## 1 Introduction  
SLMs are extensively employed for wavefront manipulation, owing to their ability to efficiently shape the intensity, polarization, or phase of light with high spatial resolution. These devices serve numerous purposes in photonics and communications, mainly as programmable diffractive optical elements, ranging from dynamic beam-shaping holography to three-dimensional imaging [1, 2, 3, 4, 5, 6]. In practical situations, the most common non-ideal behaviour observed in these LC-based devices is the smoothing effect of the director distribution at the borders between adjacent pixels, a phenomenon known as pixel crosstalk. This phenomenon results from the interaction of neighbouring pixels, producing notable deviations from the intended output, especially in patterns with high variations or contrast [7, 8, 9]. Pixel crosstalk generally arisesnot only from the natural smoothing of the electric field within the liquid crystal layer, as compared to the electrode voltages, but also from the elastic interactions among dielectric LC molecules, which impede abrupt changes in orientation, thus impacting the electric field. Thus, SLM models with smaller pixels or thicker LC layers experience increased pixel crosstalk, affecting applications that demand high spatial frequency content in phase patterns [9; 10; 11; 12]. To address this problem, minimisation of the precise Frank-Oseen free energy is typically required, most commonly through their derived Euler-Lagrange equations such as in [7; 8; 13]. This theoretical model has proven to reproduce the measured response of the SLM accurately. While efforts have been made to solve it through alternative machine learning techniques for simple and canonical structures based on 1D or 2D models [14; 15], little success has been achieved when handling a higher number of variables or an entire 3D space. Genetic algorithms are evolutionary computation techniques inspired by natural selection and genetics principles, commonly used to generate high-quality solutions to optimisation and search problems in different science disciplines [16; 17; 18; 19; 20]. These machine learning algorithms prove especially suitable when other traditional methods fail, such as dealing with complexity issues or non-differentiable objective functions. Although previous efforts have applied evolutionary machine learning methodologies to minimise the Frank-Oseen free energy, these methods have yet to be adapted for a larger number of variables or voxels representing three-dimensional space [14]. Applying these methods directly to complex 3D structures, in general, is challenging because it requires significantly increased computational power to ensure the process is competitive compared to traditional formalisms. This research paper aims to resolve the nematic cell vector directors within the liquid crystal by directly minimising the Frank-Oseen free energy through a GA framework for the highest possible spatial resolution. To overcome the challenge of solving the problem for a significantly greater number of variables, we propose a lower-dimensional remapping of the solution space using (PCA). Dimensionality reduction techniques, such as PCA or t-distributed Stochastic Neighbor Embedding (t-SNE) [21], are widely applied across diverse scientific and industrial fields [22; 23; 24; 25; 26; 27]. Their primary purpose is to enhance visualisation and understanding, particularly when managing high-dimensional datasets. These techniques are most commonly employed to project such high-dimensional data into two dimensions, facilitating efficient plotting and aiding in cluster detection for classification purposes. This allows for gaining insights by representing complex information in an accessible and visually interpretable manner [28; 29; 30]. However, as stated above, our approach uses these methods specifically for the lower-dimensional remapping of our solution space, leveraging the assumption that it is smooth, or in other words, it features significant spatial autocorrelation.