## 9 Conclusions and Future Work  
This work introduces the evolution of synthesisable HDL codes for a single-input, multi-output MSD using GE and two parent selection methods: TS and LS. Although these parent selection methods and GE are well-established techniques, they are combined here to evolve the FSM of a real-life MSD, which is complex and hard to evolve and speaks to the novelty of the presented system. The evolved MSD is applied to a practical vending machine scenario, covering the entire process from product selection through payment to delivery. Using the evolved MSD designs in a vending machine scenario, the study validates the effectiveness of the evolutionary methods in generating practical, real-world solutions. It also shows the potential for these methods in other complex systems requiring similar state management capabilities, such as robotics. The results demonstrate that LS significantly outperforms TS in evolving a sequential Mealy machine in terms of success rate and computational efficiency. The Gold standard and evolved circuits are synthesised to validate the practical application using GPDK at 45 nm, 90 nm, and 180 nm. These syntheses confirm that the circuits evolved to represent a significant step forward in automated chip design and manufacturing. Notably, at least one evolved circuit outperforms the Gold Circuit using either TS or LS. This study is the first to evolve synthesisable HDL codes for MSDs. Future work will focus on evolving more complex circuits, such as multi-input and multi-output MSDs. In addition, another future direction will consider exploring the systematic hyperparameter tuning techniques to optimise evolutionary ML approaches, such as GE, which will likely improve overall system performance. We will also investigate using some other implementations of GE than GRAPE, such as Grammatical Algorithms in C++ for Evolution (GRACE), where the LS can be compared with the results of the approach used here. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a18060345/s1](https://www.mdpi.com/article/10.3390/a18060345/s1)]([https://www.mdpi.com/article/10.3390/a18060345/s1](https://www.mdpi.com/article/10.3390/a18060345/s1)), Data_Set_Paper: Figure showing dataset generation used for training the model; Lexicase_Selection_Experiment_1: Mean of average fitness across generations for first experiment using Lexicase parent selection; Lexicase_Selection_Experiment_2: Mean of average fitness across generations for second experiment using Lexicase_Selection_Experiment_3: Mean of average  
across generations for sixth experiment using Tournament parent selection; Grammar.txt: BNF Grammar used for evolution; Human_Designed_Module.sv: SV code of human-designed module in SV file format; Human_Designed_Module.txt: SV code of human-designed module in text file format; Evolved_Solution.sv: SV code of machine-designed module in SV file format; Evolved_Solution.txt: SV code of machine-designed module in text file format; Dataset.txt: 12-bit 16 train and test vectors used for training and test purposes. Conceptualisation, B.M., R.S., A.Y., D.M.D. and C.R. ; methodology, B.M., A.Y., R.S. and C.R. ; formal analysis, B.M., R.S. and C.R. ; data curation, B.M. ; writing--original draft preparation, B.M. ; writing--review and editing, R.S., A.Y., D.M.D. and C.R. ; visualization, B.M., R.S. and C.R. ; supervision, R.S., A.Y., D.M.D. and C.R. ; project administration, D.M.D., R.S. and C.R. ; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript. This work was supported by the Research Ireland (Science Foundation Ireland (SFI) before) grant 16/IA/4605. The dataset used in this work can be accessed at Supplementary Materials. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.