Design Optimization Integrating the Outer Approximation Method with Process Simulators and Linear Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{deschaine:2002:FEA,
  author =       "Larry M. Deschaine and Frank D. Francone",
  title =        "Design Optimization Integrating the Outer
                 Approximation Method with Process Simulators and Linear
                 Genetic Programming",
  booktitle =    "Proceedings of the 6th Joint Conference on Information
                 Science",
  year =         "2002",
  editor =       "H. John Caulfield and Shu-Heng Chen and 
                 Heng-Da Cheng and Richard J. Duro and Vasant Honavar and 
                 Etienne E. Kerre and Mi Lu and Manuel Grana Romay and 
                 Timothy K. Shih and Dan Ventura and Paul P. Wang and 
                 Yuanyuan Yang",
  pages =        "618--621",
  address =      "Research Triangle Park, North Carolina, USA",
  month =        mar # " 8-13",
  publisher =    "JCIS / Association for Intelligent Machinery, Inc.",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-9707890-1-7",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/deschaine/FEA_2002_Design_Optimization.pdf",
  abstract =     "Fast process optimisation is a challenge. Processes
                 are often complex and the intricate simulators written
                 to solve them can take hours or days per simulation to
                 run. Optimization techniques that require many calls to
                 a simulator can take days or months to solve. While
                 advances in optimisation algorithms, such as the outer
                 approximation method have reduced the solution time by
                 a factor of ten or more when compared to other methods,
                 long solutions times still can occur. This work
                 explores the development of simulating a simulator to
                 enable optimal solution development in an accelerated
                 time frame. The technique used to develop the simulated
                 simulator is linear genetic programming (LGP). LGP
                 approximated a complex industrial process simulator
                 that took hours to execute per run with a high fitness
                 program - applied (testing) data set R2 fitness of
                 0.989. The LGP solution executes in less than a second.
                 This success opens up the possibility of optimising
                 functions faster using these LGP derived high fitness
                 simulator approximations. Since the LGP simulated
                 process simulator now executes in less than a second,
                 as opposed to hours, using an intensive multiple call
                 optimisation technique such as genetic algorithms and
                 evolutionary strategies is now also feasible.",
  notes =        "

                 FEA2002 In conjunction with Sixth Joint Conference on
                 Information Sciences

                 My printer refuses to deal with this as PDF",
}

Genetic Programming entries for Larry M Deschain Frank D Francone

Citations