## 1 Introduction
In this work, we propose a new system called ChordAIS-Gen, that assists the user to generate chord progressions by applying genetic programming (GP). To improve the performance of GP, we incorporate some properties derived from the Artificial Immune System [28]. At last, the system is capable of offering chord progressions with different musical properties but adapted to a tonal tension profile given as an input. To evaluate the musical features of chord progressions, we depart from the conceptual basis of [PERSON]'s model and computationally generate an objective function that captures this tonal tension profile. In this work, tonal pitch indicators inspired by [PERSON]'s model are computed using the Tonal Interval Space (TIS) by [PERSON] et al. [13], a multidimensional space based on the discrete Fourier transform, where hierarchical tonal pitch relations are expressed as distances. It is important to note that the application of the GP joined to an AIS has not been deeply explored. We aim to exploit the GP properties to create hierarchical structures that govern the chord progressions. Likewise, we also aim to take advantage of the immune properties of the AIS to generate multiple solutions different from one another. Therefore, while most state-of-the-art approaches propose a unique solution to the generation of chord progressions, the system proposed in this work is capable of proposing multiple different and good quality candidate chords. Furthermore, the lack of hierarchical structure has been a long identified problem in the field of generative music [29]. ChordAIS-Gen addresses both linear and hierarchical dependencies of chord progressions and also aims to improve the accuracy of tonal indicators by adopting the perceptually-inspired TIS. We carried out different experiments to evaluate the chords progressions created by performing a usability test with users. We also evaluated if the algorithm developed can get good solutions for our particular problem. In this case, we used a listening test to validate the candidates proposed by our system. We compared our model with two previous models: [PERSON]'s proposal [30] and ChordAIS, a previous work [24]. [PERSON]'s proposal captures similar musical properties as in this present work, but in a different topological space. With that comparison we aim to know if TIS is a good solution to capture musical properties. ChordAIS uses TIS to compute musical properties. However, the features captured differ from our work. ChordAIS measures the consonance and relatedness to the previous chord, key and harmonic function to create a chord progression. With this comparison we aim for the inclusion of new musical properties in the measure improves the results. Additionally, we compared the performance of our algorithm against the classical version of GP and AIS. Finally, we aim to validate if the function can capture the tonal tension profile of a progression. Therefore, we created a listening test and asked the participants to select and rate the curve that fits best with the tonal tension profile. The correlation between the association of the curve made by the system and the subjective ratings will support our final conclusions. This paper is organized as follows: Section 2 reviews the topological space used to represent music in this work and the musical paradigm followed. Section 3 describes the overall system with the integration of the fitness function and the bioinspired algorithm, along with the general workflow of ChordAIS-Gen. Section 4 details the musical properties captured by the function, while Section 5 exposes the technical information of the algorithm proposed. In Section 6, we give information about the experiments performed to evaluate our proposal, followed by the results and a discussion. Finally, Section 7 presents the conclusions and discusses future work.