Abstract
Bu çalışmada,
bir soru bankası içerisinden istenen ölçütlere bağlı olarak akıllı test sayfası
oluşturma probleminin genetik algoritma ile çözümü sunulmuştur. Akıllı test sayfası oluşturma, soru bankasındaki her bir sorunun pek çok
özniteliğe sahip olmasına bağlı olarak çok parametreli bir optimizasyon
problemi olarak ele alınmaktadır. Genetik
algoritma optimizasyon problemlerinin çözümünde sıkça kullanılan paralel arama
özelliğine sahip sezgisel arama algoritmasıdır. Çalışmada standart genetik
algoritmanın çaprazlama ve mutasyon operatörlerinde yapılan değişiklikler ile
genetik algoritmanın performansının artması ve istenen kalitede test sayfalarının
oluşturulması sağlanmıştır. Deneysel sonuçlar, iyileştirilmiş genetik
algoritmanın aynı koşullardaki standart genetik algoritma ile
karşılaştırıldığında daha etkili olduğunu göstermektedir. Yapılan çalışmada,
kullanıcıların genetik algoritma ve test sayfası için istediği ölçütleri
belirleyebildiği ve algoritmayı çalıştırabildiği web tabanlı bir kullanıcı
arayüzü uygulaması geliştirilmiştir.
Keywords
Genetik algoritma soru bankası test sayfası oluşturma kullanıcı arayüzü web tabanlı uygulama
References
- [1] Xiumin C., Dengcai W., Meining Z., Yanping Y., “Research on Intelligent Test Paper Generation Base on Improved Genetic Algorithm”, The 6th International Conference on Computer Science & Education (ICCSE), IEEE, 269-272, August 3-5 2011.
- [2] Jun N., “An improved genetic algorithm for Intelligent test paper generation”, Intelligent Computation Technology and Automation (ICICTA), 7th International Conference on IEEE, 72-75, October 2014.
- [3] Zhang K., Zhu L., “Application of Improved Genetic Algorithm in Automatic Test Paper Generation”, Chinese Automation Congress (CAC), IEEE, 495-499, November 2015.
- [4] Sun X., “Study on Test Databank Construction And Algorithm of Test Paper Generation System”, Second International Symposium on Electronic Commerce and Security (ISECS), IEEE, 297-302, May 2009.
- [5] Shan Y., “The Research and Realization of Multi-threaded Intelligent Test Paper Generation Based on Genetic Algorithm”, International Conference on Computer and Information Application (ICCIA), IEEE, 461-464, 2010.
- [6] Xiong L., Shi J., “Automatic Generating Test Paper System Based On Genetic Algorithm”, Second International Workshop on Education Technology and Computer Science, IEEE, 2010.
- [7] Wu X., Song Y., “Research on Intelligent Auto-generating Test Paper Based on Improved Genetic Algorithms”, International Conference on Computational Intelligence and Software Engineering, International Conference on IEEE, December 2009.
- [8] Goldberg D. E., Genetic Algorithms in Search, Optimization and Machine Learning, 1st ed., Addison-Wesley Publishing Company Inc., Boston, MA, USA, 1989.
- [9] Tuncer A, Yildirim M. “Dynamic path planning of mobile robots with improved genetic algorithm”, Computers & Electrical Engineering, Cilt 38, No 6, 1564-1572, 2012.
- [10] İnternet: “Carnegie Mellon University, Question-Answer Dataset”, http://www.cs.cmu.edu/~ark/QA-data/, Son Erişim Tarihi: 01.08.2017.
Abstract
In
this study, the solution of the problem of generating an intelligent test paper
with a genetic algorithm is presented depending on the required criteria in a
question bank. Generating the intelligent test paper is considered as a
multi-parameter optimization problem, depending on whether each question in the
question bank has many attributes. A genetic algorithm is a heuristic search
algorithm with parallel search feature which is often used to solve
optimization problems. In the study, the changes in the crossover and mutation
operators of the standard genetic algorithm increased the performance of the
genetic algorithm and created the test papers in the required quality.
Experimental results show that the improved genetic algorithm is more effective
when compared to the standard genetic algorithm in the same conditions. In the
study, a web-based user interface application was developed in which users can
set the criteria for genetic algorithm and test paper and can run the algorithm
References
- [1] Xiumin C., Dengcai W., Meining Z., Yanping Y., “Research on Intelligent Test Paper Generation Base on Improved Genetic Algorithm”, The 6th International Conference on Computer Science & Education (ICCSE), IEEE, 269-272, August 3-5 2011.
- [2] Jun N., “An improved genetic algorithm for Intelligent test paper generation”, Intelligent Computation Technology and Automation (ICICTA), 7th International Conference on IEEE, 72-75, October 2014.
- [3] Zhang K., Zhu L., “Application of Improved Genetic Algorithm in Automatic Test Paper Generation”, Chinese Automation Congress (CAC), IEEE, 495-499, November 2015.
- [4] Sun X., “Study on Test Databank Construction And Algorithm of Test Paper Generation System”, Second International Symposium on Electronic Commerce and Security (ISECS), IEEE, 297-302, May 2009.
- [5] Shan Y., “The Research and Realization of Multi-threaded Intelligent Test Paper Generation Based on Genetic Algorithm”, International Conference on Computer and Information Application (ICCIA), IEEE, 461-464, 2010.
- [6] Xiong L., Shi J., “Automatic Generating Test Paper System Based On Genetic Algorithm”, Second International Workshop on Education Technology and Computer Science, IEEE, 2010.
- [7] Wu X., Song Y., “Research on Intelligent Auto-generating Test Paper Based on Improved Genetic Algorithms”, International Conference on Computational Intelligence and Software Engineering, International Conference on IEEE, December 2009.
- [8] Goldberg D. E., Genetic Algorithms in Search, Optimization and Machine Learning, 1st ed., Addison-Wesley Publishing Company Inc., Boston, MA, USA, 1989.
- [9] Tuncer A, Yildirim M. “Dynamic path planning of mobile robots with improved genetic algorithm”, Computers & Electrical Engineering, Cilt 38, No 6, 1564-1572, 2012.
- [10] İnternet: “Carnegie Mellon University, Question-Answer Dataset”, http://www.cs.cmu.edu/~ark/QA-data/, Son Erişim Tarihi: 01.08.2017.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Original Articles |
| Authors | |
| Publication Date | December 22, 2017 |
| Submission Date | October 5, 2017 |
| Published in Issue | Year 2017 Volume: 5 Issue: 4 |
