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

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

@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 Deschaine Frank D Francone

Citations