Test Generation Algorithm for Linear Systems Based on Genetic Algorithm

Long, Ting; Wang, Houjun; Tian, Shulin; Huang, Jianguo; Li, Bing

doi:10.1007/s10836-010-5162-y

Cited by 4 publications

(6 citation statements)

References 14 publications

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“…But the first one works similar to manual operation, while the second limits the number of papers. The third type is widely used in educational field at present and this approach is already implemented by many researchers [2][3]. But the main limitation of many of these existing implementations is that they use a random generation strategy and create thousands of question papers with repetitive questions lacking in coverage of topics and learning objectives.…”

Section: Research Background and Related Workmentioning

confidence: 99%

Use of an Evolutionary Approach for Question Paper Template Generation

Paul¹,

Naik²,

Rane³

et al. 2012

2012 IEEE Fourth International Conference on Technology for Education

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This paper focuses on question paper template generation and its use in dynamic generation of examination question paper. Question paper template generation is a constrained based optimization problem. Choosing an efficient, scientific and rational algorithm to generate a template is the key to dynamic examination question paper generation. By using the evolutionary computational search technique of evolutionary programming and educational taxonomies, this paper analyses and experimentally proves that the generated question paper templates are best suited for dynamic examination paper generation. This new technique outperforms traditional algorithms in terms of coverage of topics, learning domains and marks distribution in the generated question paper.

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Section: Research Background and Related Workmentioning

confidence: 99%

Use of an Evolutionary Approach for Question Paper Template Generation

Paul¹,

Naik²,

Rane³

et al. 2012

2012 IEEE Fourth International Conference on Technology for Education

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“…S F and C n can be calculated by the CCM, so the quantities in (11) can also be calculated from (10). Equation (11) can be used to design the algorithm for the choice of the test points, but this method is different from that of [5,14].…”

Section: Sensitivity Algorithmmentioning

confidence: 99%

“…We can find more details of this test generation algorithm and the tolerance problem in the continuous fault model in Ref. [10]. The complete test generation algorithm is illustrated in Fig.…”

Section: Model and Algorithmmentioning

confidence: 99%

“…Then the possible range of test frequencies can be obtained, as drawn in Fig. 10 [10,8000], as shown in Table 9, and for the band-pass output (BPO), the range is [100, 10,000], as shown in Table 10. With the test generation algorithm, we can find the appropriate test signals for a given range of faults.…”

Section: Examplementioning

confidence: 99%

“…In this algorithm, the calculational precision of the software used in this paper may affect the result. Reference [10] presents a method to enhance the calculational precision in test generation using a genetic algorithm. …”

Section: Examplementioning

confidence: 99%

See 2 more Smart Citations

A Classical Parameter Identification Method and a Modern Test Generation Algorithm

Long

Wang

2010

Circuits Syst Signal Process

Self Cite

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Many methods have been presented for the testing and diagnosis of analog circuits. Each of these methods has its advantages and disadvantages. In this paper we propose a novel sensitivity analysis algorithm for the classical parameter identification method and a continuous fault model for the modern test generation algorithm, and we compare the characteristics of these methods. At present, parameter identification based on the component connection model (CCM) cannot ensure that the diagnostic equation is optimal. The sensitivity analysis algorithm proposed in this paper can choose the optimal set of trees to construct an optimal CCM diagnostic equation, and enhance the diagnostic precision. But nowadays increasing attention is being paid to test generation algorithms. Most test generation algorithms use a single value in the fault model. But the single values cannot substitute for the actual faults that may occur, because the possible faulty values vary over a continuous range. To solve this problem, this paper presents a continuous fault model for the test generation algorithm which has a continuous range of parameters. The test generation algorithm with this model can improve the treatment of the tolerance problem, including the tolerances of both normal and faulty parameters, and enhance the fault coverage rate. The two methods can be applied in different situations.

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