We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Reseach Article

A New Biological Operator in Genetic Algorithm for Class Scheduling Problem

by R. Lakshmi, K. Vivekanandhan, R. Brintha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 12
Year of Publication: 2012
Authors: R. Lakshmi, K. Vivekanandhan, R. Brintha
10.5120/9742-4293

R. Lakshmi, K. Vivekanandhan, R. Brintha . A New Biological Operator in Genetic Algorithm for Class Scheduling Problem. International Journal of Computer Applications. 60, 12 ( December 2012), 6-11. DOI=10.5120/9742-4293

@article{ 10.5120/9742-4293,
author = { R. Lakshmi, K. Vivekanandhan, R. Brintha },
title = { A New Biological Operator in Genetic Algorithm for Class Scheduling Problem },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 12 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number12/9742-4293/ },
doi = { 10.5120/9742-4293 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:06:21.110038+05:30
%A R. Lakshmi
%A K. Vivekanandhan
%A R. Brintha
%T A New Biological Operator in Genetic Algorithm for Class Scheduling Problem
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 12
%P 6-11
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper describes an innovative approach to solve Class Scheduling problem which is a constraint combinatorial NP hard problem. From the wonders of natural evolution, an important phenomenon of RNA interference induced silencing complex (RISC) can be used as Interference Induced Silencing operator and it is incorporated into the Genetic Algorithm to solve any practical problems like Class Scheduling problem. The aim of this research is to create an automated system for class scheduling problem using Genetic Algorithm to the extent by a new biologically inspired operator, Interference Induced Silencing (IIS) operator that it can be used to set the instant specific preferences to generate the effective time table with the probabilistic operators like crossover and mutation. The framework of the fitness function has considered the hard constraints and the soft constraints. The results were proved to be efficient than the simple Genetic algorithm.

References
  1. Holland, A text book of "Adaptation in Natural and Artificial Systems, 1975.
  2. Hitoshi Kanoh and Yuusuke Sakamoto, "Interactive Timetabling System Using Knowledge-Based Genetic Algorithms"
  3. S. SivaSathya and S. Kuppuswami, "Gene Silencing for Course Time-Tabling with Genetic Algorithm ".
  4. Sanjay R. Sutar and Rajan S. Bichkar, "University Timetabling based on Hard Constraints using Genetic Algorithm ".
  5. Leon Bambrick, "Lecture Timetabling Using Genetic Algorithms".
  6. Mila S. de Magalh˜aes, Helio J. C. Barbosa, and Laurent E. Dardenn, "Selection-Insertion Schemes in Genetic algorithms for the Flexible Ligand Docking Problem".
  7. Przemyslaw Dymarski,2010," Hidden Markov Models, Theory and Applications".
  8. www. wikipaedia. com
  9. E. Aycan and T. Ayav,"Solving the Course Scheduling Problem Using Simulated Annealing"
  10. Rakesh Kumar and Jyotishree, "Blending Roulette Wheel Selection & Rank Selection in Genetic Algorithms".
  11. E. K. Burke, J. D. Landa Silva, E. Soubeiga, "Multi objective hyper-heuristic Approaches for space allocation and timetabling".
  12. G. M. White and P. W. Chan, "Towards the Construction of Optimal Examination Timetables," INFOR 17, 1979, p. p. 219-229.
  13. Davis L. (1991) "Handbook of Genetic Algorithms" Van Nostrand Reinhold
  14. N. D. Thanh, "Solving Timetabling Problem Using Genetic and Heuristic Algorithms", Eighth ACIS International Conference on Software Engg, Artificial Intelligence, Networking, and Parallel/Distributed Computing, hal. 472- 477, 2007.
  15. R. Raghavjee and N. Pillay, "Using Genetic Algorithms to Solve the South African School Timetabling Problem," Proceedings of Second World Congress on Nature and Biologically Inspired (NaBIC) (2010), pp. 286-292.
  16. O. T. Arogundade, A. T. Akinwale, and O. M. Aweda, "A Genetic Algorithm Approach for a Real-World University Examination Timetabling Problem, International Journal of Computer Applications, Vol. 12, no. 5 (2010), pp. 1-4.
  17. S. Abdullah and A. R . Hamdan, "A hybrid approach for university course timetabling," International Journal of Computer Science and Network Security,, vol. 8, no. 8, 2008.
  18. P. Ko stuch, "The university course timetabling problem with a three-phase approach. " International Conference on the Pra ctice and Theory of Automated Timetabling ( PATAT V), pp. 109–125, 2005.
  19. Rachel Nash and Tomas Lindahl, "DNA Ligases" Imperial Cancer Research Fund Clare Hall Laboratories, United Kingdom.
  20. Neema Agrawal, P. V. N. Dasaradhi, Asif Mohmmed, Pawan Malhotra, "RNA Interference: Biology, Mechanism, and Applications" , Microbiology and Molecular Biology reviews, Dec. 2003, p. 657–685.
  21. Chen, A text book of fundamentals of microbiology".
  22. George G. Mitchell Diarmuid O'Donoghue David Barnes Mark McCarville , "GeneRepair – Repair Operator for Genetic Algorithms ".
  23. Qingfu Zhang, Jianyong Sun, and Edward Tsang, "An Evolutionary Algorithm With Guided Mutation for the Maximum Clique Problem", IEEE transactions on evolutionary computation, vol. 9, no. 2, April 2005.
  24. Arthur L. Corcoran, Roger L. Wainwright, "Reducing disruption of superior building blocks in genetic algorithms", Proceedings of the, ACM SIGAPP Symposium on Applied Computing February, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Class Scheduling Problem Genetic Algorithm Interference Induced Silencing Operator Swap Mutation Preference Settings Hard Constraint and Soft Constraint