Next, the results obtained from different executions carried out when solving the proposed optimization problem will be shown.
For this execution, the parameters b=p=8 were used. For this execution, 6600 generations of the genetic algorithm were used.
The Pareto front obtained with the genetic algorithm is shown below.
The following video is the extreme of the Pareto front, this song obtained is the minimum value for the third component of the function F. It can be seen that there is a pattern in the harmony and in the melody.
The following video shows one end of the Pareto front, where the highest value for joy is obtained; that is, the first component of the function F.
A solution outside the extremes of the Pareto front is shown below.
For this run, the parameters b=p=8 were used, with 11,200 generations.
Below is a video with the Pareto front obtained by the genetic algorithm.
The following video shows the end of the Pareto front corresponding to the third objective, where the rhythmic, harmonic pattern and the classical genre are obtained with the machine learning model.
The following video shows the result obtained corresponding to the extreme where the maximum happiness is obtained.
In the following video you can see the result obtained with the maximum minimalism for the given parameters.
In the following video you can see the change in the rhythmic pattern, where there is greater freedom of movement between the notes.
For this run p=4 b=8 were used, with 21,700 generations.
Below is a video with the Pareto front obtained by the genetic algorithm for this execution.
The following video shows an extreme obtained, which corresponds to the third objective of the function F; that is, the pattern and the musical genre.
On the other hand, the following video shows the song with maximum happiness, you can see that there are ornaments between the bars.
In the following video you can see the extreme corresponding to maximum minimalism, as can be seen in the harmonic part, whole notes are maintained.