Using an evolutionary fuzzy regression for affective product design

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  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