Using Linear Genetic Programming to Develop a C/C++ Simulation Model of a Waste Incinerator

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Larry M. Deschaine and Janardan J. Patel and 
                 Ronald D. Guthrie and Joseph T. Grimski and M. J. Ades",
  title =        "Using Linear Genetic Programming to Develop a {C/C++}
                 Simulation Model of a Waste Incinerator",
  booktitle =    "Advanced Technology Simulation Conference",
  year =         "2001",
  editor =       "M. Ades",
  pages =        "41--48",
  address =      "Seattle",
  month =        "22-26 " # apr,
  organisation = "Society for Computer Simulations",
  keywords =     "genetic algorithms, genetic programming, discipulus,
                 DSS, 10 demes",
  broken =       "",
  URL =          "",
  URL =          "",
  URL =          "",
  abstract =     "Abstract We explore whether Linear Genetic Programming
                 (LGP) can evolve a C/C++ computer simulation model that
                 accurately models the performance of a waste
                 incinerator. Human expert written simulation models are
                 used worldwide in a variety of industrial and business
                 applications. They are expensive to develop, may or may
                 not be valid for the specific process that is being
                 modeled, and may be erroneous.

                 LGP is a machine learning technique that uses
                 information about a process's inputs and outputs to
                 simultaneously write the simulation model, calibrate
                 and optimize the model's constants, and validate the
                 solution. The result is a calibrated, validated,
                 error-free C/C++ computer model specific to the desired

                 To evaluate whether this is feasible for complex
                 industrial processes, the method on data obtained from
                 the operation of a hazardous waste incinerator. This
                 process is difficult to model. Previously, in a
                 well-conducted study, the popular machine learning
                 technique, analytic neural networks, was unable to
                 derive useful solutions to this problem. The present
                 study uses various mutation rates (95%, 50%, and 10%),
                 10 random initial seeds per mutation rate, and a large
                 number of generations (1,280 to 4,461). The LGP system
                 provided accurate solutions to this problem with a
                 validation data measure of fitness, R2, equal to

                 This work demonstrates the value of LGP for process
                 simulation. The study confirms previously published
                 results and found that the distribution of outputs from
                 multiple genetic programming (GP) runs tends to include
                 an extended tail of outstanding solutions. Such a tail
                 was not found in previous studies of neural networks.
                 This result emphasizes the need for employing a
                 strategy of multiple runs using various initial seeds
                 and mutation rates to find good solutions to complex
                 problems using LGP. This result also demonstrates the
                 value of a fast LGP algorithm implemented at the
                 machine code level for both static scientific data
                 mining and real-time process control. The work consumed
                 600 hours of CPU time; it is estimated that other GP
                 algorithms would have required between 4 and 136 years
                 of CPU time to achieve similar results.",
  notes =        "ASTC 2001
                 Science Applications International Corporation

                 Model of C02 concentration from 1 weeks live running
                 hourly logs. Interactive Evaluation (Unclear what this
                 means). Print out of PDF poor

                 Author orignally misspelt Larry M. Deschain:-(",

Genetic Programming entries for Larry M Deschaine Janardan J Patel Ronald D Guthrie Joseph T Grimski M J Ades