## 3 Related Works  
The foundational work [11] offers a comprehensive primer on reinforcement learning (RL) theory, covering essential topics such as tabular methods, the exploration-exploitation trade-off, Markov decision processes, Bellman equations, and Monte Carlo methods, with an emphasis on the role of hyperparameters in shaping RL performance. Complementing this, ref. [3] introduces deep RL algorithms, including DQN, Actor-Critic (A2C), Deep SARSA, and REINFORCE, addressing challenges in policy evaluation, control, and optimal policy discovery. Additionally, this work examines value functions, reward structures, model architectures, learning mechanisms, and real-world applications of RL algorithms, particularly in robotics and finance. In [20], adaptation theory is introduced in biological and artificial systems, laying the foundation for genetic algorithms (GAs). This work examines adaptive processes such as natural selection and applies them to artificial adaptation, proposing evolution-inspired algorithms that have significantly influenced AI, control theory, and optimisation, forming the basis for modern AI optimisation methods. Complementing this, ref. [23] presents a clear and accessible overview of crossover operator algorithms, detailing their functions and applications in GAs, which enhances the understanding of adaptive mechanisms in artificial systems. The paper [16] presents HPO as a mathematical optimisation process in machine learning, with a focus on meta-optimisation techniques. The study categorises hyperparameters into those that influence model architecture and those that impact optimisation and training, particularly within deep learning frameworks, thus addressing structural challenges in current methodologies. The authors evaluated the effectiveness of various HPO techniques--random search (RS), grid search (GS), and GAs--applied to the K-Nearest Neighbours (KNN) algorithm for image classification on the MNIST dataset. Results reveal that GAs outperform RS and GS by achieving higher accuracy and shorter computation time, underscoring the efficiency of GAs due to their ability to avoid exhaustive search and their resilience against local optima. The article [30] explores the use of GA for optimising hyperparameters in deep RL, with a particular focus on improving the deep deterministic policy gradient algorithm (DDPG) through Hindsight Experience Replay (HER). By pinpointing the optimal values for six critical hyperparameters, including the discount factor \(\gamma\) and the learning rates for the critic and actor networks, the study demonstrates substantial performance gains in a robotic arm environment. The results underscore that GA-based HPO can significantly reduce training time by effectively handling non-linear dependencies among hyperparameters. Complementing this, the study [15] presents a scalable GA framework for HPO in deep RL applications, introducing a multi-objective optimisation approach where hyperparameters evolve over generations. The research reveals that GAs can outperform Bayesianoptimisation, especially due to their capacity for parallel processing, which proves advantageous in environments requiring extensive exploration of the hyperparameter space due to inherent randomness. In their methodology, ref. [15] details the GA structure, defining crossover, mutation, and selection techniques that balance exploration and exploitation for effective HPO in deep RL. The fitness function evaluates each configuration based on cumulative reward and total loss, guiding the GA toward optimal settings. The results show that GA-based HPO accelerates convergence and consistently achieves superior policies, especially in dynamic RL environments requiring adaptive strategies. The study [28] analyses static and adaptive mutation techniques in GAs.