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Lambora, A., Gupta, K., & Chopra, K. (2019, February). Genetic algorithm-A literature review. In 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 380-384). IEEE.Search in Google Scholar
Mirjalili, S., & Mirjalili, S. (2019). Genetic algorithm. Evolutionary algorithms and neural networks: theory and applications, 43-55.Search in Google Scholar
Haldurai, L., Madhubala, T., & Rajalakshmi, R. (2016). A study on genetic algorithm and its applications. Int. J. Comput. Sci. Eng, 4(10), 139-143.Search in Google Scholar
Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia tools and applications, 80, 8091-8126.Search in Google Scholar
Han, S., & Xiao, L. (2022). An improved adaptive genetic algorithm. In SHS web of conferences (Vol. 140, p. 01044). EDP Sciences.Search in Google Scholar
Hınçal, O., Altan-Sakarya, A. B., & Metin Ger, A. (2011). Optimization of multireservoir systems by genetic algorithm. Water resources management, 25, 1465-1487.Search in Google Scholar
Lee, C. K. H. (2018). A review of applications of genetic algorithms in operations management. Engineering Applications of Artificial Intelligence, 76, 1-12.Search in Google Scholar
Song, Y., Wang, F., & Chen, X. (2019). An improved genetic algorithm for numerical function optimization. Applied Intelligence, 49, 1880-1902.Search in Google Scholar
Liang, Y., & Leung, K. S. (2011). Genetic algorithm with adaptive elitist-population strategies for multimodal function optimization. Applied Soft Computing, 11(2), 2017-2034.Search in Google Scholar
Tabassum, M., & Mathew, K. (2014). A genetic algorithm analysis towards optimization solutions. International Journal of Digital Information and Wireless Communications (IJDIWC), 4(1), 124-142.Search in Google Scholar
Wang, H., Liu, J., Zhi, J., & Fu, C. (2013). The improvement of quantum genetic algorithm and its application on function optimization. Mathematical problems in engineering, 2013(1), 730749.Search in Google Scholar
Norouzi, A., & Zaim, A. H. (2014). Genetic algorithm application in optimization of wireless sensor networks. The Scientific World Journal, 2014(1), 286575.Search in Google Scholar
Elsayed, S. M., Sarker, R. A., & Essam, D. L. (2014). A new genetic algorithm for solving optimization problems. Engineering Applications of Artificial Intelligence, 27, 57-69.Search in Google Scholar
Dias, J., Rocha, H., Ferreira, B., & Lopes, M. D. C. (2014). A genetic algorithm with neural network fitness function evaluation for IMRT beam angle optimization. Central European Journal of Operations Research, 22(3), 431-455.Search in Google Scholar
Tawhid, M. A., & Ali, A. F. (2017). A hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function. Memetic Computing, 9, 347-359.Search in Google Scholar
Ruiz, A. B., Saborido, R., & Luque, M. (2015). A preference-based evolutionary algorithm for multiobjective optimization: the weighting achievement scalarizing function genetic algorithm. Journal of Global Optimization, 62, 101-129.Search in Google Scholar
Cai, M. (2022). An improved particle swarm optimization algorithm and its application to the extreme value optimization problem of multivariable function. Computational intelligence and neuroscience, 2022(1), 1935272.Search in Google Scholar
Jamil, M., & Yang, X. S. (2013). A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2), 150-194.Search in Google Scholar
González-Palacio, M., Sepúlveda-Cano, L., Valencia, J., D’Amato, J., Quiza-Montealegre, J., & Palacio, L. G. (2020, June). System dynamics baseline model for determining a multivariable objective function optimization in Wireless Sensor Networks. In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-6). IEEE.Search in Google Scholar
Sgarro, G. A., & Grilli, L. (2023). Genetic algorithm for optimal multivariate mixture. Applied Mathematical Sciences, 17(1), 15-25.Search in Google Scholar
Mkhatshwa, T. P. (2022). A study of calculus students’ difficulties, approaches and ability to solve multivariable optimization problems. International journal of mathematical education in science and technology, 53(11), 2987-3014.Search in Google Scholar
Wu, S. (2015). State space predictive functional control optimization based new PID design for multivariable processes. Chemometrics and Intelligent Laboratory Systems, 143, 16-27.Search in Google Scholar
