Extending the boundaries of design optimization by integrating fast optimization techniques with machine-code-based, linear genetic programming

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

@Article{francone:ebdo,
  author =       "Frank D. Francone and Larry M. Deschaine",
  title =        "Extending the boundaries of design optimization by
                 integrating fast optimization techniques with
                 machine-code-based, linear genetic programming",
  journal =      "Information Sciences",
  volume =       "161",
  number =       "3-4",
  month =        "20 " # apr,
  year =         "2004",
  pages =        "99--120",
  note =         "FEA 2002",
  keywords =     "genetic algorithms, genetic programming, Discipulus,
                 Darcy's law",
  DOI =          "doi:10.1016/j.ins.2003.05.006",
  abstract =     "Optimised models of complex physical systems are
                 difficult to create and time consuming to optimise. The
                 physical and business processes are often not well
                 understood and are therefore difficult to model. The
                 models of often too complex to be well optimized with
                 available computational resources. Too often
                 approximate, less than optimal models result. This work
                 presents an approach to this problem that blends three
                 well-tested components. First: We apply Linear Genetic
                 Programming (LGP) to those portions of the system that
                 are not well understood -- for example, modelling data
                 sets, such the control settings for industrial or
                 chemical processes, geotechnical property prediction or
                 UXO detection. LGP builds models inductively from known
                 data about the physical system. The LGP approach we
                 highlight is extremely fast and builds rapid to
                 execute, high-precision models of a wide range of
                 physical systems. Yet it requires few parameter
                 adjustments and is very robust against overfitting.
                 Second: We simulate those portions of the system -- for
                 example, the cost model for the processes -- these are
                 well understood with human built models. Finally: We
                 optimise the resulting meta-model using Evolution
                 Strategies (ES). ES is a fast, general-purpose
                 optimiser that requires little pre-existing domain
                 knowledge. We have developed this approach over a
                 several years period and present results and examples
                 that highlight where this approach can greatly improve
                 the development and optimisation of complex physical
                 systems.",
  notes =        "Kodak, multiple GP runs, lead to new features in
                 Discipulus, land mines, ES-CDSA",
}

Genetic Programming entries for Frank D Francone Larry M Deschain

Citations