Hybrid Genetic Algorithm for Dynamic Portfolio Optimization Problems

Dynamic portfolio optimization is a complex problem due to continuous changes in market conditions, demanding algorithms capable of effective adaptation. Genetic Algorithms (GA) are often used for optimization problems but may face limitations in convergence speed and solution precision. This research aims to develop and evaluate a Hybrid Genetic Algorithm (HGA) that integrates GA with the Hill Climbing local search method, and to compare its performance against standard GA in solving dynamic portfolio optimization problems with the objective of maximizing the Sharpe Ratio. A series of simulation-based experiments were conducted by varying key algorithmic and dynamic environment parameters. Simulation results indicate that HGA generally has significant potential to improve performance compared to standard GA. Consistently, HGA successfully achieved superior solution quality, both in terms of Offline Performance Solution Quality and Overall Best Fitness. Regarding robustness to dynamic changes, HGA also demonstrated a smaller impact from performance degradation and a more promising recovery capability after market environment changes. Although HGA's superiority in convergence speed is not always absolute and the implementation of Hill Climbing adds to the computational time per generation, the improvement in solution quality and robustness offered in many configurations can be considered a worthwhile trade-off, especially for complex dynamic portfolio optimization problems. These findings support the hypo that hybridizing GA with local search can provide a positive contribution, noting that careful parameter tuning is crucial for maximizing HGA's potential.