CFP last date
22 July 2024
Reseach Article

Performance Analysis of Diversity Measure with Crossover Operators in Genetic Algorithm

by M.Nandhini, S.Kanmani, S.Anandan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 19 - Number 2
Year of Publication: 2011
Authors: M.Nandhini, S.Kanmani, S.Anandan

M.Nandhini, S.Kanmani, S.Anandan . Performance Analysis of Diversity Measure with Crossover Operators in Genetic Algorithm. International Journal of Computer Applications. 19, 2 ( April 2011), 19-26. DOI=10.5120/2334-3039

@article{ 10.5120/2334-3039,
author = { M.Nandhini, S.Kanmani, S.Anandan },
title = { Performance Analysis of Diversity Measure with Crossover Operators in Genetic Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { April 2011 },
volume = { 19 },
number = { 2 },
month = { April },
year = { 2011 },
issn = { 0975-8887 },
pages = { 19-26 },
numpages = {9},
url = { },
doi = { 10.5120/2334-3039 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:05:57.200111+05:30
%A M.Nandhini
%A S.Kanmani
%A S.Anandan
%T Performance Analysis of Diversity Measure with Crossover Operators in Genetic Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 19
%N 2
%P 19-26
%D 2011
%I Foundation of Computer Science (FCS), NY, USA

The goal of np-hard Combinatorial Optimization is finding the best possible solution from the set of feasible solutions. In this paper, we establish an approach using genetic algorithm with various selection and crossover operators with repair function for an institute course timetabling problem. It employs a constructive heuristic approach to find the feasible timetable, fitness value calculation, selection operators, crossover operators and repair function. The performance of proposed and existing selection and crossover operators are compared and shown by keeping diversity in the fitness value of population.

  1. Abdelaziz Dammak, Abdelkarim Elloumi and Hichem Kamoun. 2009. Lecture timetabling at a Tunisian university, InderScience. Int. J. of Operational Research.4(3) (2009), 323 - 345.
  2. Ali K. Kamrani and Ricardo Gonzalez. 2008. Genetic algorithms-based solution approach to combinatorial optimisation problems. Inderscience, Int. J. Knowledge Management Studies, 2(4) (2008) , 499 - 518.
  3. Brailsford, C., Potts, C. N., and Smith, B. M. 1999. Constraint satisfaction problems: Algorithms and applications. European J. Operational Research. 119(1999),557–581.
  4. Edmund K. Burke, Jakub Mareček, Andrew J. Parkes, and Hana Rudova. 2010. Decomposition, reformulation, and diving in university course timetabling . Computers & Operations Research, 37(3) (2010), 582-597.
  5. Ghaemi, S., Vakili, M.T., Aghagolzadeh. A. 2007. Using a genetic algorithm optimizer tool to solve university timetable scheduling problem. In Proceedings of IEEE 9th Int. Symposium on Signal Processing and its Applications(2007),1 – 4.
  6. Kremena Royachka and Milena Karova. 2006. High-Performance Optimization of Genetic Algorithms. In Proceedings of IEEE Int. Spring Seminar on Electronics Technology(2006) ,395-400.
  7. Nguyen Due Thanh. 2007. Solving Timetabling Problem Using Genetic and Heuristic Algorithms. In Proceedings of 8th ACIS Int. Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 3(2007),472 – 477.
  8. Panagiotis Adamidis and Panagiotis Arapakis. 1999. Evolutionary Algorithms in Lecture Timetabling . In Proceedings of Congress on Evolutionary Computation , 2(1999),1145-1151.
  9. Rhydian Lewis , Ben Paechter. 2007. Finding feasible timetables using group-based operators. IEEE Transactions on Evolutionary Computation, 11(3)(2007),397-413.
  10. Salwani Abdullah, Edmund K. Burke, and Barry McCollum. 2007. A hybrid evolutionary approach to the University Course Timetabling Problem. In Proceedings of IEEE Congress on Evolutionary Computation (CEC 2007), 1764-1768.
  11. Salwani Abdullah and Hamza Turabieh. 2008. Generating University Course Timetable Using Genetic Algorithms and Local Search. In Proceedings of IEEE 3rd Int. Conference on Convergence and Hybrid Information Technology, (2008), 254-260.
  12. Sivanandham, S. N., and Deepa, S. N. 2008. Introduction to Genetic Algorithm , Springer .
  13. Wang Xiao Yun , Wang Feng Kun and Wang Xiang Yun. 2008. Optimize Timetabling Problem Using Improved Genetic Algorithm. In Proceedings of IEEE Workshop on Knowledge Acquiring Modeling, (2008), 260-262.
  14. Wutthipong Chinnasri, Nidapan Sureerattanan .2010. Comparison of Performance Between Different Selection Strategies on Genetic Algorithm with Course Timetabling Problem. In Proceedings of Int. Conference on Advanced Management Science, (2010),105-108.
  15. Yu Zheng, Jing-fa Liu, Wue-hua Geng and Jing-yu Yang, 2009. Quantum-Inspired Genetic Evolutionary Algorithm for Course Timetabling. In Proceedings of 3rd Int. Conference on Genetic and Evolutionary Computing,750-753.
Index Terms

Computer Science
Information Sciences


Course timetabling fitness selection crossover repair optimal solution