TY  - BOOK
AU  - Bautista,Shaira F.
AU  - Besa,Kelsey Eunice T.
AU  - Polvoreza,Jireh Arjie C.
TI  - Optimizing CSPC event scheduling using a genetic algorithm
N1  - Undergraduate Thesis (BSIT) - Information Technology; Include bibliographic references
N2  - College event scheduling poses a significant challenge due to the diverse nature of events in terms of size, style, and context. These differences often lead to disagreements and inefficiencies, creating disruptions for students and staff alike. This research aims to address this challenge by exploring the feasibility of using a Genetic Algorithm to optimize event scheduling at Camarines Sur Polytechnic Colleges. Data on various event details, such as the number of events, their durations, and the number of attendees, was collected and used to train the algorithm, which was implemented in Python. The performance of the Genetic Algorithm was evaluated by analyzing its space and time complexity under different population and generation sizes. The results indicated that a larger generation size improved scheduling optimization, resulting in reduced CPU utilization and conflicts. The prototype developed as part of this research automates the scheduling process, significantly reducing the need for manual work. Further research is recommended to adapt the algorithm for other types of events, ensuring it meets their distinct requirements. Overall, this study contributes to the efficient and effective scheduling of events in educational institutions
ER  -