نویسنده
Sanchez-Anguix, V., Chalumuri, R., Aydoğan, Reyhan, Julian, V.
تاریخ انتشار
2019-03
محل انتشار
-
Elsevier
موضوع
Genetic algorithms, Student-project allocation, Matching, Pareto optimal, Artificial intelligence
نوع
دوره ای
زبان
انگلیسی
دیجیتال
بله
نسخه خطی
خیر
کتابخانه
دانشگاه اوزیغین
شناسه دارایی کتابخانه
1568-4946
شماره ثبت
3f6ed081-8ba5-4032-afb1-1a8808796ad1
محل کتابخانه
Computer Science
تاریخ
2019-03
یادداشتها
Faculty of Engineering and Computing at Coventry University, United Kingdom ; European Commission Joint Research Centre
متن نمونه
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
