Main Article Content


Proportional tuition fees assessment is an optimization process to find a compromise point between student willingness to pay and institution income. Using a genetic algorithm to find optimal solutions requires effective chromosome representations, parameters, and operator genetic to obtain efficient search. This paper proposes a new chromosome representation and also finding efficient genetic parameters to solve the proportional tuition fees assessment problem. The results of applying the new chromosome representation are compared with another chromosome representation in the previous study. The evaluations show that the proposed chromosome representation obtains better results than the other in both execution time required and the quality of the solutions.

Article Details

How to Cite
Jauhari, F., Mahmudy, W. F., & Basuki, A. (2019). An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II. Journal of Information Technology and Computer Science, 4(3), 291–298.


  1. MENRISTEKDIKTI Republik Indonesia, Peraturan Menteri Riset, Teknologi, dan Pendidikan Tinggi Republik Indonesia Nomor 22 Tahun 2015 Tentang Biaya Kuliah dan Uang Kuliah Tunggal Pada Perguruan Tinggi Negeri Di Lingkungan Kementrian Riset, Teknologi dan Pendidikan. Indonesia, 2015.
  2. F. Jauhari, W. F. Mahmudy, and A. Basuki, “Multi-Objective Optimization for Proportional Tuition Fees Assessment Using Non-Dominated Sorting Genetic Algorithm II (NSGA II),†3rd Int. Conf. Sustain. Inf. Eng. Technol. SIET 2018 - Proc., pp. 292–297, 2019.
  3. A. Zhou, B.-Y. Qu, H. Li, S.-Z. Zhao, P. N. Suganthan, and Q. Zhang, “Multiobjective evolutionary algorithms: A survey of the state of the art,†Swarm Evol. Comput., vol. 1, no. 1, pp. 32–49, 2011.
  4. J. C. Tay and D. Wibowo, “An effective chromosome representation for evolving flexible job shop schedules,†Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 3103, pp. 210–221, 2004.
  5. W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part I: Modelling,†Adv. Mater. Res., vol. 701, pp. 359–363, May 2013.
  6. W. F. Mahmudy, R. M. Marian, and L. H. S. Luong, “Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part II: Optimization,†Adv. Mater. Res., vol. 701, pp. 364–369, May 2013.
  7. N. Rikatsih, W. F. Mahmudy, and S. Syafrial, “Hybrid Real-Coded Genetic Algorithm and Variable Neighborhood Search for Optimization of Product Storage,†J. Inf. Technol. Comput. Sci., vol. 4, no. 2, p. 166, 2019.
  8. M. T. Ben Othman and G. Abdel-Azim, “Multiple sequence alignment based on genetic algorithms with new chromosomes representation,†Proc. Mediterr. Electrotech. Conf. - MELECON, pp. 1030–1033, 2012.
  9. Y. Hou, N. Q. Wu, M. C. Zhou, and Z. W. Li, “Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm,†IEEE Trans. Syst. Man, Cybern. Syst., vol. 47, no. 3, pp. 517–530, 2017.
  10. R. Rody, W. F. Mahmudy, and I. P. Tama, “Using Guided Initial Chromosome of Genetic Algorithm for Scheduling Production-Distribution System,†J. Inf. Technol. Comput. Sci., vol. 4, no. 1, p. 26, 2019.
  11. Y. Liu, W. Huangfu, H. Zhang, H. Wang, W. An, and K. Long, “An Efficient Geometry-Induced Genetic Algorithm for Base Station Placement in Cellular Networks,†IEEE Access, vol. 7, pp. 108604–108616, 2019.
  12. N. Hitomi and D. Selva, “Constellation optimization using an evolutionary algorithm with a variable-length chromosome,†IEEE Aerosp. Conf. Proc., vol. 2018-March, pp. 1–12, 2018.
  13. L. Cruz-Piris, I. Marsa-Maestre, and M. A. Lopez-Carmona, “A Variable-Length Chromosome Genetic Algorithm to Solve a Road Traffic Coordination Multipath Problem,†IEEE Access, vol. 7, pp. 111968–111981, 2019.
  14. B. Tan, H. Ma, and Y. Mei, “Novel Genetic Algorithm with Dual Chromosome Representation for Resource Allocation in Container-Based Clouds,†2019 IEEE 12th Int. Conf. Cloud Comput., pp. 452–456, 2019.