An improved hybrid genetic algorithm using the affine combination-based reproduction

References

• Abdelatti, M. F., and M. S. Sodhi. 2021. Using reinforcement learning for tuning genetic algorithms. In Proc. of the Conference on the Genetic and Evolutionary Computation (GECCO 2021), Lille, France. doi: 10.1145/3449726.3463203.
   Google Scholar
• Adorio, E. P., and U. P. Diliman. 2005. MVF – Multivariate test functions library in c for unconstrained global optimization. http://www.geocities.ws/eadorio/mvf.pdf.
   Google Scholar
• Bäck, T., and H. P. Schwefel. 1993. An overview of evolutionary algorithm for parameter optimization. Evolutionary Computation 1 (1):1–23. doi: 10.1162/evco.1993.1.1.1.
   Web of Science ®Google Scholar
• Chehouri, A., R. Younes, J. Khoder, J. Perron, and A. Ilinca. 2017. A selection process for genetic algorithm using clustering analysis. Algorithms 10 (4):123. doi: 10.3390/a10040123.
   Web of Science ®Google Scholar
• Chen, C. T. 1998. Linear systems theory and design. New York: Holt, Rinehart and Winston.
   Google Scholar
• Deep, K., and M. Thakur. 2007. A new mutation operator for real coded genetic algorithms. Applied Mathematics and Computation 193 (1):211–30. doi: 10.1016/j.amc.2007.03.046.
   Web of Science ®Google Scholar
• De-Jong, K. A. 1975. An analysis of the behavior of a class of genetic adaptive systems. PhD diss., University of Michigan.
   Google Scholar
• Demirci, H., A. T. Özcerit, H. Ekiz, and A. Kutlu. 2015. Chaotic crossover operator on genetic algorithm. Journal of Advances in Information Technology 6 (4):217–20. doi: 10.12720/jait.6.4.217-220.
   Google Scholar
• Dziwiński, P., and L. Bartczuk. 2020. A new hybrid particle swarm optimization and genetic algorithm method controlled by fuzzy logic. IEEE Transactions on Fuzzy Systems 28 (6):1140–54. doi: 10.1109/TFUZZ.2019.2957263.
   Web of Science ®Google Scholar
• Edwards, J. T., and N. J. Ford. 2002. Boundedness and stability of solutions to difference equations. Journal of Computational and Applied Mathematics 140 (1-2):275–89. doi: 10.1016/S0377-0427(01)00480-0.
   Web of Science ®Google Scholar
• Eiben, A. E., and J. E. Smith. 2003. Parameter selection in genetic algorithms: A comprehensive survey. Berlin Heidelberg: Springer.
   Google Scholar
• Goldberg, D. E. 1991. Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Systems 5 (2):139–68.
   Google Scholar
• Gu, X., M. Huang, and X. Liang. 2019. An improved genetic algorithm with adaptive variable neighborhood search for FJSP. Algorithms 12 (11):243. doi: 10.3390/a12110243.
   Web of Science ®Google Scholar
• Haq, E., I. Ahmad, A. Hussain, and I. M. Almanjahie. 2019. A novel selection approach for genetic algorithms for global optimization of multimodal continuous functions. Computational Intelligence and Neuroscience 2019:8640218–4. doi: 10.1155/2019/8640218.
   PubMed Web of Science ®Google Scholar
• Harif, S., and R. Mohamaddoust. 2023. Zigzag mutation: A new mutation operator to improve the genetic algorithm. Multimedia Tools and Applications 82 (29):45411–32. doi: 10.1007/s11042-023-15518-3.
   Web of Science ®Google Scholar
• Holland, J. H. 1975. Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.
   Google Scholar
• Jamil, M., and X. S. Yang. 2013. A literature survey of benchmark functions for global optimization problems. Int. Journal of Mathematical Modelling and Numerical Optimisation 4 (2):150–94.
   Google Scholar
• Jebari, K., and M. Madiafi. 2013. Selection methods for genetic algorithms. International Journal of Emerging Science and Engineering 3 (4):333–44.
   Google Scholar
• Katoch, S., S. S. Chauhan, and V. Kumar. 2021. A review on genetic algorithm: Past, present, and future. Multimedia Tools and Applications 80 (5):8091–126. doi: 10.1007/s11042-020-10139-6.
   PubMed Web of Science ®Google Scholar
• Li, H., and N. Shi. 2022. Application of genetic optimization algorithm in financial portfolio problem. Computational Intelligence and Neuroscience 2022:5246309. doi: 10.1155/2022/5246309.
   PubMedGoogle Scholar
• Li, Y., X. Yao, and W. Lin. 2023. A comprehensive study of selection mechanism for genetic algorithms in dynamic environments. Evolutionary Computation 31 (2):243–75.
   Google Scholar
• MathWorks, I. 2020. Global optimization toolbox user’s guide. Natick, MA: Mathsworks, Inc.
   Google Scholar
• Michalewicz, Z. 1992. Genetic algorithms + data structures = evolution programs. New York: Springer-Verlag.
   Google Scholar
• Mishra, S. 2006. Some new test functions for global optimization and performance of repulsive particle swarm method. Munich Personal RePEc Archive, Paper No. 2718. https://mpra.ub.uni-muenchen.de/2718/.
   Google Scholar
• Moghadampour, G. 2011. Self-adaptive integer and decimal mutation operators for genetic algorithms. In Proc. of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), 184–191, Beijing, China. doi: 10.5220/0003494401840191.
   Google Scholar
• Naqvi, F. B., M. Y. Shad, and S. Khan. 2020. A new logistic distribution based crossover operator for real-coded genetic algorithm. Journal of Statistical Computation and Simulation 91 (4):817–35. doi: 10.1080/00949655.2020.1832093.
   Web of Science ®Google Scholar
• Pavai, G., and T. V. Geetha. 2016. A survey on crossover operators. ACM Computing Surveys 49 (4):1–43. doi: 10.1145/3009966.
   Web of Science ®Google Scholar
• Pham, D. T., and G. G. Jin. 1995. Genetic algorithm using gradient-like reproduction operator. Electronics Letters 31 (18):1558–9. doi: 10.1049/el:19951092.
   Web of Science ®Google Scholar
• Rabee, F., and Z. M. Hussain. 2023. Oriented crossover in genetic algorithms for computer networks optimization. Information 14 (5):276. doi: 10.3390/info14050276.
   Google Scholar
• Shu, L., J. Li, H. Wu, and Z. Heng. 2022. Optimization of multi-track laser-cladding process of titanium alloy based on RSM and NSGA-II algorithm. Coatings 12 (9):1301. doi: 10.3390/coatings12091301.
   Web of Science ®Google Scholar
• Surjanovic, S., and D. Bingham. 2013. Optimization test functions and datasets, Simon Fraser University, https://www.sfu.ca/∼ssurjano/optimization.html.
   Google Scholar
• Wang, J., M. Zhang, O. K. Ersoy, K. Sun, and Y. Bi. 2019. An improved real-coded genetic algorithm using the heuristical normal distribution and direction-based crossover. Computational Intelligence and Neuroscience 2019:4243853. Article ID 4243853. doi: 10.1155/2019/4243853.
   PubMed Web of Science ®Google Scholar
• Xie, F., Q. Sun, Y. Zhao, and H. Du. 2022. An improved directed crossover genetic algorithm based on multilayer mutation. Journal of Control Science and Engineering 2022, 1–10. doi: 10.1155/2022/4398952.
   Web of Science ®Google Scholar
• Yousef, M., T. M. Abdelkader, and K. Elbahnasy. 2020. An efficient protein structure prediction using genetic algorithm. In Computational intelligence: applications in bioinformatics and biomedicine, eds. T. M. Abdel-Rahman and E. E. Hassan, 433–48. Milton Park: Taylor & Francis Group.
   Google Scholar