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All Journal Jurnal SOLMA Jambura Journal of Electrical and Electronics Engineering Fidelity : Jurnal Teknik Elektro JURNAL TEKNOLOGI INFORMASI & KOMUNIKASI DALAM PENDIDIKAN Journal of Electrical Vocational Teacher Education JIMT: Jurnal Informatika, Multimedia dan Teknik
Arwadi Sinuraya
Universitas Negeri Medan
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Other

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The Influence of Population Size on the Computational Time of Genetic Algorithms in Course Scheduling Salman, Rudi; Sinuraya, Arwadi; Irfandi, Irfandi; Eswanto, Eswanto; Rahman, Sayuti; Herdianto, Herdianto; Hutajulu, Olnes Yosefa; Halawa, Agung Y S
Jambura Journal of Electrical and Electronics Engineering Vol 8, No 1 (2026): Januari - Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v8i1.33508

Abstract

Course scheduling is a complex problem in higher education because it must satisfy multiple constraints involving courses, instructors, rooms, and time slots. This study examines the impact of population size variation on the computational efficiency of a Genetic Algorithm (GA) applied to a medium-scale instance consisting of 35 courses, 15 instructors, 12 rooms, and 20 time slots. Simulations were conducted in MATLAB using population sizes ranging from 20 to 1000, while all other GA parameters were held constant to isolate the effect of population size. Solution quality was evaluated using a conflict-based fitness function, and all configurations yielded valid timetables with zero hard-constraint violations. Experimental results reveal a consistent non-linear relationship between population size and computation time. Statistical findings in Table 1—including mean values, standard deviations, and 95% confidence intervals—show that both very small and very large populations produce higher and more variable execution times. In contrast, population sizes of 300–400 achieve the lowest and most stable computation times, indicated by the smallest mean values and narrow confidence intervals. For the instance and configuration used in this study, this range serves as an effective starting point for population size tuning. Overall, the findings highlight the importance of empirical parameter selection to balance computational efficiency and solution quality in academic timetabling systems.
Co-Authors Afrian, T Agus, Junaidi ALEXCANDRA, HISAR Azizi Lubis, Reza Arbi Bakti Dwi Waluyo Denny Haryanto Sinaga Dinan Roy Sandi Siburian Eswanto, Eswanto Gaol, Goklas Lumban Halawa, Agung Y S Handrea Bernando Tambunan Herdianto Herdianto, Herdianto Hutajulu, Olnes Yosefa Hutasoit, Ungkap Swandi Triswanto Irfandi Irfandi, Irfandi Kabul, Abdul Hamid Lubis, Aulia Madjid Lubis, Muhammad Fiqra Rijali Manullang, Jongga Marbun, Raymon Kristovel Muhammad Ashari Muhammad Aulia Rahman Sembiring Nababan, Puji Mory Olnes Y. Hutajulu Padang, Roni Bonatua Purba, Hengki Rahman Sembiring, Muhammad Aulia Rahman, Sayuti Rahmaniar Rahmaniar, Rahmaniar Rudi Salman Rudi Salman Sembiring, Muhammad Aulia Rahman SINAGA, DESMAN JONTO Suprapto Suprapto Zubir, Moondra