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Heuristic Optimization Methods for Generating Test from a Question Bank

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MICAI 2007: Advances in Artificial Intelligence (MICAI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4827))

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Abstract

In this study, heuristic optimization methods which are genetic algorithm (GA), simulated annealing (SA) and adaptive simulated annealing genetic algorithm (ASAGA) are used for selecting questions from a question bank and generating a tets. The crossover and mutation operator of standard GA can not be directly usable for generating test, since integer-coded individuals have to be used and these operators produce duplicated genoms on individuals. In order to solve this problem, a mutation operation is proposed for preventing the duplications on crossovered individuals and also directing the search randomly to the new spaces. A database containing classified test questions is created together with predefined attributes for selecting questions. A particular test can be generated automatically, without active participation of the academician. The experiments and comparative analysis show that GA with proposed mutation operator is successful as nearly 100 percent and it produces results in noteworthy computational times.

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Authors and Affiliations

  1. Electronics and Computer Education Department, University of Kocaeli, 41380, Kocaeli, Turkey

    Mehmet Yildirim

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  1. Mehmet Yildirim
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Alexander Gelbukh Ángel Fernando Kuri Morales

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© 2007 Springer-Verlag Berlin Heidelberg

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Yildirim, M. (2007). Heuristic Optimization Methods for Generating Test from a Question Bank. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_116

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  • DOI: https://doi.org/10.1007/978-3-540-76631-5_116

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76630-8

  • Online ISBN: 978-3-540-76631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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