A New Multi-Objective Genetic Algorithm for Feature Subset Selection in Fatigue Fracture Image Identification

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.