Publication Type : Conference Proceedings
Publisher : New Trends in Computational Vision and Bio-inspired Computing: Selected works presented at the ICCVBIC 2018,
Source : New Trends in Computational Vision and Bio-inspired Computing: Selected works presented at the ICCVBIC 2018, Coimbatore, India, Springer International Publishing, Cham, p.149 - 160 (2020)
Url : https://doi.org/10.1007/978-3-030-41862-5_14
ISBN : 9783030418625
Campus : Coimbatore
School : School of Engineering
Department : Computer Science
Year : 2020
Abstract : Applying the popularly known technologies to solve real world problems are common practice among student researcher community, as it brings deeper understanding of the underlying technology for its further study and improvement. This paper aims at applying the Genetic Algorithm (GA) to solve the course allocation problem of educational institutions. The course allocation problem comprises of p number choices given by n numbers of students for m number of courses. Assigning the maximum number of students with their first or second choice of their courses is a cumbersome task. It is a typical optimization problem, which can be solved in ease by the Evolutionary Algorithms (EAs) such as GA. This paper proposes an automated system which uses GA (with five different crossover operators and three different mutation operators) to solve the course allocation system. A comparative study on the results obtained for different crossover operators is performed. The obtained results are verified with a real time data set collected from our University and validated the superiority of the proposed system.
Cite this Research Publication : S. Abhishek, Emmanuel, S. Coreya, Rajeshwar, G., and Dr. Jeyakumar G., “A Genetic Algorithm Based System with Different Crossover Operators for Solving the Course Allocation Problem of Universities”, New Trends in Computational Vision and Bio-inspired Computing: Selected works presented at the ICCVBIC 2018, Coimbatore, India. Springer International Publishing, Cham, pp. 149 - 160, 2020.