A genetic algorithm for allocating project supervisors to students

Abstract

Research projects are graduation requirements for many university students. If students are arbitrarily assigned project supervisors without factoring in the students' preferences, they may be allocated supervisors whose research interests differ from theirs or whom they just do not enjoy working with. In this paper we present a genetic algorithm (GA) for assigning project supervisors to students taking into account the students' preferences for lecturers as well as lecturers' capacities. Our work differs from several existing ones which tackle the student project allocation (SPA) problem. SPA is concerned with assigning research projects to students (and sometimes lecturers), while our work focuses on assigning supervisors to students. The advantage of the latter over the former is that it does not require projects to be available at the time of assignment, thus allowing the students to discuss their own project ideas/topics with supervisors after the allocation. Experimental results show that our approach outperforms GAs that utilize standard selection and crossover operations. Our GA also compares favorably to an optimal integer programming approach and has the added advantage of producing multiple good allocations, which can be discussed in order to adopt a final allocation.