Test Generation Algorithm for Linear Systems Based on Genetic Algorithm

Authors:
Ting Long;Houjun Wang;Shulin Tian;Jianguo Huang;Bing Long
Affiliations:
Automation Engineering, University of Electronic Science and Technology, Chengdu, China 610054;Automation Engineering, University of Electronic Science and Technology, Chengdu, China 610054;Automation Engineering, University of Electronic Science and Technology, Chengdu, China 610054;Automation Engineering, University of Electronic Science and Technology, Chengdu, China 610054;Automation Engineering, University of Electronic Science and Technology, Chengdu, China 610054
Venue:
Journal of Electronic Testing: Theory and Applications
Year:
2010

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Abstract

This paper proposes a test generation algorithm combining genetic algorithm for fault diagnosis on linear systems. Most test generation algorithms just used a single value fault model. This test generation algorithm is based on a continuous fault model. This algorithm can improve the treatment of the tolerance problem, including the tolerances of both normal and fault parameters, and enhance the fault coverage rate. The genetic algorithm can be used to choose the characteristic values. The genetic algorithm can enhance precision of test generation algorithm especially for complex fitness functions derived from complex systems under test. The genetic algorithm can also further improve the fault coverage rate by reducing the loop number of divisions of the initial fault range. The experiments are carried out to show this test generation algorithm with a linear system and an integrated circuit.