Class Schedule Generation using Evolutionary Algorithms

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 prepar...

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Published in:Journal of physics. Conference series Vol. 1950; no. 1; pp. 12067 - 12074
Main Authors: Kakkar, Mohit Kumar, Singla, Jajji, Garg, Neha, Gupta, Gourav, Srivastava, Prateek, Kumar, Ajay
Format: Journal Article
Language:English
Published: Bristol IOP Publishing 01.08.2021
Subjects:
Curricula
Evolutionary algorithms
Genetic algorithms
Schedules
Timetables
ISSN:1742-6588, 1742-6596
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Abstract 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.
AbstractList 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.
Author Garg, Neha
Kakkar, Mohit Kumar
Srivastava, Prateek
Gupta, Gourav
Singla, Jajji
Kumar, Ajay
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10.1093/comjnl/10.1.85
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SubjectTerms Curricula
Evolutionary algorithms
Genetic algorithms
Schedules
Timetables
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