## 7 Conclusions and Future Work  
This present paper proposes a system that assists a user to generate tonal chord progressions, following a tonal tension profile. The tonal tension profile is a curve that modulates the path of the music played, and it depends on different musical properties. The system is able to search for different chord progressions that comply with musical constraints such as consonance, voice leading, hierarchical tension and perceptual distance. To capture all these musical properties, a function \(M\), encoded in the Tonal Interval Space (TIS), has been designed. A Genetic Programming with artificial immune system properties has been developed and integrated into ChordAIS-Gen to find multiple solutions in parallel, resulting in multiple chord progressions that follow the tonal tension profile. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Expectation** & **Usability** & **Different Solutions** \\ \hline ChordAIS-Gen & \(4.29\pm 0.50\) & \(4.15\pm 0.67\) & \(4.04\pm 0.75\) \\ \hline ChordAIS Values & \(4.17\pm 0.75\) & \(4.03\pm 0.67\) & \(3.17\pm 0.75\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Shows the percentages of satisfaction when the users finished testing the ChordAIS-Gen system. This new algorithm can be applied to problems in very different contexts, including Healthcare, energy optimization or smart cities. A listening test was carried out to demonstrate that the chord progressions proposed by ChordAIS-Gen adapt to the tonal tension profile. Most listeners appreciated the same curve that our model predicted with the function \(M\). Additionally, most listeners also rated the chord progressions proposed by ChordAIS-Gen more positively than the progressions discarded. Statistical analysis showed that our algorithm is able to propose good candidates according to the subjective ratings. Additionally, we made a comparison to evaluate if the incorporation of AIS properties to a GP algorithm can improve the performance of those algorithms individually. We would like to highlight the subjective nature of this task, the generation of music, is therefore hard to evaluate through mathematical measures. We developed the listening tests, which are subjective evaluations, and thus, whose results depend on factors such as culture, musical education or personal preferences. Those factors are not encoded in the objective function. Consequently, even when the participants involved were familiar with the Western tonal music, the results of the listening test can vary between individuals. For example, people with musical training may be more receptive to capture tonal tension due to wide exposure to Western tonal music, or can interpret dissonances as possibilities within the tonal music paradigm. In this work, we focused on the long-term and hierarchical aspects of the automatic generation of chord progressions. Furthermore, we focused on the ability to provide multiple options that are considered good candidate solutions with variety among themselves. However, the system does not learn from previous experiences to understand the users' preferences. Future work should investigate how to automatically incorporate this knowledge and combine it with the function constructed. Additionally, we will compare our results with other bioinspired algorithms and proposals based on artificial neural networks, which have demonstrated positive results in other areas [48,49]. Conceptualization by M.N.-C. and J.F.M.S.-J. ; methodology, M.N.-C. and J.F.M.S.-J. ; software, M.N.-C. and R.G.-O. ; validation, V.R.Q.L. and R.G.-O. ; data curation, V.R.Q.L. ; writing--original draft preparation, M.N.-C. and V.R.Q.L. ; writing--review and editing, [PERSON] and [PERSON]; supervision M.N.-C. and V.R.Q.L. All authors have read and agreed to the published version of the manuscript.