## 2 Literature Review
### Related Study of Spatial Planning with an Algorithm  
Optimal solutions can be found based on a suitable heuristic algorithm; ML applications for spatial layouts can be grouped into three categories: facility layout, floor layout, and furniture layout (Table 5).  
In the facility layout issue, the problem is determining the efficient arrangement of equipment in an area. Several studies address facility layout problems. In 2019, [PERSON] and [PERSON] applied GA to solve the facility layout problem [32]. [PERSON] et al. used A* search to determine the shortest path within the facility and transportation routes and then paired the A* with GA to improve the quality of the facility layouts [33]. [PERSON] et al. utilized a chaos search combined with GA and Tabu search to optimize facility layout problems [34]. They found that the results were better than others in reducing operating costs. In 2020, [PERSON] improved the double row layout problem to minimize the total cost of material flow among machines [35]. [PERSON] and [PERSON] also utilized the GA and particle swarm algorithm (PSA) to experiment with the dynamic facility layout problem [36]. [PERSON] et al. coupled GA with the surplus rectangle fill algorithm to experimental facility layout endeavors and achieved better solutions in their implementation [37].  
In the floor layout, [PERSON] et al. employed a perceptron learning algorithm to plan an apartment's partitions into several separate rooms in a 10 \(\times\) 10 bounding box [38]. They defined five different shapes (kitchen, living room, bedroom, bathroom, and corridor), and generated random contexts with a combination of room types to maximize the rooms with random to lower bounds. In 2020, [PERSON] et al. utilized the shoelace algorithm to design the floor layout in post-disaster temporary housing units in a specific area (4.51 m \(\times\) 5.01 m), which contained a living room, kitchen, entrance, and bathroom [39]. [PERSON] et al. utilized the Monte Carlo tree search to address floor plans within adjacency constraints, and they obtained good results in a time-efficient manner to generate the plans [40].  
Regarding furniture layout, [PERSON] and [PERSON] applied the improved particle swarm algorithm to optimize the arrangement of eight objects in the kitchen, and the arranged area is 48.6 m\({}^{2}\), which is included the kitchen area and living area. The optimized plan was the core area of the kitchen operation center integrated the water tank, multifunction table, and user's behavior line in the kitchen and living space, which was maximized to meet the needs of the user group [41]. [PERSON] et al. employed a perceptron learning algorithm to arrange tables in a room by choosing a recommendation to synthesize a novel configuration [38].  
Each heuristic algorithm has features to leverage people toward their intended target. Even with the same layout problem, e.g., facility layout or floor layout, some algorithms provide acceptable results depending on the desired outcome. It is important to choose the right algorithm depending on the features of properties and constraints of the training data. Among the above-mentioned layout categories, GA is the most common and basic algorithm for spatial layout problems. Consequently, GA is the optimal method for ELP to develop optimal layout instances.