## 1 Introduction  
The unrelated parallel machine scheduling problem is an important combinatorial optimisation problem with frequent applications in the real world [1], such as task scheduling [2], device scheduling [3], and manufacturing [4]. Since the problem is NP-hard, it is traditionally solved using various metaheuristic optimisation methods [5, 6]. However, since real-world problems are often dynamic in nature, traditional optimisation approaches that continuously improve potential solutions are usually not directly applicable. The reason for this is that not all information about the problem under consideration may be known, and in this case, it is not possible to construct a complete solution. This was the motivation for researching alternative methods that can be used to solve dynamic problems such as dispatching rules. Dispatching rules (DRs) have emerged as an alternative solution method for solving various scheduling problems, mostly dynamic problem variants [7]. They represent simple constructive heuristics that solve the problem incrementally by making decisions about which job to schedule next based solely on the information currently available in the system. Therefore, such rules can react quickly to the changing problem environment, as they construct the solution based only on the currently available information. As new information becomes available, they can take it into account when constructing the solution. Although many DRs have been proposed over the years [7], they have been developed for a limited number of problem variants and scheduling criteria. In addition, the developed DRs perform scheduling decisions using simple heuristic strategies, which affects the quality of the plans they produce. To address the above problems, various methods based on evolutionary computation and machine learning have been used over the years to automatically design new DRs for various problems [8; 9; 10]. One of the most commonly used methods for this purpose is genetic programming [11]. GP is an evolutionary computational method developed to enable the generation of various mathematical expressions and even computer programmes [12]. It has been applied in many fields, where it has achieved competitive results with humans [13], proving its effectiveness. In recent years, however, GP has established itself as the leading hyperheuristic method [14; 15], a method used to automatically develop new heuristics. In the context of DRs, GP is used to generate an expression, often referred to as a priority function, that is used by the DR to rank all decisions at each decision point and select the appropriate decision [16]. GP generates the expression based on a set of defined problem characteristics, which it attempts to combine in a meaningful way to determine the optimal scheduling decision at each decision point. Therefore, it is necessary to provide GP with all the relevant information about the problem in the form of terminal nodes representing certain features of the problem. However, an incomplete or wrong choice of terminal nodes can result in myopic DRs that offer poor performance [17]. Regardless, this step is often overlooked, and quite often only the most basic problem information is used as terminal nodes without a selection of appropriate nodes. Naturally, providing too many terminal nodes again has the drawback that the search space of the method grows exponentially, making it more difficult to find high-quality solutions. Although the definition and choice of appropriate terminal nodes is an important decision that significantly affects the quality and complexity of DRs, it has not yet been investigated in the context of the automated design of DRs for unrelated machine environments. Thus, there is no guarantee that the terminal nodes currently applied in the literature are really the most appropriate or whether the results could be improved by applying a different set of terminal nodes.