## 3 Methods

Table 8: Comparison of route length results obtained by the GA on simulated data utilizing three seeding approaches, bold font means the best performance. **Author Contributions:** Conceptualization, E.A. and A.H.; methodology, A.H., E.A. and S.E. ; software, A.H. and S.E. ; validation, E.A. and E.E. ; formal analysis, E.A. and E.E. ; investigation, E.E. and A.H.; resources, E.A. and E.E. ; data curation, S.E. and E.A. ; writing--original draft preparation, A.H., E.A., S.E. and E.E. ; writing--review and editing, A.H., E.A., S.E. and E.E. ; visualization, E.E. and E.A. ; supervision, A.H. and S.E. ; project administration, S.E. and E.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data that support the findings of this study are openly available in TSPLIB at [[http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/)]([http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/)) (accessed on 22 January 2024). We genuinely appreciate the reviewers' voluntary efforts and are grateful for their valuable insights. The authors have no conflicts of interest to declare. All co-authors have seen and agree with the contents of the manuscript, and there are no financial interests to report. We certify that the submission is original work and is not under review at any other publication. The following abbreviations are used in this manuscript: \begin{tabular}{l l} GA & Genetic algorithm \\ NN & Nearest neighbor \\ TSP & Traveling Salesman Problem \\ IAM-TSP & Iterative Approximate Methods \\ MLRBT & Multi-Linear Regression-Based Technique \\ \end{tabular} 

## References

* [1]
* [2] [PERSON]; [PERSON]; [PERSON] Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm. _Energy_**2022**, _247_, 123437. [CrossRef]
* [3] [PERSON]; [PERSON] An improved adaptive genetic algorithm. _Proc. Shs Web Conf. Edp Sci._**2022**, _140_, 01044. [CrossRef]
* [4] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Two-level principal-agent model for schedule risk control of IT outsourcing project based on genetic algorithm. _Eng. Appl. Artif. Intell._**2020**, _91_, 103584. [CrossRef]
* [5] [PERSON]; [PERSON] A novel multi-parent order crossover in genetic algorithm for combinatorial optimization problems. _Comput. Ind. Eng._**2019**, _133_, 267-274. [CrossRef]
* [6] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Enhancing genetic algorithms using multi mutations: Experimental results on the travelling salesman problem. _Int. J. Comput. Sci. Inf. Secur._**2016**, _14_, 785. * [7] [PERSON]; [PERSON]; [PERSON]; [PERSON] Simulated annealing genetic algorithm based schedule risk management of IT outsourcing project. _Math. Probl. Eng._**2017**, _2017_, 6916575. [CrossRef]
* [8] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Choosing mutation and crossover ratios for genetic algorithms--A review with a new dynamic approach. _Information_**2019**, _10_, 390. [CrossRef]
* [9] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A new population seeding technique for permutation-coded Genetic Algorithm: Service transfer approach. _J. Comput. Sci._**2014**, \(5\), 277-297. [CrossRef]
* [10] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Performance assessment over heuristic population seeding techniques of genetic algorithm: Benchmark analyses on traveling salesman problems. _Int. J. Appl. Eng. Res. (Ijarer) Res. India Publ._**2013**, \(8\), 1171-1184. * [11] [PERSON] Genetic algorithm and a double-chromosome implementation to the traveling salesman problem. _Appl. Sci._**2019**, \(1\), 1397. [CrossRef]
* [12] [PERSON]; [PERSON] [PERSON] On enhancing genetic algorithms using new crossovers. _Int. J. Comput. Appl. Technol._**2017**, _55_, 202-212. [CrossRef]
* [13] [PERSON]; [PERSON]; [PERSON] A review on genetic algorithm: Past, present, and future.