## 4 An Exploration of the Advantages of Genetic Algorithms
### Application of GA-Based Energy Management for HEVs
When combined with fuzzy logic parameter adjustment, the coverage of the Pareto frontier in multi-objective optimization was 15.7% higher than that of MOPSO. This comprehensively validates the technical superiority of GAs in addressing multi-objective and multi-constraint optimization problems in HEVs. [PERSON] et al. (2022) [38] assessed the performance of energy management strategies optimized by the GA and presented a detailed comparison among GA-optimized, non-optimized, and DP strategies. To verify the robustness of the GA-optimized fuzzy control energy management strategy, the researchers conducted a comparative performance analysis under different ambient temperature conditions (10 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C). The comprehensive analysis results indicate that the energy economy of the electric vehicle is improved by 2.6%, 2.4%, and 3.3% compared to the non-optimized strategy under different temperature conditions. The hybrid energy storage system (HESS) reduced energy consumption by 2.6%, 2.4%, and 3.3% at 10 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C, respectively. Additionally, the performance difference between the GA optimization strategy and the DP strategy decreased to 4.3%, 2.7%, and 5.1% at 10 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C, respectively. [PERSON] et al. (2022) [188] employed GAs in the optimization of the hybrid vehicle EMS, demonstrating significant advantages under two typical driving conditions, NEDC and UDDS. When compared with rule-based EMS and fuzzy logic EMS, the GA optimization scheme achieves optimal performance close to that of DP in terms of fuel cell durability and battery charge maintenance. Specifically, under NEDC conditions, the GA optimization reduces hydrogen consumption by 34.43% compared to the rule-based EMS and by 18.56% compared to the fuzzy logic EMS. Under UDDS conditions, the reductions are 26.18% and 20.63%, respectively. These data comprehensively demonstrate that the GA can effectively overcome the local optimum limitation of the traditional rule-based approach through its global optimization capability, while avoiding the lack of accuracy in the fuzzy logic controller. In the study conducted by [PERSON] et al. (2025) [189], a multi-objective evolutionary reinforcement learning (MOEvoRL) framework is proposed to optimize electric vehicle charging strategies using GAs, enabling the identification of multiple optimal strategies in a single training session. The study focuses on the simultaneous attainment of key objectives, including battery state-of-charge maximization, photovoltaic (PV) power consumption enhancement, peak load reduction, and overall grid load smoothing. In this context, the GA exhibits significant technical advantages. In comparison to the traditional gradient-based MODDPG algorithm, which is susceptible to falling into local optima, the GA-driven MOEvoRL demonstrates excellent convergence stability in multi-objective optimization due to its global search capability and population intelligence mechanism. In particular, its LSTM-NSGA-II architecture not only generates diverse solution sets that comply with grid constraints, but also significantly reduces the reliance on large-scale training data. When compared with deep reinforcement learning methods, GAs possess outstanding features such as simple implementation, efficient computation, and strong parallelization ability. Its population optimization property is inherently suitable for the multi-objective trade-off problem in energy management. Experiments confirm that the GA achieves a 13.7% improvement in energy allocation efficiency over traditional methods while ensuringgrid stability. This comprehensive performance advantage renders it a promising intelligent optimization paradigm in the field of EV energy management. Therefore, these studies collectively show that the advantages of applying genetic algorithms compared with other hybrid vehicle energy management strategies are mainly reflected in the following four aspects:  
1.