Learning inexpensive parametric design models using an augmented genetic programming technique

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

@Article{Matthews:2006:AIEDAM,
  author =       "Peter C. Matthews and David W. F. Standingford and 
                 Carren M. E. Holden and Ken M. Wallace",
  title =        "Learning inexpensive parametric design models using an
                 augmented genetic programming technique",
  journal =      "Artificial Intelligence for Engineering Design,
                 Analysis and Manufacturing",
  year =         "2006",
  volume =       "20",
  pages =        "1--18",
  publisher =    "Cambridge University Press",
  keywords =     "genetic algorithms, genetic programming, Data Mining,
                 Design Model Induction, Knowledge Elicitation,
                 Metamodels SVM, GP-HEM, demes",
  DOI =          "doi:10.10170S089006040606001X",
  size =         "18 pages",
  abstract =     "Previous applications of genetic programming (GP) have
                 been restricted to searching for algebraic
                 approximations mapping the design parameters e.g.,
                 geometrical parameters, to a single design objective
                 e.g., weight. In addition, these algebraic expressions
                 tend to be highly complex. By adding a simple extension
                 to the GP technique, a powerful design data analysis
                 tool is developed. This paper significantly extends the
                 analysis capabilities of GP by searching for multiple
                 simple models within a single population by splitting
                 the population into multiple islands according to the
                 design variables used by individual members. Where
                 members from different islands 'cooperate', simple
                 design models can be extracted from this cooperation.
                 This relatively simple extension to GP is shown to have
                 powerful implications to extracting design models that
                 can be readily interpreted and exploited by human
                 designers. The full analysis method, GP heuristics
                 extraction method, is described and illustrated by
                 means of a design case study.",
  notes =        "School of Engineering, University of Durham, Durham,
                 United Kingdom

                 BAE Systems, Advanced Technology Centre, Filton,
                 Bristol, United Kingdom

                 Aerodynamic Methods and Tools, Airbus UK, Filton,
                 Bristol, United Kingdom

                 Engineering Design Centre, Engineering Department,
                 University of Cambridge, Cambridge, United Kingdom

                 Flat screen design.",
}

Genetic Programming entries for Peter C Matthews David W F Standingford Carren M E Holden Ken M Wallace

Citations