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

Yang Liu; Wen-Jing Ye

doi:10.1109/IEMBS.2005.1616641

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

Conf Proc IEEE Eng Med Biol Soc. 2005:2005:1208-11. doi: 10.1109/IEMBS.2005.1616641.

Authors

Yang Liu¹, Wen-Jing Ye

Affiliation

¹ Department of Engineering, Exeter University, EX4 4QF, UK.

PMID: 17282410
DOI: 10.1109/IEMBS.2005.1616641

Abstract

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-1NN-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 (1NN) 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.