Author
Sanchez-Anguix, V., Chalumuri, R., Aydoğan, Reyhan, Julian, V.
Publication Date
2019-03
Publication Place
-
Elsevier
Subject
Genetic algorithms, Student-project allocation, Matching, Pareto optimal, Artificial intelligence
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1568-4946
Record ID
3f6ed081-8ba5-4032-afb1-1a8808796ad1
Library Location
Computer Science
Date
2019-03
Notes
Faculty of Engineering and Computing at Coventry University, United Kingdom ; European Commission Joint Research Centre
Sample Text
The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors' preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the studentsupervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time.
DOI
10.1016/j.asoc.2018.11.049
Cilt
76
