Quadratic Approximation based GA for Function Optimization
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Date
2025
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Department of Mathematics, NITS
Abstract
This thesis presents an enhanced Genetic Algorithm (GA) framework utilizing Quadratic Approximation (QA) to solve both constrained and unconstrained optimization problems more efficiently. The primary objective is to obtain better fitness values by accelerating convergence and improving solution accuracy. In the proposed approach, a local quadratic approximation is integrated into the GA to refine solutions, thus balancing global exploration and local exploitation. To evaluate the effectiveness of the proposed QA-based GA (QA-GA), extensive experiments were conducted on standard benchmark functions, including Sphere, Rastrigin, and Ackley functions, among others. The performance of the QA-GA was systematically compared with the classical GA in terms of convergence speed and optimal solution quality. Experimental results indicate that QA-GA consistently outperforms the traditional GA by converging faster and locating better global optima across various test cases. The findings confirm that the integration of quadratic approximation significantly enhances the search capability of GA, making QA-GA a robust approach for complex optimization tasks.