Genetic algorithm-based optimal repertoire selection for music therapy on therapeutic effects

In recent years, music therapy has gradually become an adjunct to conventional medicine and drug therapy. In this paper, genetic algorithms are used in conventional music therapy, and they are also used to optimize the combination of repertoire in music therapy. The repertoire mining model based on an improved genetic algorithm is constructed, and the optimized search is carried out with the genetic algorithm to mine the most effective therapeutic repertoire for patients. Based on the recommendation degree analysis, the personalized recommended repertoire is customized according to the specific conditions of patients. The therapeutic effect of this paper’s music therapy based on the optimization of repertoire by genetic algorithm is examined through comparative experiments from the changes in the therapeutic effects of the experimental and control groups before and after the experiments. Before the experiment, the experimental and control groups are not identical. After the experiment, the therapeutic effect of the experimental group was greatly improved, while the control group remained basically unchanged, the total score of the therapeutic effect of the two groups differed by 50.09 points, and showed significant differences in all dimensions of the therapeutic effect. The experimental group received an improvement of 4-8 points in each dimension of the treatment effect after the experiment, while the control group received an improvement of no more than 0.5 points. Both groups and inter- and intra-group differences were statistically significant.