Abstract:
This study explores the various application of Coded Genetic Algorithm (CGA) along with the solving of two complex mathematical optimization problems. Coded Genetic Algorithm can be used in design optimization, injection moulding, facility layout and scheduling, information security etc. In this study, the first function a trigonometric-exponential function was maximized using CGA, while the second, a convex Sphere-function, was minimized using CGA. Coding of GA was implemented in Python without relying on external libraries, ensuring wide accessibility. To visually and interpret the optimization performance the high-resolution 3D surface plots, contour plots, and convergence curves were generated. The results confirm the effectiveness of CGA in accurately locating global optima in nonlinear models and convex search spaces. This paper contributes to the growing use of the metaheuristic optimization studies by offering mathematical insights.
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