## 3 Related Works
It demonstrates that static techniques, such as Gaussian, uniform, and boundary mutation, effectively support GA convergence, especially with complex parameters, whereas adaptive techniques are better suited to specific problems but may underperform on general benchmarks. The study concludes that selecting an appropriate mutation technique can significantly enhance the efficiency of GA in locating global minima. The chapter [4] presents key approaches to HPO in machine learning, covering GAs alongside modern techniques like Bayesian and multi-fidelity optimisation. It identifies challenges within the field and suggests future research directions to advance HPO. The article [22] presents a review of the literature on selection techniques in GA, focusing on methods such as roulette wheel and tournament selection. By comparing findings from various studies, the authors examine how these techniques affect population diversity and algorithm efficiency, providing valuable information for future applications. The publication [24] offers an in-depth overview of crossover operators in GAs, guiding researchers in selecting effective operators for improved outcomes. It covers classical, binary, and application-specific crossover operators, discussing the strengths and limitations of each across different applications, thereby enhancing understanding of the impact of crossover methods on GA performance. While [24] provides a broad review, ref. [23] takes a more technical approach by detailing a specific algorithm, offering a precise, hands-on perspective for implementation within GAs. Adaptive techniques in GA have been widely explored to enhance optimisation efficiency, particularly in balancing exploration and exploitation. The introduction of adaptive twin probability dynamically adjusts the likelihood of a twin operator based on the fitness of the best individual, relieving the user from manually defining this parameter and resulting in improved accuracy and faster convergence [31]. Additionally, self-adaptation mechanisms have been developed to maintain population diversity without requiring explicit parameter tuning, thereby mitigating premature convergence and reducing the risk of entrapment in local optima [32]. Another key advancement is the application of Gaussian mutation, which integrates principles of evolutionary theory to enhance the adaptability of genetic algorithms, making them more effective in solving complex optimisation problems [33]. These adaptive strategies collectively contribute to more robust and efficient evolutionary search processes, particularly in scenarios that demand dynamic parameter tuning. The study in [34] proposed a structural damage identification method based on a GA, taking into account the effect of temperature on material properties and boundary conditions [34]. By optimising an objective function combining natural frequencies and mode shapes, the authors achieved high accuracy in damage localisation even in the presence of noise. Similar adaptive approaches in GA are applied in RL, where dynamic training conditions hinder hyperparameter optimisation, and evolutionary methods such as GA effectively fine-tune deep RL algorithm parameters [35]. Modern hyperparameter optimisation methods increasingly employ adaptive strategies for managing computational resources. [PERSON] et al. (2024) [36] proposed FastBO, an extension of Bayesian optimisation to multi-fidelity settings, which dynamically adjusts resource levels based on learning curve analysis, reducing computational costs without compromising result quality. Meanwhile, [PERSON] et al. (2024) [37] developed ASAPSO-CNN, which combines particle swarm optimisation with simulated annealing to fine-tune CNN hyperparameters, improving learning stability and effectiveness with limited data. The integration of multi-fidelity strategies with metaheuristics could find applications in deep RL, where optimising hyperparameters such as the learning rate or exploration strategy is crucial for algorithm performance.