Open AccessClass Schedule Generation using Evolutionary Algorithms
Author(s) -
Mohit Kumar Kakkar,
Jajji Singla,
Neha Garg,
Gourav Gupta,
Prateek Srivastava,
Ajay Kumar
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1950/1/012067
Subject(s) - schedule , memetic algorithm , computer science , convergence (economics) , genetic algorithm , mathematical optimization , scheme (mathematics) , evolutionary algorithm , class (philosophy) , chromosome , institution , algorithm , artificial intelligence , machine learning , mathematics , mathematical analysis , biochemistry , chemistry , political science , law , economics , gene , economic growth , operating system
Timetabling problem is known as an NP-hard problem that centres around finding an optimized allocation of subjects onto a finite available number of slots and spaces. It is perhaps the most challenging issues looked by colleges around the globe. Every academic institution faces a problem when preparing courses and exam plans. There are many restrictions raised while preparing a timetable. This paper proposed a method based on the evolutionary algorithms to solve the constrained timetable problem, which helps to create theory as well as lab schedule for universities. A smart adaptive mutation scheme is used to speed up convergence and chromosome format is also problem specific. Here in this paper two algorithms are compared in respect of Timetabling problems. Using GA (Genetic Algorithm) and MA (Memetic algorithm), we optimised the output by selecting the best solution from the available options to present a comprehensive curriculum system.