Improving Predictability of Simulation Models using Evolutionary Computation-Based Methods for Model Error Correction

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

@PhdThesis{Zechman:thesis,
  author =       "Emily Michelle Zechman",
  title =        "Improving Predictability of Simulation Models using
                 Evolutionary Computation-Based Methods for Model Error
                 Correction",
  school =       "Civil Engineering, North Carolina State University",
  year =         "2005",
  address =      "Raleigh, USA",
  keywords =     "genetic algorithms, genetic programming, genetic
                 programming, non-uniquness, evolutionary computation,
                 alternatives generation, parameter estimation, water
                 resources management, model error correction,
                 calibration",
  URL =          "http://www.lib.ncsu.edu/theses/available/etd-08082005-105133/unrestricted/etd.pdf",
  URL =          "http://www.lib.ncsu.edu/theses/available/etd-08082005-105133/",
  size =         "148 pages",
  abstract =     "Simulation models are important tools for managing
                 water resources systems. An optimisation method coupled
                 with a simulation model can be used to identify
                 effective decisions to efficiently manage a system. The
                 value of a model in decision-making is degraded when
                 that model is not able to accurately predict system
                 response for new management decisions. Typically,
                 calibration is used to improve the predictability of
                 models to match more closely the system observations.
                 Calibration is limited as it can only correct parameter
                 error in a model. Models may also contain structural
                 errors that arise from mis-specification of model
                 equations. This research develops and presents a new
                 model error correction procedure (MECP) to improve the
                 predictive capabilities of a simulation model. MECP is
                 able to simultaneously correct parameter error and
                 structural error through the identification of suitable
                 parameter values and a function to correct
                 misspecifications in model equations. An evolutionary
                 computation (EC)-based implementation of MECP builds
                 upon and extends existing evolutionary algorithms to
                 simultaneously conduct numeric and symbolic searches
                 for the parameter values and the function,
                 respectively. Non-uniqueness is an inherent issue in
                 such system identification problems. One approach for
                 addressing non-uniqueness is through the generation of
                 a set of alternative solutions. EC-based techniques to
                 generate alternative solutions for numeric and symbolic
                 search problems are not readily available. New EC-based
                 methods to generate alternatives for numeric and
                 symbolic search problems are developed and investigated
                 in this research. The alternatives generation
                 procedures are then coupled with the model error
                 correction procedure to improve the predictive
                 capability of simulation models and to address the
                 non-uniqueness issue. The methods developed in this
                 research are tested and demonstrated for an array of
                 illustrative applications.",
  notes =        "etd-08082005-105133",
}

Genetic Programming entries for Emily M Zechman

Citations