Using an evolutionary fuzzy regression for affective product design

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@InProceedings{Chan:2010:ieee-fuzz,

• author = "K. Y. Chan and T. S. Dillon and C. K. Kwong",
• title = "Using an evolutionary fuzzy regression for affective product design",
• booktitle = "IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2010)",
• year = "2010",
• address = "Barcelona, Spain",
• month = "18-23 " # jul,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4244-6920-8",
• abstract = "In affective product design, one of the main goals is to maximise customers' affective satisfaction by optimising design variables of a new product. To achieve this, a model in relating customers' affective responses and design variables of a new product is required to be developed based on customers' survey data. However, previous research on modelling the relationship between affective response and design variables cannot address the development of explicit models either involving nonlinearity or fuzziness, which exist in customers' survey data. In this paper, an evolutionary fuzzy regression approach is proposed to generate explicit models to represent this nonlinear and fuzzy relationship between affective responses and design variables. In the approach, genetic programming is used to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. Fuzzy coefficients of the model, which is represented by the tree, are determined based on a fuzzy regression algorithm. As a result, the fuzzy nonlinear regression model can be obtained to relate affective responses and design variables.",
• DOI = "doi:10.1109/FUZZY.2010.5584493",
• notes = "WCCI 2010. Also known as \cite{5584493}",

}

Genetic Programming entries for Kit Yan Chan Tharam S Dillon Che Kit Kwong

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