Estimate design intent: a multiple genetic programming and multivariate analysis based approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Ishino:2002:AEI,
  author =       "Yoko Ishino and Yan Jin",
  title =        "Estimate design intent: a multiple genetic programming
                 and multivariate analysis based approach",
  journal =      "Advanced Engineering Informatics",
  year =         "2002",
  volume =       "16",
  pages =        "107--125",
  number =       "2",
  keywords =     "genetic algorithms, genetic programming, Design
                 process, Design intent, Multivariate analysis",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6X1X-45XR6TT-3/2/d9b1ec675457ba42091348338705293d",
  ISSN =         "1474-0346",
  DOI =          "doi:10.1016/S1474-0346(01)00005-2",
  abstract =     "Understanding design intent of designers is important
                 for managing design quality, achieving coherent
                 integration of design solutions, and transferring
                 design knowledge. This paper focuses on automatically
                 estimating design intent, represented as a summation of
                 weighted functions, based on the operational and
                 product-specific information monitored through design
                 processes. This estimated design intent provides a
                 basis for us to identify the evaluation tendency of
                 designers' ways of doing design. To represent and
                 estimate the design intent, we introduced a staged
                 design evaluation model as a general yet powerful model
                 of design decision-making process, and developed a
                 methodology for estimation of design intent (MEDI) as a
                 reasoning method. MEDI is composed of two basic
                 algorithms. One is our newly introduced multiple
                 genetic programming (MGP) and the other is statistical
                 multivariate analysis including principal component
                 analysis and multivariate regression. The
                 characteristics of MEDI are; (1) principal component
                 analysis provides approximate evaluation of how much
                 preferable a specific product model is, assuming the
                 final product model (or design) is the most preferable
                 one; (2) MGP enables us to simultaneously estimate both
                 structure of target performance functions and the
                 approximate values of their weights for a domain of
                 design problems; and (3) multivariate regression
                 readjusts the approximate weights obtained by MGP into
                 more accurate ones for specific design problems within
                 the domain. Our framework and methods have been
                 successfully tested in a case study of designing a
                 double-reduction gear system.",
}

Genetic Programming entries for Yoko Ishino Yan Jin

Citations