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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Deschain:2001:ASTC,

• 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 = "http://pw2.netcom.com/~lmdmit84/ASTC2001-LGP-INCINERATOR.pdf",
• URL = "http://www.aimlearning.com/Environmental.Engineering.pdf",
• URL = "http://citeseer.ist.psu.edu/451766.html",
• URL = "http://citeseer.ist.psu.edu/396498.html",
• 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 process.

  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 0.961.

  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 http://www.scs.org/confernc/astc01/prelim-program/astc01prelim.html (broken 2020) 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

Citations