Intelligence Classification of the Timetable Problem: A Memetic Approach
International Journal on Data Science and Technology
Volume 3, Issue 2, March 2017, Pages: 24-33
Received: Apr. 5, 2017; Accepted: May 15, 2017; Published: Jul. 27, 2017
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Authors
Eboka Andrew Okonji, Department of Computer Education, Federal College of Education Technical, Asaba, Nigeria
Yerokun Mary Oluwatoyin, Department of Computer Education, Federal College of Education Technical, Asaba, Nigeria
Okoba Ifeoma Patricia, Department of Computer Education, Federal College of Education Technical, Asaba, Nigeria
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Abstract
Scheduling tasks persist in our daily functioning and as academia, abounds more in University circle as hard-NP constraint satisfaction tasks. Many studies exist with the objective of resolving the many conflicted constraints that exists in a timetable schedule using various algorithms. Many of such algorithms simply search the domain space for a goal state that satisfies the problem constraints. Our study yields an outcome assignment that provides a complete, feasible and optimal academic schedule that satisfies medium cum hard constraints for the Federal University of Petroleum Resource Effurun in Delta State of Nigeria using memetic algorithm. Results showed that model converges after 4minutes and 29seconds; while its convergence time depends on use of belief space to ensure agents do not violate model bounds, encoding scheme used amongst others. The schedule considers both instructors and students’ preference as medium constraints of high priority.
Keywords
Fitness, Constraints, Cross-Over, Mutation, Timetable, Memetic Algorithm, Intelligent, Schedule
To cite this article
Eboka Andrew Okonji, Yerokun Mary Oluwatoyin, Okoba Ifeoma Patricia, Intelligence Classification of the Timetable Problem: A Memetic Approach, International Journal on Data Science and Technology. Vol. 3, No. 2, 2017, pp. 24-33. doi: 10.11648/j.ijdst.20170302.12
Copyright
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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