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:1.3949

@InCollection{Deschaine:2005:IPEA,
  author =       "L. M. Deschaine and F. D. Francone",
  title =        "Extending the Boundaries of Design Optimization by
                 Integrating Fast Optimization Techniques with Machine
                 Code Based, Linear Genetic Programming",
  booktitle =    "Information Processing with Evolutionary Algorithms",
  year =         "2005",
  editor =       "Manuel Grana and Richard J. Duro and 
                 Alicia d'Anjou and Paul P. Wang",
  series =       "Advanced Information and Knowledge Processing",
  chapter =      "2",
  pages =        "11--30",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-85233-866-4",
  URL =          "http://dx.doi.org/10.1007/1-84628-117-2_2",
  DOI =          "doi:10.1007/1-84628-117-2_2",
  abstract =     "Summary and Conclusions

                 We are in the early stages of building a comprehensive,
                 integrated optimisation and modelling system to handle
                 complex industrial problems. We believe a combination
                 of machine-code-based, LGP (for modelling) and ES CDSA
                 (for optimization) together provides the best
                 combination of available tools and algorithms for this
                 task.

                 By conceiving of design optimisation projects as
                 integrated modeling and optimisation problems from the
                 outset, we anticipate that engineers and researchers
                 will be able to extend the range of problems that are
                 solvable, given today's technology.

                 The general approach of Deschaine and Francone is to
                 reverse engineer a system with Linear Genetic
                 Programming at the machine code level. This approach
                 provides very fast and accurate models of the process
                 that will be subject to optimisation. The optimisation
                 process itself is performed using an Evolutionary
                 Strategy with completely deterministic parameter
                 self-adaptation. The authors have tested this approach
                 in a variety of academic problems. They target
                 industrial problems, characterised by low formalisation
                 and high complexity. As a final illustration they deal
                 with the design of an incinerator and the problem of
                 subsurface unexploded ordnance detection.",
  notes =        "cf FEA 2002 and JCIS 2002. Series editors Xindong Wu
                 and Lakhmi Jain",
}

Genetic Programming entries for Larry M Deschain Frank D Francone

Citations