Genetic Algorithm for Teaching Distribution based on Lecturers’ Expertise
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Genetic Algorithm for Teaching Distribution based on Lecturers’ Expertise

Rizki Agung Pambudi, Wahyuni Lubis, Firhad Rinaldi Saputra, Hanif Prasetyo Maulidina, Vivi Nur Wijayaningrum


The teaching distribution for lecturers based on their expertise is very important in the teaching and learning process. Lecturers who teach a course that is in accordance with their interests and abilities will make it easier for them to deliver material in class. In addition, students will also be easier to accept the material presented. However, in reality, the teaching distribution is often not in accordance with the expertise of the lecturer so that the lecturers are not optimal in providing material to their students. This problem can be solved using optimization methods such as the genetic algorithm. This study offers a solution for teaching distribution that focuses on the interest of each lecturer by considering the order of priorities. The optimal parameters of the test results are crossover rate (cr) = 0.6, mutation rate (mr) = 0.4, number of generations = 40, and population size = 15. Genetic algorithm is proven to be able to produce teaching distribution solutions with a relatively high fitness value at 4903.3.


genetic algorithm, optimization, scheduling, teaching distribution

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[1] R. M. Felder and E. R. Henriques, “Learning and Teaching Styles In Foreign and Second Language Education,” Foreign Lang. Ann., vol. 28, no. 1, pp. 21–31, 1995.

[2] A. F. Grasha, Teaching With Style: A Practical Guide to Enhancing Learning by Understanding Teaching and Learning Styles. Pittsburgh: Alliance Publishers, 1996.

[3] M. A. Mawoli and A. Y. Babandako, “An Evaluation of Staff Motivation, Dissatisfaction and Job Performance in an Academic Setting,” Aust. J. Bus. Manag. Res., vol. 1, no. 9, pp. 1–13, 2011.

[4] A. R. Komijan and M. N. Koupaei, “A Mathematical Model for University Course Scheduling: A Case Study,” Int. J. Tech. Res. Appl., no. 19, pp. 20–25, 2017.

[5] N. A. H. Aizam and C. Y. Liong, “Mathematical Modelling of University Timetabling : A Mathematical Programming Approach,” Int. J. Appl. Math. Stat., vol. 37, no. 7, pp. 110–122, 2013.

[6] L. Urbanucci, “Limits and Potentials of Mixed Integer Linear Programming Methods for Optimization of Polygeneration Energy Systems,” Energy Procedia, vol. 148, pp. 1199–1205, 2018.

[7] R. Ganguli and S. Roy, “A Study on Course Timetable Scheduling using Graph Coloring Approach,” Int. J. Comput. Appl. Math., vol. 12, no. 2, pp. 469–485, 2017.

[8] M. A. Al-Betar and A. T. Khader, “A Harmony Search Algorithm for University Course Timetabling,” Ann. Oper. Res., vol. 194, no. 1, pp. 3–31, 2012.

[9] P. Gore, P. Sonawane, and S. Potdar, “Timetable Generation Using Ant Colony Optimization Algorithm,” Int. J. Innov. Res. Comput. Commun. Eng., vol. 5, no. 3, pp. 6033–6039, 2017.

[10] A. N. Syahputra, A. S. Kholimi, and L. Husniah, “Genetic Algorithm Application for Navigation System on Virtual Shop,” KINETIK, vol. 3, no. 4, pp. 311–318, 2018.

[11] Q. Kotimah, W. F. Mahmudy, and V. N. Wijayaningrum, “Optimization of Fuzzy Tsukamoto Membership Function using Genetic Algorithm to Determine the River Water,” Int. J. Electr. Comput. Eng., vol. 7, no. 5, pp. 2838–2846, 2017.

[12] X.-S. Yang, Nature-Inspired Metaheuristic Algorithms, Second Edi. Luniver Press, 2010.

[13] V. N. Wijayaningrum and W. F. Mahmudy, “Fodder composition optimization using modified genetic algorithm,” Indones. J. Electr. Eng. Informatics, vol. 7, no. 1, pp. 67–74, 2019.

[14] S. Malik and S. Wadhwa, “Preventing Premature Convergence in Genetic Algorithm Using DGCA and Elitist Technique,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 4, no. 6, pp. 410–418, 2014.

[15] H. A. Taha, Operations Research: An Introduction, 8th Edi. New Jersey: Prentice Hall, 2007.

[16] A. Bedboudi, C. Bouras, and M. T. Kimour, “An Heterogeneous Population-Based Genetic Algorithm for Data Clustering,” Indones. J. Electr. Eng. Informatics, vol. 5, no. 3, pp. 275–284, 2017.

[17] V. N. Wijayaningrum, W. F. Mahmudy, and M. H. Natsir, “Optimization of Poultry Feed Composition using Hybrid Adaptive Genetic Algorithm and Simulated Annealing,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 2–8, pp. 183–187, 2017.

[18] J. Magalhães-Mendes, “A Comparative Study of Crossover Operators for Genetic Algorithms to Solve the Job Shop Scheduling Problem,” WSEAS Trans. Comput., vol. 12, no. 4, pp. 164–173, 2013.

[19] M. Mitchell, An Introduction to Genetic Algorithms. Cambridge, USA: MIT Press, 1998.

[20] R. L. Haupt and S. E. Haupt, Practical Genetic Algorithms. New York, USA: John Willey & Sons, Inc., 2004.

[21] A. E. Eiben and J. E. Smith, Introduction to Evolutionary Computing, Second Edi. Springer, 2015.

[22] V. N. Wijayaningrum and W. F. Mahmudy, “Optimization of Ship’s Route Scheduling Using Genetic Algorithm,” Indones. J. Electr. Eng. Comput. Sci., vol. 2, no. 1, pp. 180–186, 2016.

[23] W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part II: Optimization,” Adv. Mater. Res., vol. 701, pp. 364–369, 2013.


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