¹ College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing city, JiangSu province, China 210016
² Key Laboratory of Nondestructive Testing (Ministry of Education), Nanchang Hangkong Univerity, Nanchang city, JiangXi province, China, 330063

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Abstract—Feature subset selection is the most important and difficult task in the field of fatigue fracture image identification. In this paper, a new method which is hybrid of linear prediction, called LP-Based Multi-Objective Genetic Algorithms (LP-MOGA) is proposed for fatigue fracture feature subset selection. In LP-MOGA, predicted new solutions with elite solutions by liner prediction to improve the local search ability. For fatigue fracture identification, texture character and fractal dimension feature are extracted for original features; and then, feature subset selection is performed by LP-MOGA, in which, the objective functions minimize error identification rate, undetected identification rate and selected featured number; at last, the identification is executed by quadratic distance classifier. Compared with other methods, the experiment results of actual data demonstrate the presented algorithm is effective.

Index Terms—Multi-objective Genetic Algorithm, Liner Prediction, Feature Extraction, Feature Subset Selection, Fatigue Fracture Identification

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Cite: Ling Li, Ming Li, Yuming Lu, and YongLiang Zhang, " A New Multi-Objective Genetic Algorithm for Feature Subset Selection in Fatigue Fracture Image Identification," Journal of Computers vol. 5, no. 7, pp. 1105-1111, 2010.