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Research Article
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Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization

Year 2022, Volume: 6 Issue: 2, 220 - 232, 31.12.2022

Fatih Ağalday , Ali Nizam

https://doi.org/10.38088/jise.1006070

Abstract

Exam seat allocation has become a complex problem, with an increasing number of students, subjects, exams, departments, and rooms in higher education institutions. The requirements and constraints of this problem demonstrate characteristics similar to extensively researched exam timetabling problems. They plan for a limited capacity effectively and efficiently. Additionally, exam seating requires a seating arrangement to reduce the number of cheating incidents. In the literature, several genetic algorithm-based methods have been recommended to prevent students, who are close friends, from sitting close during the exams while providing the best exam session arrangement. We improved the performance of the genetic algorithm using parameter optimization and a new elitism method to increase the saturation rate and accuracy. The algorithm was tested on a real-world dataset and demonstrated high potential for the realization of a high-quality seating arrangement compatible with the requirements of educational institutions.

Keywords

Data science applications in education genetic algorithm multi-parameter optimization

References

  • Walker J. 1998. Student plagiarism in universities: What are we doing about it? High Educ Res Dev, 21(1), pp. 89–106.
  • McCabe DL, Klebe LT, Butterfield KD. 2010. Cheating in Academic Institutions: A Decade of. Ethics Behav., 8422, pp. 37–41.
  • Mccabe DL. 2005. Cheating among college and university students: A North American perspective. Int J Educ Integr, 1(1).
  • Newstead S. 1996. Individual differences in student motivation. J Educ Psychol., 88(2), pp. 229–41.
  • Yee K, MacKown P. 2009. Detecting and preventing cheating during exams. Pedagogy, not Policing.
  • Wang J, Tong Y, Ling M, Zhang A, Hao L, Li X. 2015. Analysis on test cheating and its solutions based on extenics and information technology. Procedia Comput Sci., 55(2015):1009–14. DOI:10.1016/j.procs.2015.07.102
  • David LT. 2015. Academic cheating in college students: relations among personal values , self-esteem and mastery. Procedia - Soc Behav Sci., 187, pp. 88–92, DOI:10.1016/j.sbspro.2015.03.017
  • Topîrceanu A. 2017. Breaking up friendships in exams: A case study for minimizing student cheating in higher education using social network analysis, Comput Educ., 115, pp. 171–87, DOI: 10.1016/j.compedu.2017.08.008
  • Danielsen RD, Simon AF, Pavlick R. 2006. The Culture of Cheating: From the Classroom to the Exam Room. J Physician Assist Educ. , 17(1), pp. 23–9.
  • Ayob M, Malik A. 2011. A New Model for an Examination-Room Assignment Problem. J Comput Sci., 11(10):187–90.
  • Güler MG, Geçici E. 2020. A spreadsheet-based decision support system for examination timetabling. Turkish J Electr Eng Comput Sci., 28(3): pp. 1584–98.
  • Elsaka T. 2017. Autonomous Generation of Conflict-Free Examination Timetable Using Constraint Satisfaction Modelling. In: IntArtificial Intelligence and Data Processing Symposium (IDAP), p. 1–10.
  • Vasupongayya S, Noodam W, Kongyong P. 2013. Developing Examination Management System: Senior Capstone Project, a Case Study. Int J Comput Inf Eng., 7(7), pp. 1046–52.
  • Dammak A, Elloumi A, Kamoun H. 2006. Classroom assignment for exam timetabling. Adv Eng Softw., 37(10), pp. 659–66.
  • Mohmad Kahar MN, Kendall G. 2014. Universiti Malaysia Pahang examination timetabling problem: Scheduling invigilators, J Oper Res Soc., 65(2), pp-214–26.
  • Elloumi A, Kamoun H, Jarboui B, Dammak A. 2014. The classroom assignment problem: Complexity, size reduction and heuristics, Appl Soft Comput J., 14 (PART C), pp-677–86. DOI: 10.1016/j.asoc.2013.09.003
  • Burke EK, Mccollum B, Mcmullan P, Qu R. 2006. Examination Timetabling: A New Formulation, In Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling, Brno, pp. 373-375.
  • Chaki PK. 2016. Algorithm For Efficient Seating Plan For Centralized Exam System, Int Conf Comput Tech Inf Commun Technol., pp. 320–5.
  • Inamdar A, Gangar A, Gupta A, Shrivastava V. 2018. Automatic Exam Seating & Teacher Duty Allocation System, Second Int Conf Inven Commun Comput Technol. (Icicct), pp. 1302–6.
  • Wang XF, Chen G. 2003. Complex networks: Small-world, scale-free and beyond, IEEE Circuits and Systems Magazine, 2003, 3(1), pp. 6–20.
  • Brailsford SC, Potts CN, Smith BM. 1999. Constraint satisfaction problems: Algorithms and applications, European Journal of Operational Research, Vol. 119, pp. 557–81.
  • Hosny M, Fatima S. 2011. A Survey of Genetic Algorithms for the University Timetabling Problem A Survey of Genetic Algorithms for the University Timetabling Problem Manar Hosny and Shameem Fatima, Int Conf Future Inf Technol IPCSIT 13., pp. 34–9.
  • Abdelhalim EA, Khayat GA El. 2016. A Utilization-based Genetic Algorithm for Solving the University Timetabling Problem ( UGA ), Alexandria Eng J, 55(2), pp.1395–409. DOI: 10.1016/j.aej.2016.02.017
  • Karova M. 2004. Solving Timetabling Problems Using Genetic Algorithms, 2Th Int’l Spring Seminar on Electronics Technology, pp. 20–2.
  • Rozaimee A, Shafee AN, Anissa N, Hadi A, Mohamed MA. 2017. A Framework for University’s Final Exam Timetable Allocation Using Genetic Algorithm, World Appl Sci J., 35(7), pp. 1210–5.
  • Pillay N, Banzhaf W. 2010. An informed genetic algorithm for the examination timetabling problem, Appl Soft Comput J., 10(2), pp. 457–67.
  • Abdullah S, Alzaqebah M. 2013. A hybrid self-adaptive bees algorithm for examination timetabling problems, Appl Soft Comput J., 13(8), pp. 3608–20. DOI: 10.1016/j.asoc.2013.04.010
  • Holland JH. 1992. Adaption in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press
  • Şen Z. 2004. Genetik Algoritmalar ve En İyileme Yöntemleri, İstanbul, Su Vakfı Yayınları
  • Karaboğa D. 2004. Yapay Zekâ Optimizasyon Algoritmaları, İstanbul, Atlas Yayınevi, pp. 75–112.
  • Negnevitsky M. 2005. Artificial intelligence: A guide to intelligent systems, Systems (2nd Edition), Pearson Education.
  • Tindell KW, Burns A, Wellings AJ. 1992. Allocating hard real-time tasks: An NP-Hard problem made easy, J Real-Time Syst., (4), p. 145.
  • Bulut F, Subaşı Ş. 2015. Best Seating Plan For Centeral Exams Using Genetic Algorihtms. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Derg., 17(51), pp. 122–37.
  • Beligiannis GN, Moschopoulos C, Likothanassis SD. 2009. A genetic algorithm approach to school timetabling, J Oper Res Soc., 60(1), pp. 23–42.
  • Akkan C, Gülcü A. 2018. Computers and Operations Research A bi-criteria hybrid Genetic Algorithm with robustness objective for the course timetabling problem, Comput Oper Res., 90:pp. 22–32. DOI:10.1016/j.cor.2017.09.007
  • Soremekun, G., Gürdal, Z., Haftka, R. T., & Watson, L. T. 2001. Composite laminate design optimization by genetic algorithm with generalized elitist selection. Computers & structures, 79(2), 131-143.
  • Ağalday, M. F. 2018, Genetik algoritma ile merkezi sınavlarda tek ve çok boyutlu yakınlığa göre en iyi oturum planının oluşturulması (Master's thesis, Fatih Sultan Mehmet Vakıf Üniversitesi, Mühendislik ve Fen Bilimleri Enstitüsü).
  • Nadeau C, Bengio Y. 2003. Inference for the Generalization Error. Mach Learn Norwell Kluwer Acad Publ, 52(3), pp. 307–13.
  • Schaffer JD. 1989. A study of control parameters affecting online performance of genetic algorithms for function optimization. In: 3rd International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann, p. 51–60.
  • Aksu Ö. 2008. Yeni Bir Paralel Genetik Algoritma Modeli Ve Analog Devre Tasarımına Uygulanması Yüksek Lisans Tezi, Kayseri Erciyes Üniversitesi.
  • Grefenstette JJ. 1992. Genetic algorithms for changing environments. Parallel Probl Solving from Nat., 2, pp. 137–44.

Year 2022, Volume: 6 Issue: 2, 220 - 232, 31.12.2022

Fatih Ağalday , Ali Nizam

https://doi.org/10.38088/jise.1006070

Abstract

Thanks

Mardin Artuklu Üniversitesine araştırmada veri sağladığı için teşekkür ederiz.

References

  • Walker J. 1998. Student plagiarism in universities: What are we doing about it? High Educ Res Dev, 21(1), pp. 89–106.
  • McCabe DL, Klebe LT, Butterfield KD. 2010. Cheating in Academic Institutions: A Decade of. Ethics Behav., 8422, pp. 37–41.
  • Mccabe DL. 2005. Cheating among college and university students: A North American perspective. Int J Educ Integr, 1(1).
  • Newstead S. 1996. Individual differences in student motivation. J Educ Psychol., 88(2), pp. 229–41.
  • Yee K, MacKown P. 2009. Detecting and preventing cheating during exams. Pedagogy, not Policing.
  • Wang J, Tong Y, Ling M, Zhang A, Hao L, Li X. 2015. Analysis on test cheating and its solutions based on extenics and information technology. Procedia Comput Sci., 55(2015):1009–14. DOI:10.1016/j.procs.2015.07.102
  • David LT. 2015. Academic cheating in college students: relations among personal values , self-esteem and mastery. Procedia - Soc Behav Sci., 187, pp. 88–92, DOI:10.1016/j.sbspro.2015.03.017
  • Topîrceanu A. 2017. Breaking up friendships in exams: A case study for minimizing student cheating in higher education using social network analysis, Comput Educ., 115, pp. 171–87, DOI: 10.1016/j.compedu.2017.08.008
  • Danielsen RD, Simon AF, Pavlick R. 2006. The Culture of Cheating: From the Classroom to the Exam Room. J Physician Assist Educ. , 17(1), pp. 23–9.
  • Ayob M, Malik A. 2011. A New Model for an Examination-Room Assignment Problem. J Comput Sci., 11(10):187–90.
  • Güler MG, Geçici E. 2020. A spreadsheet-based decision support system for examination timetabling. Turkish J Electr Eng Comput Sci., 28(3): pp. 1584–98.
  • Elsaka T. 2017. Autonomous Generation of Conflict-Free Examination Timetable Using Constraint Satisfaction Modelling. In: IntArtificial Intelligence and Data Processing Symposium (IDAP), p. 1–10.
  • Vasupongayya S, Noodam W, Kongyong P. 2013. Developing Examination Management System: Senior Capstone Project, a Case Study. Int J Comput Inf Eng., 7(7), pp. 1046–52.
  • Dammak A, Elloumi A, Kamoun H. 2006. Classroom assignment for exam timetabling. Adv Eng Softw., 37(10), pp. 659–66.
  • Mohmad Kahar MN, Kendall G. 2014. Universiti Malaysia Pahang examination timetabling problem: Scheduling invigilators, J Oper Res Soc., 65(2), pp-214–26.
  • Elloumi A, Kamoun H, Jarboui B, Dammak A. 2014. The classroom assignment problem: Complexity, size reduction and heuristics, Appl Soft Comput J., 14 (PART C), pp-677–86. DOI: 10.1016/j.asoc.2013.09.003
  • Burke EK, Mccollum B, Mcmullan P, Qu R. 2006. Examination Timetabling: A New Formulation, In Proceedings of the 6th International Conference on the Practice and Theory of Automated Timetabling, Brno, pp. 373-375.
  • Chaki PK. 2016. Algorithm For Efficient Seating Plan For Centralized Exam System, Int Conf Comput Tech Inf Commun Technol., pp. 320–5.
  • Inamdar A, Gangar A, Gupta A, Shrivastava V. 2018. Automatic Exam Seating & Teacher Duty Allocation System, Second Int Conf Inven Commun Comput Technol. (Icicct), pp. 1302–6.
  • Wang XF, Chen G. 2003. Complex networks: Small-world, scale-free and beyond, IEEE Circuits and Systems Magazine, 2003, 3(1), pp. 6–20.
  • Brailsford SC, Potts CN, Smith BM. 1999. Constraint satisfaction problems: Algorithms and applications, European Journal of Operational Research, Vol. 119, pp. 557–81.
  • Hosny M, Fatima S. 2011. A Survey of Genetic Algorithms for the University Timetabling Problem A Survey of Genetic Algorithms for the University Timetabling Problem Manar Hosny and Shameem Fatima, Int Conf Future Inf Technol IPCSIT 13., pp. 34–9.
  • Abdelhalim EA, Khayat GA El. 2016. A Utilization-based Genetic Algorithm for Solving the University Timetabling Problem ( UGA ), Alexandria Eng J, 55(2), pp.1395–409. DOI: 10.1016/j.aej.2016.02.017
  • Karova M. 2004. Solving Timetabling Problems Using Genetic Algorithms, 2Th Int’l Spring Seminar on Electronics Technology, pp. 20–2.
  • Rozaimee A, Shafee AN, Anissa N, Hadi A, Mohamed MA. 2017. A Framework for University’s Final Exam Timetable Allocation Using Genetic Algorithm, World Appl Sci J., 35(7), pp. 1210–5.
  • Pillay N, Banzhaf W. 2010. An informed genetic algorithm for the examination timetabling problem, Appl Soft Comput J., 10(2), pp. 457–67.
  • Abdullah S, Alzaqebah M. 2013. A hybrid self-adaptive bees algorithm for examination timetabling problems, Appl Soft Comput J., 13(8), pp. 3608–20. DOI: 10.1016/j.asoc.2013.04.010
  • Holland JH. 1992. Adaption in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, MIT Press
  • Şen Z. 2004. Genetik Algoritmalar ve En İyileme Yöntemleri, İstanbul, Su Vakfı Yayınları
  • Karaboğa D. 2004. Yapay Zekâ Optimizasyon Algoritmaları, İstanbul, Atlas Yayınevi, pp. 75–112.
  • Negnevitsky M. 2005. Artificial intelligence: A guide to intelligent systems, Systems (2nd Edition), Pearson Education.
  • Tindell KW, Burns A, Wellings AJ. 1992. Allocating hard real-time tasks: An NP-Hard problem made easy, J Real-Time Syst., (4), p. 145.
  • Bulut F, Subaşı Ş. 2015. Best Seating Plan For Centeral Exams Using Genetic Algorihtms. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Derg., 17(51), pp. 122–37.
  • Beligiannis GN, Moschopoulos C, Likothanassis SD. 2009. A genetic algorithm approach to school timetabling, J Oper Res Soc., 60(1), pp. 23–42.
  • Akkan C, Gülcü A. 2018. Computers and Operations Research A bi-criteria hybrid Genetic Algorithm with robustness objective for the course timetabling problem, Comput Oper Res., 90:pp. 22–32. DOI:10.1016/j.cor.2017.09.007
  • Soremekun, G., Gürdal, Z., Haftka, R. T., & Watson, L. T. 2001. Composite laminate design optimization by genetic algorithm with generalized elitist selection. Computers & structures, 79(2), 131-143.
  • Ağalday, M. F. 2018, Genetik algoritma ile merkezi sınavlarda tek ve çok boyutlu yakınlığa göre en iyi oturum planının oluşturulması (Master's thesis, Fatih Sultan Mehmet Vakıf Üniversitesi, Mühendislik ve Fen Bilimleri Enstitüsü).
  • Nadeau C, Bengio Y. 2003. Inference for the Generalization Error. Mach Learn Norwell Kluwer Acad Publ, 52(3), pp. 307–13.
  • Schaffer JD. 1989. A study of control parameters affecting online performance of genetic algorithms for function optimization. In: 3rd International Conference on Genetic Algorithms. San Mateo, CA: Morgan Kaufmann, p. 51–60.
  • Aksu Ö. 2008. Yeni Bir Paralel Genetik Algoritma Modeli Ve Analog Devre Tasarımına Uygulanması Yüksek Lisans Tezi, Kayseri Erciyes Üniversitesi.
  • Grefenstette JJ. 1992. Genetic algorithms for changing environments. Parallel Probl Solving from Nat., 2, pp. 137–44.
There are 41 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Fatih Ağalday 0000-0002-2635-0661

Ali Nizam 0000-0002-5613-0686

Early Pub Date October 11, 2022
Publication Date December 31, 2022
Published in Issue Year 2022 Volume: 6 Issue: 2

Cite

APA Ağalday, F., & Nizam, A. (2022). Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization. Journal of Innovative Science and Engineering, 6(2), 220-232. https://doi.org/10.38088/jise.1006070
AMA Ağalday F, Nizam A. Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization. JISE. December 2022;6(2):220-232. doi:10.38088/jise.1006070
Chicago Ağalday, Fatih, and Ali Nizam. “Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization”. Journal of Innovative Science and Engineering 6, no. 2 (December 2022): 220-32. https://doi.org/10.38088/jise.1006070.
EndNote Ağalday F, Nizam A (December 1, 2022) Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization. Journal of Innovative Science and Engineering 6 2 220–232.
IEEE F. Ağalday and A. Nizam, “Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization”, JISE, vol. 6, no. 2, pp. 220–232, 2022, doi: 10.38088/jise.1006070.
ISNAD Ağalday, Fatih - Nizam, Ali. “Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization”. Journal of Innovative Science and Engineering 6/2 (December2022), 220-232. https://doi.org/10.38088/jise.1006070.
JAMA Ağalday F, Nizam A. Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization. JISE. 2022;6:220–232.
MLA Ağalday, Fatih and Ali Nizam. “Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization”. Journal of Innovative Science and Engineering, vol. 6, no. 2, 2022, pp. 220-32, doi:10.38088/jise.1006070.
Vancouver Ağalday F, Nizam A. Performance Improvement of Genetic Algorithm Based Exam Seating Solution by Parameter Optimization. JISE. 2022;6(2):220-32.
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