International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 8, No. 1, 8 Jan. 2016
Cover page and Table of Contents: PDF (size: 714KB)
A Genetic Approach Based Solution for Seat Allocation during Counseling for Engineering Courses
Full Text (PDF, 714KB), PP.29-36
Views: 0 Downloads: 0
Author(s)
Ashwani Chandel
1,*
Manu Sood
1
Index Terms
Genetic algorithm, seat allocation, Fitness cost, Mutation, Crossover, Population, Chromosomes
Abstract
Genetic Algorithm (GA) is one of the most popular optimization solutions for scheduling problems and has already been used to implement variety of applications. In this paper, we describe a heavily constrained seat allocation problem experienced during counseling for seat allocation in college/universities based upon the merit of students computed on the basis of an entrance test. Manual process of allocating seats is not just inconvenient but proves expensive in terms of time and money. The application of GA involves using selection, crossover or mutation operators applied to populations of chromosomes. We propose a powerful technique using genetic algorithm (GA) in scheduling as a potential solution to the seat allocation process which has been supported with the help of an illustrative example.
Cite This Paper
Ashwani Chandel, Manu Sood, "A Genetic Approach Based Solution for Seat Allocation during Counseling for Engineering Courses", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.8, No.1, pp.29-36, 2016. DOI:10.5815/ijieeb.2016.01.04
Reference
[1]Evan, S. Itai, A.Shamir, “On the Complexity and Multicommodity Flow Problems”, SIAM Journal of Computing 1976, vol. 5, no.4, pp. 691-703.
[2]Y.Ichikawa &Y.Ishii, “Retaining diversity of genetic algorithms for multivariable optimization and neural network learning”, IEEE International Conference on Neural Networks 1993,vol.2, pp. 110-114.
[3]A. Thengade & R. Dondal, “Genetic Algorithm- Survey Paper”, IJCA-2012 pp25-29.
[4]A.Chandel & M.Sood, “Searching and Optimization Techniques in Artificial Intelligence: A Comparative Analysis & Complexity Analysis”, vol-3, issue-3, IJARCET -2014, pp 866-871.
[5]Filho, G. R. & Lovena, L. A. N. (2000). “A Constructive Evolutionary Approach To School Timetabling”,www.lac.inpe.br/~marcos/arsig2/CGA-timet-EVOCOP.pdf
[6]D. Stefano & A.Tettamanzi, “An Evolutionary Algorithm for solving the School Time-Tabling Problem”, Evo: Workshops 2001, pp. 452-462.
[7]Melicio, F.Caldeira, J.Ruso, “Two Neighborhoods Approaches to the Timetabling Problem.” Proceedings of the 5th International Conference on the Practice and Theory of Automated Timetabling-2004.
[8]Gy’ori, S. Petres, Z. & Varkonyi-Koczy, “Genetic Algorithms in Timetabling. A New Approach”, MFT Periodika Hungama Society of IFSA-2001. http://www.mft.hu/hallg/200107.pdf.
[9]M.Davis, L. Steenstrup, “Genetic Algorithms and Simulated Annealing: An Overview”, Morgan Kaufmann Publishers Inc., Los Altos, pp- 1-11.
[10]A. Sahu & R.Tapadar, “Solving the Assignment problem using Genetic Algorithm and Simulated Annealing”, IAENG International Journal of Applied Mathematics, IJAM-2007, pp 1-7.
[11]A.Ajami, J.Wright, “Selecting the Most Efficient Genetic Algorithm Sets in Solving Unconstrained Building Optimization Problem”, vol.-3, issue1, IJSBE-2014, pp 18-26.
[12]R. Kumar, G.Gopal & R.Kumar, “Hybridization in Genetic Algorithms, “vol-3, issue-2, IJARCSSE-2011, pp252-258.
[13]K.Matous, M.Leps, J.Zeman & M.Sejhohl, “Applying Genetic Algorithm to Selected Topics Commonly Encountered in Engineering Practise”, Comp. Methods Appl. Mech. Engg., Elsevier Publication -2000, pp-1629-1630.
[14]R.Garg & S.Mittal, “Optimization by Genetic Algorithm” vol-4, issue-4, IJARCSSE-2014, pp-587-589.
[15]P.Yadav & N. Prajapati, “An Overview of Genetic Algorithm and Modeling”, vol-2, issue-9, IJSRP-2012, pp-1-4.