%0 Conference Paper %1 adhikari2023evolutionary %A Adhikari, Saurav %A Khawas, Prasiddha B. %A Paudel, Rajeev %A Shakya, Aman %B Proceedings of the International Conference on Technologies for Computer, Electrical, Electronics & Communication (ICT-CEEL 2023) %D 2023 %E Maka, Sujan %E Duwal, Sunil %K myown %P 163-169 %T Evolutionary Computing for Academic Timetabling: A Genetic Algorithm Approach %U https://ictceel.khwopaconference.com/uploads/conferencepaper/fullpaper/1122.pdf %X The efficient allocation and scheduling of academic resources in universities have become critical operational challenges in today’s complex educational landscape. Traditional manual or rudimentary software-based approaches often result in suboptimal routines that can compromise the quality of education and strain resources. In response to the growing intricacy of academic institutions and scheduling requirements, this research paper introduces a novel approach to university and school scheduling using Genetic Algorithms (GAs). GAs, part of evolutionary algorithms, provide an automated and adaptable solution by mimicking processes of natural selection and genetics to find approximate solutions to optimization problems. This paper outlines the design and implementation of the GA-based scheduler, covering crucial components such as chromosomes, constraints, population, fitness function, selection, crossover, and mutation. A penalty-driven fitness function is employed to ensure conflict-free schedules while adhering to diverse constraints and preferences. Results from test cases demonstrate the algorithm’s effectiveness in generating optimized routines, with the average fitness value steadily decreasing over generations. The schedules produced are logical, reflecting a wellstructured flow and adherence to specific requirements. The research emphasizes the significance of the genetic algorithm approach in handling complex combinatorial scheduling problems. The proposed methodology provides an efficient and effective means to optimize academic schedules, offering a promising solution for universities seeking to enhance their scheduling processes. Future work includes introducing soft constraints, exploring adaptive parameter adjustments, and investigating hybrid approaches to further improve the algorithm’s performance and solution quality.

@inproceedings{adhikari2023evolutionary, abstract = {The efficient allocation and scheduling of academic resources in universities have become critical operational challenges in today’s complex educational landscape. Traditional manual or rudimentary software-based approaches often result in suboptimal routines that can compromise the quality of education and strain resources. In response to the growing intricacy of academic institutions and scheduling requirements, this research paper introduces a novel approach to university and school scheduling using Genetic Algorithms (GAs). GAs, part of evolutionary algorithms, provide an automated and adaptable solution by mimicking processes of natural selection and genetics to find approximate solutions to optimization problems. This paper outlines the design and implementation of the GA-based scheduler, covering crucial components such as chromosomes, constraints, population, fitness function, selection, crossover, and mutation. A penalty-driven fitness function is employed to ensure conflict-free schedules while adhering to diverse constraints and preferences. Results from test cases demonstrate the algorithm’s effectiveness in generating optimized routines, with the average fitness value steadily decreasing over generations. The schedules produced are logical, reflecting a wellstructured flow and adherence to specific requirements. The research emphasizes the significance of the genetic algorithm approach in handling complex combinatorial scheduling problems. The proposed methodology provides an efficient and effective means to optimize academic schedules, offering a promising solution for universities seeking to enhance their scheduling processes. Future work includes introducing soft constraints, exploring adaptive parameter adjustments, and investigating hybrid approaches to further improve the algorithm’s performance and solution quality.}, added-at = {2023-10-13T12:26:58.000+0200}, author = {Adhikari, Saurav and Khawas, Prasiddha B. and Paudel, Rajeev and Shakya, Aman}, biburl = {https://www.bibsonomy.org/bibtex/219ad5f54b1f6735f119d9073ddad28cc/amanshakya}, booktitle = {Proceedings of the International Conference on Technologies for Computer, Electrical, Electronics & Communication (ICT-CEEL 2023)}, editor = {Maka, Sujan and Duwal, Sunil}, eventdate = {11-13 October 2023}, eventtitle = {International Conference on Technologies for Computer, Electrical, Electronics & Communication (ICT-CEEL 2023)}, interhash = {949b7b7a599bc80f898be860ee5c5106}, intrahash = {19ad5f54b1f6735f119d9073ddad28cc}, keywords = {myown}, month = {October}, pages = {163-169}, timestamp = {2024-04-11T19:11:43.000+0200}, title = {Evolutionary Computing for Academic Timetabling: A Genetic Algorithm Approach}, url = {https://ictceel.khwopaconference.com/uploads/conferencepaper/fullpaper/1122.pdf}, venue = {Bhaktapur, Nepal}, year = 2023 }