Improving the Efficiency of Genetic Algorithms through Discrete-Event Simulation

Genetic Algorithms (GAs) have been extensively utilized as powerful heuristic methods for addressing complex optimization problems across various scientific and engineering domains.Their ability to explore large and nonlinear solution spaces makes them suitable for solving problems where traditional methods fall short.However, a significant limitation arises when GAs are applied in dynamic environments, where system parameters and objectives are subject to frequent and unpredictable changes.In such contexts, the performance of GAs tends to degrade due to their sensitivity to real-time events, delays in adaptation, and a lack of mechanisms to respond promptly to environmental shifts.To address this challenge, this paper introduces an integrated approach that leverages Discrete Event Simulation (DES) as a comprehensive framework for modeling, observing, and evaluating the performance of Genetic Algorithms in time-sensitive and non-static scenarios.Unlike conventional evaluation methods, which often assume static environments and fixed benchmarks, the proposed DES-based methodology enables the simulation of real-world processes through event-driven timelines and state transitions, providing a more nuanced and accurate representation of system behavior.By embedding GA operations within a DES framework, the study enables real-time tracking of algorithmic convergence, robustness under varying conditions, and adaptability to dynamic constraints.This integration facilitates a deeper understanding of how GAs respond to changes over time, such as shifts in objective functions or system parameters.Extensive simulation results validate the effectiveness of this approach, demonstrating that DES-supported evaluation not only reveals performance bottlenecks but also uncovers opportunities for algorithmic tuning and structural enhancements.The findings highlight the potential of Discrete Event Simulation as a valuable tool for advancing the development and deployment of Genetic Algorithms in realworld applications, particularly in domains characterized by uncertainty, variability, and temporal dependencies.This approach paves the way for more resilient, adaptive, and efficient optimization solutions in dynamic and complex systems.