Balancing academic curricula by using a mutation-only genetic algorithm

In universities, the academic programs are organized in a number of periods, usually in six or ten semesters, for a bachelor or a master degree, respectively. It usually happens that a given semester is much loaded with courses than the others. This makes it hard for the students to comprehend and deal with a high volume of learning material per certain semesters. This problem is difficult, because some courses have prerequisites (e.g. Math2 should be taught after Math1), and this means that course correlation mast be taken into account. Therefore, in this paper, we present an intelligent method that is based on genetic algorithms to optimize the academic curricula of a given program, by trying to dispatch the courses over the available semesters, so that the load of individual semesters, in terms of course credits, is balanced as much as possible. The proposed genetic algorithm explores the search space by means of two mutation operators, which swap or shift courses between the semesters. The algorithm performance is fine-tuned and evaluated by using three state of art instances from the literature. The results show that the proposed algorithm is comparable with the state of the art solutions for the problem at hand.