Do Genetic Algorithms Perform Better than Brute-Force Optimization for Solar Calibrations?

Solar calibration of the convective mixing length, $\alpha_\text{MLT}$, is a critical step in stellar modeling. However, traditional methods like brute force parameter sweeps are computationally expensive and other methods are sensitive to initial conditions. In this analysis, we study an alternative approach for solar optimization: Genetic Algorithms, or GAs. We test this method using the open-source stellar evolution code MESA. We compare GA-based optimization against adaptive brute force grids by evaluating convergence behavior, computational cost, and resulting best-fit solar parameters. We find that GAs achieve comparable accuracy while reducing computational cost relative to adaptive brute force methods. However, GA solutions exhibit random convergence behavior and consistently undershoot the solar value of $\alpha_\text{MLT}$ found using the adaptive brute force grid. Using a variety of metrics, including how quickly GAs found a solution, the quality of the solution, and the persistence of the solution, we quantified GA performance. Our results suggest that Genetic Algorithms provide a more efficient method for solar calibration.