## 6 Evaluation
### Evaluation of the \(Gp\) with Immune Properties  
The proposed algorithm is capable of finding multiple local minima of the objective function while preserving diversity. The progressions with the highest fitness values would be considered most appropriate to be selected, as they comply with the specifications given by the users in the input. However, there are other chord progressions that can also comply with the rules. All these potential solutions can be local minima and can have different fitness values. We aim to evaluate if the chords selected by the algorithm are all considered better candidates than the chords that were discarded. For this purpose, we performed a listening test to evaluate the perceptual quality of the chords proposed by the algorithm. We run the system 10 times with different curves and selected 2 candidate chords selected by the algorithm and 2 additional chords that were rejected, sampling the objective function regularly from lower to higher fitness values. Finally, we designed a listening test that presents these 40 chord progressions ordered randomly with a picture of a curve that represents their tonal tension profile. As mentioned above, we included chords selected by the algorithm as well as chords rejected by it to validate the objective function. Each chord progression can be played multiple times before assessing it. This listening test also presents chord progressions with between 5 and 8 chords in each one. As in the previous listening test, each chord progression may have a different tree structure, with different keys. The rhythm in which the chords are played was always regular. Each chord progression was presented with a reproduction bar, and they were synthesized with a standard piano. The presentation of each chord progression in the listening test is shown Figure 5.  
The listeners were asked to rate how well the curve represents the tonal tension profile of the chord progression from 1 (Very bad) to 5 (Very good). In total, 35 people took the test, among which 8 declared no musical training, 10 considered themselves amateurs, and 17 professional musicians. We expect the chords selected by our algorithm to be rated positively by the listeners, while we expect the discarded chords to be rated negatively. Figure 6 shows the values of the subjective ratings (represented in the vertical axis) versus the fitness values (in the horizontal axis) for the chord progressions included in the listening test. To simplify the analysis, we calculated the mean of the fitness values of the chord progressions that our algorithm ranked first, second, third, and successively until the eighth. We normalized these values between 0 and 1, and these mean fitness values were represented in the horizontal line. We also calculated the mean and standard deviation of the subjective ratings of the participants. The standard deviation is represented with the vertical lines, and the points are the cut points between the objective fitness of our model and the subjective ratings of the users for each chord progression. The first four points are chord progressions discarded by the algorithm, while the rest are progressions that our algorithm considers as good candidates. According to Figure 6 all the progressions selected lie above the horizontal line that cuts through 3. This indicates that our algorithm selects chord progressions that follow the tonal tension profile presented, according to the listening participants. Most chords discarded lie below the fair rating. Therefore, according to our results, we can conclude that our algorithm is able to select options that adapt to their tonal tension profile. tension. Our goal is to show that globally, our system can capture the tonal tension profile of a chord progression according to both musicians and non-musicians perceptions. As a future work, we will perform a deeper analysis to show how the results differ between people with and without musical training.