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A test paper generation algorithm based on diseased enhanced genetic algorithm.

JunChuan Cui1, Ya Zhou1, Guimin Huang1

  • 1School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China.

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Summary
This summary is machine-generated.

A new Diseased Enhanced Genetic Algorithm (DEGA) improves test paper generation efficiency. This algorithm generates exams faster with fewer iterations, outperforming traditional methods for educational assessments.

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Applications in subject areasDistance education and online learningImproving classroom teaching

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Computer Science

Background:

  • Increasing demand for efficient test paper generation algorithms due to frequent exams.
  • Limitations of existing algorithms like Genetic Algorithm (GA) in avoiding local optima and randomness.

Purpose of the Study:

  • To propose and evaluate a Diseased Enhanced Genetic Algorithm (DEGA) for test paper generation.
  • To enhance the efficiency and quality of automated test paper creation.

Main Methods:

  • Development of DEGA, incorporating a dynamic crossover probability and Hamming distance for population diversity.
  • Introduction of a novel 'disease operator' (natural disease, infection, mutation) to mitigate random mutation issues.
  • Comparative experimental analysis using College English Test Band 4 (CET-4) data (2014-2021) against RSA, GA, and EGA.

Main Results:

  • DEGA demonstrated faster test paper generation speeds compared to RSA, GA, and EGA.
  • The algorithm required fewer iterations to achieve optimal results.
  • DEGA significantly outperformed other algorithms in terms of result quality and efficiency.

Conclusions:

  • The proposed DEGA effectively addresses the limitations of traditional genetic algorithms in test paper generation.
  • DEGA offers a superior approach for creating high-quality test papers efficiently.
  • The 'disease operator' is a key innovation for improving population diversity and avoiding local optima.