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

Abstract

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.

Keywords

genetic algorithm, optimization, scheduling, teaching distribution

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References

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