Yazar
Sanchez-Anguix, V., Chalumuri, R., Aydoğan, Reyhan, Julian, V.
Basım Tarihi
2019-03
Basım Yeri
-
Elsevier
Konu
Genetic algorithms, Student-project allocation, Matching, Pareto optimal, Artificial intelligence
Tür
Süreli Yayın
Dil
İngilizce
Dijital
Evet
Yazma
Hayır
Kütüphane
Özyeğin Üniversitesi
Demirbaş Numarası
1568-4946
Kayıt Numarası
3f6ed081-8ba5-4032-afb1-1a8808796ad1
Lokasyon
Computer Science
Tarih
2019-03
Notlar
Faculty of Engineering and Computing at Coventry University, United Kingdom ; European Commission Joint Research Centre
Örnek Metin
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
