Abstract
Genetic Algorithm has the dynamic performance and the auto adaptability. The algorithm can search the solution glibly and include the operation of coding, selection, intercross, mutation of the chromosome. The search starts from an initial cluster and reduces the probability of falling in local optima. In the test paper auto-generation, it can satisfy the requirement of the test paper generation system, improve the quality and efficiency of extracting the subjects from the test bank. © 2011 Springer-Verlag.
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CITATION STYLE
Hu, J. J., Sun, Y. H., & Xu, Q. Z. (2011). The genetic algorithm in the test paper generation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6987 LNCS, pp. 109–113). https://doi.org/10.1007/978-3-642-23971-7_16
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