New Approach of Self-Adaptive Simulated Binary Crossover-Elitism in Genetic Algorithms for Numerical Function Optimization


  • Adriana Fanggidae Department of Computer Science, Faculty of Science and Engineering, Universitas Nusa Cendana, Indonesia
  • Muhammad Iqrom Catur Prasetyo Department of Computer Science, Faculty of Science and Engineering, Universitas Nusa Cendana, Indonesia
  • Yulianto Triwahyuadi Polly Department of Computer Science, Faculty of Science and Engineering, Universitas Nusa Cendana, Indonesia
  • Meiton Boru Department of Computer Science, Faculty of Science and Engineering, Universitas Nusa Cendana, Indonesia


Elitism, Genetic Algorithm, Real-Valued Encoding, Self-Adaptive, Simulated Binary Crossover


One of the critical evolutionary operators in genetic algorithms (GAs) is crossover. Simulated Binary Crossover (SBX) is a commonly employed crossover operator in GA for real-valued encoding. Self-Adaptive SBX introduces a distribution index parameter that is updated in each generation, enabling the offspring solution distance to be independent of the parent solution distance. During evolution, the extinction of the fittest individuals is possible, and elitism is employed to prevent such extinction, thereby preserving the quality of the offspring. This research proposes GA with Self-Adaptive SBX-Elitism to enhance the performance of GA with Self-Adaptive SBX. The performance of GA with Self-Adaptive SBX-Elitism and GA with Self-Adaptive SBX is tested on ten benchmark functions. The test results on ten populations in dimensions ten, twenty, and thirty indicate that GA with Self-Adaptive SBX-Elitism can reduce the average relative error by 99.99%, with an average computation time that is 19.40% faster compared to GA with Self-Adaptive SBX. GA with Self-Adaptive SBX-Elitism performs well across twenty populations in all test dimensions.


Download data is not yet available.


H. Wu, Z. Ni, and L. Ni, “Solving Roots of Complex Functional Equation Based on Improved Ant Colony Algorithm,” in 2010 International Conference on E-Product E-Service and E-Entertainment, IEEE, Nov. 2010, pp. 1–4. doi: 10.1109/ICEEE.2010.5661471.

I. Boussaïd, J. Lepagnot, and P. Siarry, “A survey on optimization metaheuristics,” Inf. Sci. (Ny)., vol. 237, pp. 82–117, Jul. 2013, doi: 10.1016/j.ins.2013.02.041.

Z. Beheshti and S. M. H. Shamsuddin, “A review of population-based meta-heuristic algorithm,” Int. J. Adv. Soft Comput. its Appl., vol. 5, no. 1, pp. 1–35, 2013.

T. T. Tanyimboh, “Redundant binary codes in genetic algorithms: multi-objective design optimization of water distribution networks,” Water Supply, vol. 21, no. 1, pp. 444–457, Feb. 2021, doi: 10.2166/ws.2020.329.

M. V. Pathan, S. Patsias, and V. L. Tagarielli, “A real-coded genetic algorithm for optimizing the damping response of composite laminates,” Comput. Struct., vol. 198, pp. 51–60, Mar. 2018, doi: 10.1016/j.compstruc.2018.01.005.

K. Deb and R. Bhushan Agrawal, “Simulated Binary Crossover for Continuous Search Space,” Complex Syst., vol. 9, pp. 115–148, 1995.

K. Deb and A. Kumar, “Real-coded Genetic Algorithms with Simulated Binary Crossover : Studies on Multimodal and Multiobjective Problems,” Complex Syst., vol. 9, pp. 431–454, 1995.

J. Chacon and C. Segura, “Analysis and Enhancement of Simulated Binary Crossover,” in 2018 IEEE Congress on Evolutionary Computation (CEC), IEEE, Jul. 2018, pp. 1–8. doi: 10.1109/CEC.2018.8477746.

S. Gunasekaran and M. W. Iruthayarajan, “Contour optimization of suspension insulators using real coded genetic algorithm with simulated binary crossover,” in 2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering, IEEE, Feb. 2013, pp. 360–364. doi: 10.1109/ICPRIME.2013.6496501.

L. Pan, W. Xu, L. Li, C. He, and R. Cheng, “Adaptive simulated binary crossover for rotated multi-objective optimization,” Swarm Evol. Comput., vol. 60, p. 100759, Feb. 2021, doi: 10.1016/j.swevo.2020.100759.

K. Deb, K. Sindhya, and T. Okabe, “Self-adaptive simulated binary crossover for real-parameter optimization,” in Proceedings of the 9th annual conference on Genetic and evolutionary computation, New York, NY, USA: ACM, Jul. 2007, pp. 1187–1194. doi: 10.1145/1276958.1277190.

S. S. Pabboju and T. Adilakshmi, “An Improved Approach for Scheduling in Cloud Using GA and PSO,” J. Theor. Appl. Inf. Technol., vol. 100, no. 18, pp. 5298–5307, 2022.

A. Fanggidae and E. S. Y. Pandie, “Elitisme algoritma genetika pada fungsi nonlinear dua peubah,” J. Komput. dan Inform., vol. 8, no. 2, pp. 145–148, Oct. 2020, doi: 10.35508/jicon.v8i2.2894.

V. K. Singh and V. Sharma, “Elitist Genetic Algorithm Based Energy Balanced Routing Strategy to Prolong Lifetime of Wireless Sensor Networks,” Chinese J. Eng., vol. 2014, pp. 1–6, Mar. 2014, doi: 10.1155/2014/437625.

M. Jamil and X. S. Yang, “A literature survey of benchmark functions for global optimisation problems,” Int. J. Math. Model. Numer. Optim., vol. 4, no. 2, p. 150, 2013, doi: 10.1504/IJMMNO.2013.055204.

S. Surjanovic and D. Bingham, “Virtual Library of Simulation Experiments: Test Functions and Datasets.” Accessed: Mar. 22, 2023. [Online]. Available:

B. Farah, M. Awad, and A. Rutrot, “Prediction for Non-Revenue and Demand of Urban Water Using Hybrid Models of Neural Networks and Genetic Algorithms,” J. Theor. Appl. Inf. Technol., vol. 100, no. 21, pp. 6537–6551, 2022.

Y. P. Makimaa and R. Sundarmani, “Genetic Algorithm Based Energy Efficient Cluster Head Selection and Cluster Formation and Establishment for Hierarchical Wireless Sensor Networks,” Int. J. Intell. Syst. Appl. Eng., vol. 10, no. 4, pp. 173–178, 2022.

E. Wirsansky, Hands-on genetic algorithms with Python : applying genetic algorithms to solve real-world deep learning and artificial intelligence problems. Birmingham: Packt Publishing Ltd, 2020.

S. Kadam and T. Srinivasarao, “ElitGA: Elitism Based Genetic Algorithm for Evaluation of Mutation Testing on Heterogeneous Dataset,” Int. J. Intell. Syst. Appl. Eng., vol. 11, no. 4s, pp. 509–516, 2023.

D. David, T. Widayanti, and M. Q. Khairuzzahman, “Performance Comparison of Cat Swarm Optimization and Genetic Algorithm on Optimizing Functions,” in 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), Denpasar, Indonesia: IEEE, Aug. 2019, pp. 35–39. doi: 10.1109/ICORIS.2019.8874901.

Z. Avdagic, A. Smajevic, S. Omanovic, and I. Besic, “Path route layout design optimization using genetic algorithm: based on control mechanisms for on-line crossover intersection positions and bit targeted mutation,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 2, pp. 835–847, Feb. 2022, doi: 10.1007/s12652-021-02937-z.




How to Cite

Fanggidae, A. ., Catur Prasetyo, M. I. ., Triwahyuadi Polly, Y. ., & Boru, M. . (2024). New Approach of Self-Adaptive Simulated Binary Crossover-Elitism in Genetic Algorithms for Numerical Function Optimization. International Journal of Intelligent Systems and Applications in Engineering, 12(14s), 174–183. Retrieved from



Research Article