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Time Consuming Numerical Model Calibration Using Genetic Algorithm (GA), 1-Nearest Neighbor (1NN) Classifier and Principal Component Analysis (PCA)

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2 Author(s)
Yang Liu ; Dept. of Eng., Exeter Univ. ; Wen-Jing Ye

Single objective genetic algorithm (SGA) optimization process usually needs a large number of objective function evaluations before converging towards global optimum or a near-optimum. The SGA is used as automatic calibration method for a wide range of numerical models. However, the evaluation of the quality of solutions is very time-consuming in many real-world numerical model calibration problems. The algorithm SGA-INN-PCA, an effective and efficient dynamic approximation model to reduce the number of actual fitness evaluations, is presented in this paper. Training data of 1NN classifier are produced from early generations. 1-nearest neighbor (INN) classifier is used to predict objective function values for evaluations. Principal component analysis (PCA) linearly transforms high-dimensional optimization parameters into low-dimensional optimization parameters to save test time for 1NN. The test results show that the proposed method only requires about 25 percent of actual fitness evaluations of the SGA

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Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the

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