## 2 Literature Review  
The automated generation of DRs has its roots in the pioneering studies of [PERSON] [18] and [PERSON] and [PERSON] [19]. These studies showed that GP can be efficiently used to design new DRs for various scheduling problems. Since then, GP has been applied in numerous studies to generate DRs for various problems, such as the parallel machine scheduling problem [20], flexible job shop problem [21], resource-constrained project scheduling problem [22], and one-machine variable capacity problem [23]. Over the years, many research directions have been explored in the context of the automated design of DRs. One line of research has focused on investigating different representations for DRs. In [24], the authors compared three DR representations: one that generates an expression that selects an existing DR based on system properties to make the scheduling decision, one that generates a new DR from basic system information, and one that combines the previous two approaches. The results showed that the first approach was inferior to the other two approaches. In [25], the potential of GP to generate DRs was compared with that of artificial neural networks. The results showed that both methods performed similarly, but GP developed interpretable and more user-friendly DRs. In [26], the authors investigated several evolutionary algorithms for developing DRs and found that their performance was similar in most cases, although some resulted in simpler DRs. Another line of research focused on the generation of DRs for multi-objective scheduling problems, which were considered in [27; 28; 29] for the job shop problem and [30] for the unrelated machine scheduling problem. Ensemble learning is one of the most widely considered topics related to DR creation, as it has the potential to significantly improve the performance of individual DRs. This topic has been studied from many aspects, including methods used to construct ensembles [31; 32; 33], methods used to aggregate the decisions of individual DRs in the ensemble [34; 35], and the application of ensemble learning for multi-objective problems [36]. Another way to improve the performance of automatically generated DRs is the application of surrogate models [37; 38] and local search [39], both of which have been shown to improve the performance of GP. Another recent research trend is the application of multitask learning in GP, which enables knowledge transfer between individuals to solve different types of problems [40]. Only limited attention has been given to the issues of feature selection and construction in the context of automatic generation of DRs, and this has focused mainly on the job shop scheduling problem. In [41], the authors proposed a feature selection algorithm that is embedded in GP and based on niching. This feature selection method takes into account both the fitness of the individuals in which the features occur and the frequency of occurrence of these features. The experimental results showed that the proposed approach can select features that lead to better results compared to using the entire feature set. In [42], the authors considered feature selection in the context of developing DRs for the flexible job shop problem. In this approach, two terminal sets are used and, therefore, featuresneed to be selected for each of them simultaneously. A GP approach with adaptive search based on the frequency of features was proposed in [43] and was shown to perform better compared to standard GP in some scenarios. Another feature selection approach was proposed in [44], which led to simpler DRs that achieved comparable performance to those developed without feature selection. Finally, in [45], the authors considered feature construction in addition to feature selection, but no follow-ups or extensions to this original research were conducted. Overviews of the literature on the remaining studies dealing with the automated design of DRs for various scheduling problems can be found in [8; 9; 10].