Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm
University course timetabling problem (UCTP) is a scheduling problem that requires courses to be assigned to the limited time slots, classrooms, and lecturers, while adhering to a set of predefined constraints. Due to the effectiveness of genetic algorithm (GA) in optimisation problems, it has...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Year Project / Dissertation / Thesis |
| Published: |
2025
|
| Subjects: | |
| Online Access: | http://eprints.utar.edu.my/7101/1/fyp_CS_2025_FYH.pdf http://eprints.utar.edu.my/7101/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1854094473485615104 |
|---|---|
| author | Foo, Yao Heng |
| author_facet | Foo, Yao Heng |
| author_sort | Foo, Yao Heng |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | University course timetabling problem (UCTP) is a scheduling problem that requires courses
to be assigned to the limited time slots, classrooms, and lecturers, while adhering to a set of
predefined constraints. Due to the effectiveness of genetic algorithm (GA) in optimisation
problems, it has been widely discussed in numerous research to address UCTP. Nonetheless,
the performance of GA in terms of operation techniques has not been studied enough, as the
researchers have often focused on using a single GA combination or hybrid approaches to solve
UCTP case studies. Therefore, this project aims to analyse the performance of different
combinations of GA operation techniques and identify the best GA model. A flexible GA
framework is developed, which allows alternative techniques to be integrated and executed
easily. 64 combinations, involving 4 selection, 4 crossover, 1 mutation, and 4 replacement
techniques, are evaluated on a partial mock dataset. In addition, this project proposes a new
soft constraint, which requires consecutive classes for a student to be held in the same building.
This constraint targets to reduce students’ travel distance, thus producing a more student
friendly timetable. Experimental results shows that GA44 model which comprises of binary
tournament selection, uniform crossover, swap mutation, and weak chromosome replacement
is the best GA combination. In conclusion, the proposed constraint demonstrates clear benefits
to student experience on campus and offers a fresh idea for future research with alternative
approaches. |
| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7101 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2025 |
| record_format | eprints |
| spelling | my-utar-eprints.71012025-12-28T15:54:58Z Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm Foo, Yao Heng T Technology (General) University course timetabling problem (UCTP) is a scheduling problem that requires courses to be assigned to the limited time slots, classrooms, and lecturers, while adhering to a set of predefined constraints. Due to the effectiveness of genetic algorithm (GA) in optimisation problems, it has been widely discussed in numerous research to address UCTP. Nonetheless, the performance of GA in terms of operation techniques has not been studied enough, as the researchers have often focused on using a single GA combination or hybrid approaches to solve UCTP case studies. Therefore, this project aims to analyse the performance of different combinations of GA operation techniques and identify the best GA model. A flexible GA framework is developed, which allows alternative techniques to be integrated and executed easily. 64 combinations, involving 4 selection, 4 crossover, 1 mutation, and 4 replacement techniques, are evaluated on a partial mock dataset. In addition, this project proposes a new soft constraint, which requires consecutive classes for a student to be held in the same building. This constraint targets to reduce students’ travel distance, thus producing a more student friendly timetable. Experimental results shows that GA44 model which comprises of binary tournament selection, uniform crossover, swap mutation, and weak chromosome replacement is the best GA combination. In conclusion, the proposed constraint demonstrates clear benefits to student experience on campus and offers a fresh idea for future research with alternative approaches. 2025-06 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7101/1/fyp_CS_2025_FYH.pdf Foo, Yao Heng (2025) Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm. Final Year Project, UTAR. http://eprints.utar.edu.my/7101/ |
| spellingShingle | T Technology (General) Foo, Yao Heng Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm |
| title | Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm |
| title_full | Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm |
| title_fullStr | Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm |
| title_full_unstemmed | Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm |
| title_short | Evaluate the performance of university course timetabling problem with different combinations of genetic algorithm |
| title_sort | evaluate the performance of university course timetabling problem with different combinations of genetic algorithm |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/7101/1/fyp_CS_2025_FYH.pdf http://eprints.utar.edu.my/7101/ |
| url_provider | http://eprints.utar.edu.my |