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dc.contributor.authorChakradhar, M.-
dc.contributor.authorCharan, M.S.-
dc.contributor.authorSai, R.U.-
dc.contributor.authorKunal, M.-
dc.contributor.authorMurthy, Y.V.S.-
dc.contributor.authorKoolagudi, S.G.-
dc.identifier.citation2019 10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, 2019, Vol., , pp.-en_US
dc.description.abstractIn the paper, we propose an algorithm using genetic alogithm to find out the optimal solution for the academic load balancing problem. The load balancing problem is to optimize the load of credits per semester in an academic curriculum. In the proposed method, we try to distribute the course load as evenly as possible so that the deviation from the mean credit load per each semester is as minimal as possible. The objective function is to distribute the credit load among all the semesters evenly such that the deviation from the mean credits per semester is minimal. The proposed approach explores the solution space using only mutation operators and does not operate using crossover as the solutions obtained using cross over does not create any newer and better solutions in the solution space.The algorithm is applied on three data sets and the results are compared with the solutions obtained using the existing approaches. The results obtained using the state of the art solution are either better than approaches or on par with the state of art optimal solutions. The solution set obtained using the proposed approach is well spread out through out all the periods and all the periods contain almost mean number of credits. � 2019 IEEE.en_US
dc.titleAcademic Curriculum Load Balancing using GAen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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