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

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@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

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