## 2 Background
### Related Work
They are used to prevent the use of a substantial amount of computing time when evaluating unpromising rules. Typically, running these models is far more efficient for assessing individual fitness than running full simulations [19]. A tree-based representation has been shown to be the most effective method for developing robust scheduling heuristics in early investigations. As a consequence of their tendency to grow throughout their evolution, other representation structures have also been explored. Gene expression programming (GEP), a kind of GP with liner representation, has been employed to design dispatching rules in a wide range of scheduling contexts, including single machine scheduling, parallel machine scheduling, job-shop scheduling, and flexible job-shop scheduling. [PERSON] et al. [33] designed dispatching rules for dynamic single-machine scheduling issues using the GEP-based hyperheuristic method. [PERSON] et al. [34] addressed a GEP method for flexible DJSS with job release dates by designing reactive priority rules in a linear representation. The findings demonstrated that GEP is capable of developing appropriate scheduling policies across a broad spectrum of processing circumstances and performance metrics. For dynamic multiobjective scheduling issues, [PERSON] et al. [35] proposed two new approaches to extract composite scheduling rules by applying the GEP method. According to the authors, all the rules developed using GEP for this scenario outperformed the GP-evolved rules and the classical rules from the literature. Later, to construct scheduling policies for multiobjective dynamic job-shop scheduling with the constrained buffer space, [PERSON] et al. [36] employed GEP in conjunction with a simulation model, which heightened the complexity of the issue. The experimental results proved the superiority of using the GEP algorithm with the existing priority dispatching rules under different operating conditions. To address the issue of flexible job-shop scheduling problems with setup time and unpredictable work arrival, [PERSON] et al. [37] suggested a GEP-based dynamic scheduling technique with enhanced features. The variable neighborhood search method was employed in the survey to enhance the local search capacity of GEP, achieving superior results in comparison to the standard GEP and GP algorithms;  
**(3) Feature selection approaches. ** In dynamic job-shop situations, a diverse range of characteristics related to workshop states can be considered as potential features to be incorporated into the terminal set. It can be difficult to determine which features are advantageous for studying scheduling heuristics. Previous studies frequently incorporated any potential characteristic into the ultimate set of terminal features. The scheduling heuristics that have been designed exhibit a diverse range of characteristics, rendering them difficult to comprehend. Moreover, a significant number of terminals with redundant or irrelevant attributes can result in a search space that is both extensive and noisy, thus reducing the efficacy of the search of GP algorithms. Hence, it is essential to choose suitable terminal sets based on the practical requirements of job-shop conditions. The feature selection process is a crucial technique in the field of machine learning, as it enables the identification and selection of relevant data that can effectively reduce the exploration scope of evolutionary algorithms. As the features appearing in the evolved individual can be treated as a set of selected features, GP is considered to have the built-in feature selection ability. Determining the hidden links between a subset of features, however, is neither very efficient nor precise. [PERSON] et al. [38] suggested a feature selection methodology that relied on frequency analysis, specifically, the frequency of the occurrence of a given terminal in rules. This methodology was subject to certain constraints, whereby particular characteristics may be inaccurately assessed due to their prevalence in the redundant priority function.