## 7 Conclusions and Future Work  
The aim of this study was to investigate the impact of different types of terminal nodes on the performance of GP in developing DRs for unrelated machine scheduling problems. The experimental results show that when GP is equipped with only atomic features to generate DRs, it can outperform simple DRs but has difficulty in evolving rules better than complex manually designed DRs. Therefore, two methods were used to construct composite terminal nodes and improve the performance of GP. In the first method, new terminals were constructed based on sub-expressions most commonly found in DRs developed with atomic terminals. Although with these terminals, GP matched the performance of manually constructed DRs, it still rarely outperformed them. However, the second approach, where composite terminal nodes were designed manually, proved to be more efficient and allowed GP to generate DRs that significantly outperformed manually designed DRs. Therefore, a certain level of expertise is required when designing terminal nodes, as it is necessary to define terminal nodes that provide more complex information about the scheduling problem. In addition to defining new terminals, it is also imperative to apply a feature selection method to determine the appropriate set of terminal nodes to prevent the search space from growing exponentially. The experiments showed that selecting terminal nodes based on their frequency of occurrence can help in selecting meaningful terminal nodes. However, this approach is not as robust as standard feature selection methods, such as backward sequential feature selection.  
The results obtained in this study open up several avenues for future research. One possible research direction, which was only briefly explored, would be to introduce feature construction methods into the GP algorithm so that it can automatically design new features. In addition, better measures for identifying relevant features and sub-expressions are needed, aside from frequency of occurrence. Finally, another option would be to manually construct additional terminal nodes, as such nodes usually lead to better results compared to those constructed based on the frequency of occurrence.  
Conceptualization, M.D., D.J., S.P. and L.M.; methodology, M.D., D.J., S.P. and L.M.; software, M.D., D.J., S.P. and L.M.; validation, M.D., D.J., S.P. and L.M.; formal analysis, M.D., D.J., S.P. and L.M.; investigation, M.D., D.J., S.P. and L.M.; resources, M.D., D.J., S.P. and L.M.; data curation, M.D., D.J., S.P. and L.M.; writing--original draft preparation, M.D., D.J., S.P. and L.M.; writing--review and editing, M.D., D.J., S.P. and L.M.; visualization, M.D., D.J., S.P. and L.M.; supervision, M.D., D.J., S.P. and L.M.; project administration, M.D., D.J., S.P. and L.M.; funding acquisition, M.D., D.J., S.P. and L.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Croatian Science Foundation under the project IP-2019-04-4333.  
**Data Availability Statement:** Dataset available on request from the authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.