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

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

@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