Optimization of Nonlinear Systems Using Genetic Algorithms: A Case Study in Resource Allocation

Abstract
This paper explores the use of genetic algorithms (GAs) for optimizing nonlinear systems in resource allocation. By simulating various allocation scenarios, we demonstrate the efficiency of GAs in finding near-optimal solutions in complex environments. The study provides a comparison of GA performance against traditional optimization methods and identifies scenarios where GAs outperform. The results emphasize the utility of GAs in real-world applications, especially when conventional approaches struggle with large solution spaces.
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How to Cite

Sarah Elhassan, et al. (2024). Optimization of Nonlinear Systems Using Genetic Algorithms: A Case Study in Resource Allocation. International Journal of Applied Mathematics and Computing, 1(2). https://doi.org/10.62951/ijamc.v1i2.72

Sarah Elhassan; Mohammed Idris; Hiba Abdallah, "Optimization of Nonlinear Systems Using Genetic Algorithms: A Case Study in Resource Allocation," International Journal of Applied Mathematics and Computing, vol. 1, no. 2, 2024.

Sarah Elhassan; Mohammed Idris; Hiba Abdallah. "Optimization of Nonlinear Systems Using Genetic Algorithms: A Case Study in Resource Allocation." International Journal of Applied Mathematics and Computing, vol. 1, no. 2, 2024.

Sarah Elhassan; Mohammed Idris; Hiba Abdallah. "Optimization of Nonlinear Systems Using Genetic Algorithms: A Case Study in Resource Allocation." International Journal of Applied Mathematics and Computing 1, no. 2 (2024).

Sarah Elhassan, et al. (2024) 'Optimization of Nonlinear Systems Using Genetic Algorithms: A Case Study in Resource Allocation', International Journal of Applied Mathematics and Computing, 1(2). doi: 10.62951/ijamc.v1i2.72.

Sarah Elhassan; Mohammed Idris; Hiba Abdallah. Optimization of Nonlinear Systems Using Genetic Algorithms: A Case Study in Resource Allocation. International Journal of Applied Mathematics and Computing. 2024;1(2).

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