.

Hoyle; C.; Chen; W.; Ankenman; B. and Wang; N. (2009). Optimal Experimental Design of Human Appraisals for Modeling Consumer Preferences in Engineering Design. ASME J. Mech. Des.; 131(7); 071008.1– 071008.9.

Hsiao S.W. (2002). Concurrent design method for developing a new product. International Journal of Industrial Ergonomics 29; 41-55.

Hsu S.H.; Chuang M.C.; Chang C.C. (2000). A semantic differential study of designers’ and users’ product form perception. International Journal of Industrial Ergonomics 25; 375-391.

Jindo T.; Hirasago K. (1997). Application studies to car interior of Kansei engineering. International journal of industrial ergonomics 19; 105-114.

Kelly J. (2008). Interactive genetic algorithms for shape preference assessment in engineering design. PhD Thesis; University of Michigan.

Kelly J. C.; Maheut P.; Petiot; J-F.; Papalambros P. Y. (2011). Incorporating user shape preference in engineering design optimization; Journal of Engineering Design; vol. 22; issue 9; 627-650.

Kim; H. S.; Cho; S. B. (2006). Application of Interactive Genetic Algorithm to Fashion Design. Engineering Design 38; 224-237.

Krippendorff K. and Butter R. (1984). Product semantics: Exploring the symbolic qualities of form. The Journal of the Industrial Designers Society of America; Spring; 4-9.

MacDonald; E.; Gonzalez; R.; and Papalambros; P. (2009). The Construction of Preferences for Crux and Sentinel Product Attributes. J. Eng. Design; 20(6); 09–626.

Nagamachi M. (1995). Kansei engineering: a new ergonomic consumer-oriented technology for product development. International Journal of Industrial Ergonomics; 15; 3-11.

Orsborn S.; Boatwright P.; and Cagan J. (2009). Quantifying Aesthetic Form Preference in a Utility Function. ASME J. Mech. Des.; 131; 6; 061001.

Poirson E.; Petiot J-F.; Benabes J.; Boivin L.; Blumenthal D. (2011). Detecting Design Trends Using Perceptive Tests Based on an Interactive Genetic Algorithm. Proceedings of IDETC/DTM 2011; August 28-31; 2011; Washington; DC; USA.

Poirson E.; Petiot J-F.; Boivin L.; Blumenthal D. (2013). Eliciting User Perceptions Using Assessment tests based on an Interactive Genetic Algorithm. Journal of Mechanical Design; Vol. 135; No3; 031004-1; 131004-16.

Poirson E.; Petiot J-F.; Aliouat E.; Boivin L. and Blumenthal D. (2010). Study of the convergence of Interactive Genetic Algorithm in iterative user’s tests: application to car dashboard design. Proceedings of IDMME - Virtual Concept 2010 Bordeaux; France; October 20 – 22; 2010.

Qian L.; Ben-Arieh D. (2009). Joint pricing and platform configuration in product family design with genetic algorithm. DETC2009-86110. Proceedings of IDETC/CIE 2009; August 2009; San Diego; CA; USA.

Sylcott B.; Michalek J. J.; Cagan J. (2013). Towards understanding the role of interaction effects in visual conjoint analysis. DETC2013-12622. Proceedings of IDETC/CIE 2013; August 2013; Portland; Oregon; USA.

Takagi; H. (2001). Interactive Evolutionary Computation: Fusion of the Capabilities of EC Optimization and Human Evaluation; Proc. IEEE; 89; 9; 1275–1296.

Wassenaar H.; Chen W.; Cheng J.; and Sudjianto A. (2005). Enhancing Discrete Choice Demand Modeling for Decision-Based Design. ASME J. Mech. Des.; 127(4); 514-523.

Yannou B.; Dihlmann M.; Awedikian R. (2008). Evolutive design of car silhouettes. DETC2008-49439. Proceedings of IDETC/CIE 2008; August 2008; Brooklyn; NY; USA.

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