Evidence for using Interactive Genetic Algorithms in shape preference assessment

Preferences for subjective design qualities, such as shape, are difficult to capture and relate to engineering specifications. The present paper uses Interactive Evolutionary Systems (IES) to locate a human user's most preferred cola bottle shape among a set of parameterised bottle shapes. Several researchers have used IES to identify user preference, but have never independently confirmed that preference. In the present paper, participants used the IGA to select their favourite design from a small design space. The method of paired comparisons was used to characterise preference over the entire design space, showing a 91% agreement with IGA most preferred selections.

[1]  Hideyuki Takagi,et al.  Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation , 2001, Proc. IEEE.

[2]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[3]  Hideyoshi Yanagisawa,et al.  Interactive Reduct Evolutional Computation for Aesthetic Design , 2005, J. Comput. Inf. Sci. Eng..

[4]  Jianhong Shen,et al.  On the foundations of vision modeling: I. Weber’s law and Weberized TV restoration , 2003 .

[5]  R. Lanzara.  Weber's law modeled by the mathematical description of a beam balance. , 1994, Mathematical biosciences.

[6]  John M. Rose,et al.  Applied Choice Analysis: A Primer , 2005 .

[7]  Panos Y. Papalambros,et al.  Incorporating user shape preference in engineering design optimisation , 2011 .

[8]  D. Hensher,et al.  Stated Choice Methods: Analysis and Applications , 2000 .

[9]  N. Cowan.  Evolving conceptions of memory storage, selective attention, and their mutual constraints within the human information-processing system. , 1988, Psychological bulletin.

[10]  Cpm Cor Govers,et al.  What and how about quality function deployment (QFD) , 1996 .

[11]  Ying Zhang,et al.  Reduced human fatigue interactive evolutionary computation for micromachine design , 2005, 2005 International Conference on Machine Learning and Cybernetics.

[12]  Dimitri N. Mavris,et al.  Small Supersonic Transport Concept Exploration Using Interactive Evolutionary Algorithms , 2004 .

[13]  Jean-François Petiot,et al.  Product design: a vectors field-based approach for preference modelling , 2006 .

[14]  John H. Frazer,et al.  Capturing aesthetic intention during interactive evolution , 2006, Comput. Aided Des..

[15]  Mitsuo Nagamachi,et al.  Kansei Engineering: A new ergonomic consumer-oriented technology for product development , 1995 .

[16]  Gregory H. Wakefield,et al.  Efficient perceptual tuning of hearing aids with genetic algorithms , 2004, IEEE Transactions on Speech and Audio Processing.

[17]  Damien Chablat,et al.  Subjective Evaluation of Forms in an Immersive Environment , 2007, ArXiv.

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  Sung-Bae Cho,et al.  Towards Creative Evolutionary Systems with Interactive Genetic Algorithm , 2002, Applied Intelligence.

[20]  Jeremy J. Michalek,et al.  Linking Marketing and Engineering Product Design Decisions via Analytical Target Cascading , 2005 .

[21]  Yoon Mo Jung,et al.  On the foundations of vision modeling III. Pattern-theoretic analysis of Hopf and Turing's reaction-diffusion patterns , 2003 .

[22]  R. Dawkins.  The Blind Watchmaker , 1986 .

[23]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .