# The Use of Thinking Visualization Techniques in College Teaching Based on Improved Genetic Algorithms

Authors: Luyan Su
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

Current educational resources do not maximize energy efficiency, and scientific and proven teaching methods are necessary for today’s university education to help achieve the integration of teaching resources and improve teaching quality. This study discusses the application of artificial intelligence and thinking visualization technology in college education and teaching, firstly introduces the advantageous role of genetic algorithm in artificial intelligence and the energy efficiency of thinking visualization technology, then conducts a research analysis, introduces genetic algorithm as technical support in teaching and learning, uses thinking visualization technology as a tool, aims to study the application of thinking visualization technology in college teaching, and expects to use genetic algorithm and thinking visualization techniques to improve teaching effectiveness. The experimental results show that genetic algorithm and thinking visualization technology can effectively optimize the co-giving of educational services, reduce the workload of teachers, while reducing the cost of learning, enhance the learning effect and experience, and improve students' efficient thinking.


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## Body

## 1. Introduction

With the continuous improvement of the economic level, the curriculum is constantly changing in order to adapt to the current development, from the initial subject education to quality education. In the development process of the overall quality education, vocal music occupies an important position, and the educational nature of music is becoming more and more prominent. Vocal music teaching is the main content of music teaching. With the continuous exchange of Chinese and foreign cultures, we found that there are many excellent musical skills that deserve attention. Looking at the current music market, there are many outstanding music representatives, but there are still many deficiencies. For example, when teachers are teaching, they focus on singing songs, ignoring the understanding of the lyrics and songs themselves. Their grasp of lyrics and music remains on the surface, and they cannot grasp the core things. This study aims to study the application of thinking visualization technology in vocal music teaching by integrating and improving genetic algorithm, and expects to use genetic algorithm and thinking visualization technology to improve the effect of vocal music teaching.In the development of the 21st century, we are no longer limited only by the economy. Vocal music education also adds color to the world. Vocal music teaching has far-reaching significance for the development of music education in the future. It is necessary to change the traditional concept of education, strengthen the comprehensive quality education of students, and provide a good employment orientation for future employment. The combination of vocal music teaching and thinking vision can improve the tacit understanding between teachers and students. This improves the overall level of vocal music teaching and singing and makes vocal music teaching systematic and scientific.There is not a lot of intersection between the two disciplines of computing and music. But it is still a relatively new point of view in vocal music teaching. Combining the two provides a new research direction. In this study, vocal music education is intuitive and systematized, so that the learning of vocal music classroom is more abundant.


## 2. Related Work

Genetic algorithm has a very great application prospect at present. To understand a brief understanding of its application direction, Tavakkoli-Moghaddam et al. proposed a genetic algorithm (GA) for the redundancy assignment problem of series-parallel systems. Experiments show that genetic algorithm is an effective method to solve this kind of problem. Finally, he presents computational results for typical scenarios and discusses the robustness of the proposed algorithm [1]. Dawid and Kopel used the idea of artificial adaptive agents in economic theory. He used a genetic algorithm (GA) to simulate the learning behavior in the system. The striking differences between the results of different setups are explained. Finally, he explained the relationship between the encoding and convergence properties of GA [2]. Volkanovski proposed a new method to optimize the maintenance scheduling of power system generator sets. The maintenance plan minimizes the risk by minimizing the annual value of the expected loss of load. He also compared the results gained with those obtained by approximate methods. The results show that the maintenance schedule obtained with the new method improves the reliability of the power system compared to the results of the approximate method [3]. Long and Wu studied a global optimization method combining genetic algorithm and the Hooke–Jeeves method to solve a class of constrained optimization problems. He introduces the method of the second penalty factor and the law of precise penalty factor. When dealing with constrained optimization problems, a common approach is to add constraints as penalty terms to the objective function. “Punishment” is a very vivid name, which means that when the optimization process iterates beyond the constraints, it is punished, or negative feedback. Then, the genetic algos were combined with the Hooke–Jeeves approach to tackle the conversion followed by the postoptimization process. This methodology greatly reduces the genetic model’s accuracy and its speed of convergence [4]. Yoshitomi et al. extended genetic algorithms to uncertain environments. The method was applied to the stochastic optimal assignment problem. He proved that the solution of GAUCE agrees well with the solution [5]. To solve the linear and U-shaped pipeline balance problem, Alavidoost et al. proposed an improved genetic algorithm. He selected one out of five rules of failure in the algorithm to enhance the performance of the hereditary method. Furthermore, he selected fuzzy controllers under pressure for better implementation of one out of five success rules in genetic algorithms. The outcome demonstrates the outstanding quality of his presented algorithm [6]. Metawa et al. proposed an intelligent genetic algorithm (GA)-based model. It is used to constrain bank borrowing and lending decision making. It seeks dynamic lending decisions by minimizing bank profit and also bank default probability. It allows banks to reduce their loan screening time by 12–50% [7]. Gong et al. proposed an ensemble-based genetic algorithm. He defined an ensemble-based Pareto dominance relation to modify the fast nondominated sorting method in NSGA-II. Numerical results demonstrate the superiority of the method [8]. Fetouh and Zaky proposed a new method for coordinating the stability of power systems. The proposed method is designed to minimize the computation time, reduce the storage capacity required for optimization problems, and improve the stability performance of the power system [9]. Rajeswari and Neduncheliyan have proposed a fault-tolerant technology based on clumps which uses genuine arithmetic. The genetic algorithm is used to select a set of standby knots based on sponsorship coverage and residual energy parameters. Simulation results show that his proposed technique minimizes energy loss and overhead [10]. Prichard’s aim was to examine the effectiveness of music teaching among teachers enrolled in introductory music education courses. He used a sequential explanatory mixed methods design and divided it into two parts. Mixed methods analysis revealed that music education majors had higher beliefs about music teaching efficacy [11]. Steele Royston examined the significance of human connections within the context of the lesson and offered tips for proactive faculty to better their connections. Connecting with students as a musical teacher leads them to understand a whole lot with the arts of playing music. It also teaches them something about the skill of what it means to be a sympathetic and loving person [12]. The introduction of genetic algorithm and vocal music teaching in these researches is in place, but the practicability is not high due to the strong professionalism.


## 3. Methods

### 3.1. Genetic Algorithm

Genetic algorithm refers to the population of solution sets, and the population is composed of several individuals encoded by genes. In essence, a genetic algorithm is an optimization algorithm, which is a random search algorithm that uses the idea of natural selection and biological evolution to search for the optimal solution in the search space. In the process of vocal music teaching, the genetic algorithm can integrate the characteristics of students, optimize the vocal music teaching method, improve the students’ vocal music mastery ability, and better serve the vocal music classroom.Evolutionary computing is a stochastic optimization method that simulates the genetic mechanism of the animal kingdom, with the idea of “survival of the fittest.” Therefore, genetic manipulation and natural selection become an important part when using evolutionary thinking to solve target optimization problems. Evolutionary algorithms iteratively generate multiple solutions to each problem during the optimization process and continue to generate better solutions. The optimization function is called the fitness function. Each solution is called an individual, and all individuals in each generation form a group. The fitness value of individuals in each group is different. With iteration, it obtains better individuals through certain evolutionary strategies, such as crossover and mutation. When the algorithm terminates, the individual with the best fitness value in the entire population is selected as the solution to the problem [13]. It is shown in Figure 1.Figure 1
Basic framework of genetic algorithm.Evolutionary algorithms are random search methods. Compared with other enumeration techniques and heuristic search techniques, the global optimal solution probability of this problem is high. The evaluation information of the objective function is used to make it actionable and general. It can be operated with massively parallel computers and can be easily combined with other methods. At present, evolutionary computing mainly includes genetic algorithm, evolutionary strategy, and evolutionary planning. In the process of vocal music teaching, the evolutionary algorithm can be combined to find the most suitable teaching method and improve the teaching efficiency and effect.(1)minga|a∈Q.Formula (1) represents the function expression of the function optimization model, where a represents the decision variable, ga represents the objective function, and Q represents the spatial subset.(2)minga.st∗pa≥0.Formula (2) represents the decision variable inequality.(3)Q=a∈Ws|pa≥0.Formula (3) represents the feasible region of the decision solution, which is the set of all solutions in layman’s terms.In order to get the optimal solution for the region, we need to optimize the model.(4)∀a∈Q∩a∈Ws|∑a1−a∗2<α.When formula (4) satisfies ga∗≤ga, we say that the model has an optimal solution. Figure 2 shows the basic flow chart of the algorithm.Figure 2
Model algorithm flow demonstration.Various encoding methods have emerged today. The most commonly used genetic algorithm is binary coding. In binary, all symbols consist of “0” and “1.”(5)β=Tmax−Tmin2p−1.Formula (5) represents the precision of binary encoding, where T represents the range of values and p represents the symbol length of the encoding.(6)k=Tmin+∑o=1pwo∗2o−2∗Tmax−Tmin2p−1.Formula (6) represents the decoding function expression when the encoded length is k.(7)fp=wp,fp=wp+1⊕wp.(8)wp=fp,wp=wp+1⊕fp..Formulas (7) and (8) represent binary codes under different Gray codes.In the genetic algorithm, we usually use the fitness function to judge the situation of the individual. The fitness function satisfies the generality, so the calculation steps can be reduced in the actual use process.(9)Fitnessga=ga.Formula (9) represents the functional expression of the maximal optimization problem.(10)Fitnessga=−ga.Formula (10) represents the functional expression for the minimal optimization problem.For multi-objective optimization problems, researchers have proposed different evolutionary multi-objective research algorithms. In the process of solving the problem, we have two goals to achieve. First, the frontier of the approximate Pareto optimal solution set should be as close as possible to the frontier of the true Pareto optimal solution set. The closeness of the two fronts reflects the convergence of the algorithm. Second, the target vectors corresponding to the Pareto optimal solution set should be distributed as widely and evenly as possible, which reflects the diversity of the knowledge set. Multi-objective optimization can be described by the following formula:(11)maxGi=g1ig2i,⋯,gxiy,s.y.i∈θ..If the Pareto boundary resolution system is able to fulfill the condition, it means that it is practicable for the algorithm to address the MOP issue.(12)minθ=Gi=g1i,g2i,⋯,gniysy.pti≤0,t=1,2⋯ztei=0,e=1,2⋯,q.In the formula,i=i1，i2,⋯,in represents the n-dimensional decision vector, and giis the objective function. pti≤0,t=1,2⋯z defines the inequality constraint for z, and tei=0,e=1,2⋯,q defines the equality constraint function for q.As can be seen from the above formula, the MOP problem is difficult to determine a unique solution, which is different from a single objective. In addition, the solution process of this algorithm is very complicated and difficult due to many constraints.Standard genetic algorithms are optimization algorithms for individual objectives. The fitness function of an individual is usually the objective function of optimization. The result of the genetic algorithm is to obtain the individual with the best fitness. In reality, many problems are multi-objective problems, that is, multiple values of the problem solution need to be optimized simultaneously. For multi-objective optimization problems, a solution may be better for one objective and worse for others. The two keys to multi-objective optimization using genetic algorithms are: (1) How to carry out fitness value distribution and individual selection, so that individuals in the population converge to the nondominated solution set; (2) How to keep the population multiple and avoid premature stock convergence, so that the individuals in the population are evenly distributed on the Pareto frontier. Around these two problems, researchers have proposed different types of multi-objective evolutionary algorithms.(13)minfaby|ci,d∗=max0≤x≤1gxa−dx∗cxi.Formula (13) represents the Chebyshev function expression, in order to avoid the case of division by zero, when cx = 0, cx = 10−6.(14)minfzxca|dc,k∗=p1c+ρp2c,p1c=Ga−k∗tdcdc,p2c=Ga−k∗−p1cdcdc.The above 3 function expressions belong to the boundary intersection method, whereSubjectto a∈β.(15)Fitnessga=ga+Ymin,0..Among them,Ymin belongs to the set minimum value, and usually its value range is ga+Ymin>0.(16)Fitnessga=Ymax−ga,0..Among them,Ymax belongs to the set maximum value, and usually its value range is Ymax−ga>0.(17)Fitnessga=11+k−ga.Formula (17) represents the maximum optimization problem function expression, where k−ga≥0.(18)Fitnessga=11+k+ga.Formula (18) represents the minimum optimization problem function expression, where k+ga≥0.According to the algorithm of the objective optimization problem, we can use it in the vocal music teaching classroom. We regard the different characteristics of students as different optimization objectives. Using multi-objective optimization, we can find a teaching algorithm suitable for all students, so that the results of acoustic teaching can be achieved.


### 3.2. Vocal Music Teaching

As an ancient art, vocal music is a combination of music and language. Since the human voice is added to the process of vocal music performance, the performance effect of vocal music is more moving [14]. Vocal music teaching practice activities are social, and teaching methods are unique, cooperative, and practical. Articulation is the most important thing during a vocal performance. It directly determines the effect of the performance to some extent. In related works, it has been described as “the clearness of the word is one must; the purity of the cavity is the second; the board is the three”. “Banzheng” refers to the need of pronouncing clearly. It should first be the correct character, so that the audience can clearly hear what is being sung. This also shows the importance of language in vocal music. However, according to the current market situation, it is not difficult to find that it is still a problem that many singers need to face. This problem is mainly caused by the lack of basic vocal skills, the lack of research on the standardization of Chinese pinyin, and the failure to connect the sound of the word and the melody line of the music. In response to these problems, relevant scholars believe that they can be solved from the aspects of phoneme and word orthography. Figure 3 shows the structure of audio data processing.Figure 3
Audio data processing structure.Breathing is the foundation and motivation of singing and vocalization. From a professional level, the breath directly affects the performance of the performance. Exhalation is the expulsion of air out of the body. Inhalation means taking air into the body. These two actions are opposite and happen in succession at the same time. If there is excessive breathing during singing, it will reduce the integrity of the work. In order to improve the level of the work, it is necessary to train the breathing method when singing. In general, it is necessary to consciously control the breathing rate according to the length of the phrase to prevent the organ from becoming rigid [15]. Figure 4 shows a training diagram of the vocal pronunciation system structure.Figure 4
Vocal articulation system structure training.If vocal skills are directly transmitted in language, it will be very obscure and difficult to understand. If it can be combined with visual thinking when explaining, it will make the skills more vivid, arouse students’ interest, and make students master more accurately. Therefore, combining teaching with visual thinking is one of the most effective teaching methods.


### 3.3. Visual Thinking

Thinking refers to thought or thinking, which is a high-level physiological phenomenon. It belongs to the biochemical reaction in the brain and is a high-level stage of human cognitive process. In the process of vocal music singing, it is also necessary to carry out thinking activities, and it is necessary to use thinking to lead one’s own consciousness and complete the work. In vocal singing, there are action thinking, image thinking, convergent thinking, and divergent thinking. In the practice of singing training, action thinking and image thinking play an important auxiliary role in the improvement of our vocal music practice. For some specific song singing behavior, the singer needs to analyze it with the help of thinking. For example, we need to use hearing to judge the beauty of sound. This is the most important part of thinking. For beginners in vocal music, it is necessary to strengthen them. This makes it gradually form a correct sound image thinking, which lays a good foundation for future learning. Figure5 shows the simple vocal practice structure.Figure 5
Simple vocal practice structure.Visualization is a means of processing images and data using computer means. It is a way to display information more intuitively. The technology was first used in the computer field. In terms of how it works, visualization is the process of turning information into images. Thinking visualization is the reflection of invisible ways of thinking through images. In fact, we often use its methods in daily life. Thinking visualization refers to presenting thinking methods and thinking paths that were otherwise invisible using a series of graphic techniques to make them clearly visible. In fact, we often use its methods in our daily life. For example, we often say we can do it by bending the calf, but we do not know which way to bend it. At this time, if someone makes a demonstration, it can be displayed intuitively. This is a process of thinking visualization. This method is also very common in vocal music teaching. Figure6 shows the basic structure diagram of visualization technology.Figure 6
Basic structure of the visualization technique.


## 3.1. Genetic Algorithm

Genetic algorithm refers to the population of solution sets, and the population is composed of several individuals encoded by genes. In essence, a genetic algorithm is an optimization algorithm, which is a random search algorithm that uses the idea of natural selection and biological evolution to search for the optimal solution in the search space. In the process of vocal music teaching, the genetic algorithm can integrate the characteristics of students, optimize the vocal music teaching method, improve the students’ vocal music mastery ability, and better serve the vocal music classroom.Evolutionary computing is a stochastic optimization method that simulates the genetic mechanism of the animal kingdom, with the idea of “survival of the fittest.” Therefore, genetic manipulation and natural selection become an important part when using evolutionary thinking to solve target optimization problems. Evolutionary algorithms iteratively generate multiple solutions to each problem during the optimization process and continue to generate better solutions. The optimization function is called the fitness function. Each solution is called an individual, and all individuals in each generation form a group. The fitness value of individuals in each group is different. With iteration, it obtains better individuals through certain evolutionary strategies, such as crossover and mutation. When the algorithm terminates, the individual with the best fitness value in the entire population is selected as the solution to the problem [13]. It is shown in Figure 1.Figure 1
Basic framework of genetic algorithm.Evolutionary algorithms are random search methods. Compared with other enumeration techniques and heuristic search techniques, the global optimal solution probability of this problem is high. The evaluation information of the objective function is used to make it actionable and general. It can be operated with massively parallel computers and can be easily combined with other methods. At present, evolutionary computing mainly includes genetic algorithm, evolutionary strategy, and evolutionary planning. In the process of vocal music teaching, the evolutionary algorithm can be combined to find the most suitable teaching method and improve the teaching efficiency and effect.(1)minga|a∈Q.Formula (1) represents the function expression of the function optimization model, where a represents the decision variable, ga represents the objective function, and Q represents the spatial subset.(2)minga.st∗pa≥0.Formula (2) represents the decision variable inequality.(3)Q=a∈Ws|pa≥0.Formula (3) represents the feasible region of the decision solution, which is the set of all solutions in layman’s terms.In order to get the optimal solution for the region, we need to optimize the model.(4)∀a∈Q∩a∈Ws|∑a1−a∗2<α.When formula (4) satisfies ga∗≤ga, we say that the model has an optimal solution. Figure 2 shows the basic flow chart of the algorithm.Figure 2
Model algorithm flow demonstration.Various encoding methods have emerged today. The most commonly used genetic algorithm is binary coding. In binary, all symbols consist of “0” and “1.”(5)β=Tmax−Tmin2p−1.Formula (5) represents the precision of binary encoding, where T represents the range of values and p represents the symbol length of the encoding.(6)k=Tmin+∑o=1pwo∗2o−2∗Tmax−Tmin2p−1.Formula (6) represents the decoding function expression when the encoded length is k.(7)fp=wp,fp=wp+1⊕wp.(8)wp=fp,wp=wp+1⊕fp..Formulas (7) and (8) represent binary codes under different Gray codes.In the genetic algorithm, we usually use the fitness function to judge the situation of the individual. The fitness function satisfies the generality, so the calculation steps can be reduced in the actual use process.(9)Fitnessga=ga.Formula (9) represents the functional expression of the maximal optimization problem.(10)Fitnessga=−ga.Formula (10) represents the functional expression for the minimal optimization problem.For multi-objective optimization problems, researchers have proposed different evolutionary multi-objective research algorithms. In the process of solving the problem, we have two goals to achieve. First, the frontier of the approximate Pareto optimal solution set should be as close as possible to the frontier of the true Pareto optimal solution set. The closeness of the two fronts reflects the convergence of the algorithm. Second, the target vectors corresponding to the Pareto optimal solution set should be distributed as widely and evenly as possible, which reflects the diversity of the knowledge set. Multi-objective optimization can be described by the following formula:(11)maxGi=g1ig2i,⋯,gxiy,s.y.i∈θ..If the Pareto boundary resolution system is able to fulfill the condition, it means that it is practicable for the algorithm to address the MOP issue.(12)minθ=Gi=g1i,g2i,⋯,gniysy.pti≤0,t=1,2⋯ztei=0,e=1,2⋯,q.In the formula,i=i1，i2,⋯,in represents the n-dimensional decision vector, and giis the objective function. pti≤0,t=1,2⋯z defines the inequality constraint for z, and tei=0,e=1,2⋯,q defines the equality constraint function for q.As can be seen from the above formula, the MOP problem is difficult to determine a unique solution, which is different from a single objective. In addition, the solution process of this algorithm is very complicated and difficult due to many constraints.Standard genetic algorithms are optimization algorithms for individual objectives. The fitness function of an individual is usually the objective function of optimization. The result of the genetic algorithm is to obtain the individual with the best fitness. In reality, many problems are multi-objective problems, that is, multiple values of the problem solution need to be optimized simultaneously. For multi-objective optimization problems, a solution may be better for one objective and worse for others. The two keys to multi-objective optimization using genetic algorithms are: (1) How to carry out fitness value distribution and individual selection, so that individuals in the population converge to the nondominated solution set; (2) How to keep the population multiple and avoid premature stock convergence, so that the individuals in the population are evenly distributed on the Pareto frontier. Around these two problems, researchers have proposed different types of multi-objective evolutionary algorithms.(13)minfaby|ci,d∗=max0≤x≤1gxa−dx∗cxi.Formula (13) represents the Chebyshev function expression, in order to avoid the case of division by zero, when cx = 0, cx = 10−6.(14)minfzxca|dc,k∗=p1c+ρp2c,p1c=Ga−k∗tdcdc,p2c=Ga−k∗−p1cdcdc.The above 3 function expressions belong to the boundary intersection method, whereSubjectto a∈β.(15)Fitnessga=ga+Ymin,0..Among them,Ymin belongs to the set minimum value, and usually its value range is ga+Ymin>0.(16)Fitnessga=Ymax−ga,0..Among them,Ymax belongs to the set maximum value, and usually its value range is Ymax−ga>0.(17)Fitnessga=11+k−ga.Formula (17) represents the maximum optimization problem function expression, where k−ga≥0.(18)Fitnessga=11+k+ga.Formula (18) represents the minimum optimization problem function expression, where k+ga≥0.According to the algorithm of the objective optimization problem, we can use it in the vocal music teaching classroom. We regard the different characteristics of students as different optimization objectives. Using multi-objective optimization, we can find a teaching algorithm suitable for all students, so that the results of acoustic teaching can be achieved.


## 3.2. Vocal Music Teaching

As an ancient art, vocal music is a combination of music and language. Since the human voice is added to the process of vocal music performance, the performance effect of vocal music is more moving [14]. Vocal music teaching practice activities are social, and teaching methods are unique, cooperative, and practical. Articulation is the most important thing during a vocal performance. It directly determines the effect of the performance to some extent. In related works, it has been described as “the clearness of the word is one must; the purity of the cavity is the second; the board is the three”. “Banzheng” refers to the need of pronouncing clearly. It should first be the correct character, so that the audience can clearly hear what is being sung. This also shows the importance of language in vocal music. However, according to the current market situation, it is not difficult to find that it is still a problem that many singers need to face. This problem is mainly caused by the lack of basic vocal skills, the lack of research on the standardization of Chinese pinyin, and the failure to connect the sound of the word and the melody line of the music. In response to these problems, relevant scholars believe that they can be solved from the aspects of phoneme and word orthography. Figure 3 shows the structure of audio data processing.Figure 3
Audio data processing structure.Breathing is the foundation and motivation of singing and vocalization. From a professional level, the breath directly affects the performance of the performance. Exhalation is the expulsion of air out of the body. Inhalation means taking air into the body. These two actions are opposite and happen in succession at the same time. If there is excessive breathing during singing, it will reduce the integrity of the work. In order to improve the level of the work, it is necessary to train the breathing method when singing. In general, it is necessary to consciously control the breathing rate according to the length of the phrase to prevent the organ from becoming rigid [15]. Figure 4 shows a training diagram of the vocal pronunciation system structure.Figure 4
Vocal articulation system structure training.If vocal skills are directly transmitted in language, it will be very obscure and difficult to understand. If it can be combined with visual thinking when explaining, it will make the skills more vivid, arouse students’ interest, and make students master more accurately. Therefore, combining teaching with visual thinking is one of the most effective teaching methods.


## 3.3. Visual Thinking

Thinking refers to thought or thinking, which is a high-level physiological phenomenon. It belongs to the biochemical reaction in the brain and is a high-level stage of human cognitive process. In the process of vocal music singing, it is also necessary to carry out thinking activities, and it is necessary to use thinking to lead one’s own consciousness and complete the work. In vocal singing, there are action thinking, image thinking, convergent thinking, and divergent thinking. In the practice of singing training, action thinking and image thinking play an important auxiliary role in the improvement of our vocal music practice. For some specific song singing behavior, the singer needs to analyze it with the help of thinking. For example, we need to use hearing to judge the beauty of sound. This is the most important part of thinking. For beginners in vocal music, it is necessary to strengthen them. This makes it gradually form a correct sound image thinking, which lays a good foundation for future learning. Figure5 shows the simple vocal practice structure.Figure 5
Simple vocal practice structure.Visualization is a means of processing images and data using computer means. It is a way to display information more intuitively. The technology was first used in the computer field. In terms of how it works, visualization is the process of turning information into images. Thinking visualization is the reflection of invisible ways of thinking through images. In fact, we often use its methods in daily life. Thinking visualization refers to presenting thinking methods and thinking paths that were otherwise invisible using a series of graphic techniques to make them clearly visible. In fact, we often use its methods in our daily life. For example, we often say we can do it by bending the calf, but we do not know which way to bend it. At this time, if someone makes a demonstration, it can be displayed intuitively. This is a process of thinking visualization. This method is also very common in vocal music teaching. Figure6 shows the basic structure diagram of visualization technology.Figure 6
Basic structure of the visualization technique.


## 4. Application Experiment

Vocal Learning Data Collection: in order to investigate the current state of vocal music learning, we first need to conduct a critical survey of student learning. In this experiment, we used a questionnaire survey to investigate the vocal music students in place A. The details are as follows:In order to analyze the students’ situation, we have investigated the students’ comprehensive quality of vocal music. According to the actual situation of students, their comprehensive quality is divided into four grades. According to the data in Table1, there are 37 students with good overall quality, accounting for 27.6% of the total number of respondents. There are 31 students with average comprehensive quality, accounting for 23.1% of the total number of respondents. There are 46 students who have passed the comprehensive quality, accounting for 34.3% of the total number of respondents. There are 20 students with unqualified comprehensive quality, accounting for 15% of the total number of respondents. According to the survey data, there is a big gap in the comprehensive quality of students. Generally speaking, the level is relatively average, the students’ cultural background is not very high, and the cultural background and accomplishment are relatively weak.Table 1
Analysis of students’ comprehensive quality survey.
CategoryNumber of peopleProportionGood3727.6Moderate3123.1Pass4634.3Not passing2015According to the data in Table2, we have investigated and analyzed the students’ vocal music foundation and mastery of skills. According to the data, there are 6 people with the ability to teach acoustics, accounting for 4.5% of the total number of respondents. There are 23 people with singing experience, accounting for 17.2% of the total number of respondents. There are 39 people who can master the knowledge and skills proficiently, accounting for 29.1% of the total surveyed. 66 people have basic knowledge and skills, accounting for 49.3% of the total surveyed. According to the data, the proportion of students with basic mastery is the highest, and the proportion of students with vocal music teaching ability is the lowest. It can be seen from this that students generally lack practical experience and have fewer opportunities for vocal music activities. According to the actual situation, most of the students pay attention to the learning skills, lack the perception of the works, and have a low degree of completion.Table 2
Analysis of the mastery of basic vocal knowledge and basic skills.
CategoryNumber of peopleProportionAbility to teach vocal music64.5Experience in singing2317.2Proficiency in3929.1Basic mastery6649.3Experimental Data: the theme of this study is to improve the effect of vocal music teaching with the help of a genetic algorithm. In order to investigate the effect of genetic algorithm, we conduct a brief data analysis of the results of different calculation methods of genetic algorithm. The details are as follows:From the experimental data in Table3, we can see that although the algorithm NSGA-II is stronger than the algorithm USMOEA in terms of breadth, the original intention of the experimental design is to obtain a nondominated solution set that is closer to the Pareto front and more uniformly distributed. Then from the data in the table, the expected solution goal has been basically achieved. The algorithm can obtain a nondominated solution.Table 3
Algorithm NSGA-II and algorithm USMOEA on the M-measure of the test function.
M-measureGroupZDT1ZDT2ZDT3ZDT4ZDT6USMOEABEST1.29841.03201.75431.21980.7816MEAN1.05690.05991.53280.66920.7569WORST0.81490.81741.05420.54730.6736NSGA-IIBEST1.41591.32982.30291.30360.8851MEAN1.25781.07981.73190.81320.8269WORST0.98990.96381.29780.70560.7025From the experimental data in Table4, we can see that the values of ZDT1, ZDT2, and ZDT3 are compared. Algorithm NSGA-II is weaker than algorithm UCMOEA in terms of worst data, mean, or best data. According to the data obtained by ZDT4, we can see that the algorithm NSGA-II is stronger, and the algorithm UCMOEA is relatively weak. For the experimental data of ZDT6, we can see that the algorithm NSGA-II is better than the algorithm UCMOEA in the mean and worst cases, but the algorithm UCMOEA is stronger than the algorithm NSGA-II in the optimal data.Table 4
Algorithm NSGA-II and algorithm UCMOEA on the S-measure of the test function.
S-measureGroupZDT1ZDT2ZDT3ZDT4ZDT6UCMOEABEST0.00270.00210.00990.01630.0011MEAN0.02140.00310.01960.01960.0039WORST0.02990.01350.02130.02350.0047NSGA-IIBEST0.00360.00400.01000.00480.0016MEAN0.03190.00750.02360.00890.0035WORST0.07360.01200.06390.01320.0048Attitudes of Students toward Vocal Music Courses: with the continuous progress of national education, the education curriculum has become more and more perfect, and the requirements for comprehensive quality have become more and more strict. In addition to the strict requirements for subject education, we also pay more and more attention to art courses, incorporating the comprehensive development of students into the educational requirements. In order to explore students’ attitudes toward vocal music courses, we conducted a brief analysis of students’ attitudes toward vocal music courses in place A. The details are as follows:To explore why students take vocal lessons, we have grouped them into four broad categories. According to the data in Figure7, there are 22 people who choose vocal music courses purely for learning needs, accounting for 16% of the total number of respondents. 19 people chose vocal music courses because of their personal hobbies, accounting for 14% of the total surveyed. There are 31 people who choose vocal music courses to pave the way for later work, accounting for 23% of the total surveyed. There are 63 people who choose vocal music courses to learn music knowledge and vocal skills, accounting for 47% of the total surveyed. According to this data, most students choose vocal music classes to learn vocal skills. This shows the importance of vocal skills in vocal music courses.Figure 7
Analysis of students’ attitudes toward vocal courses.To understand the school’s vocal learning climate, we surveyed students’ personal perceptions. According to the survey data, 76 people believe that the atmosphere for learning vocal music in schools is very good, accounting for 57% of the total number of respondents. 19 people think that the atmosphere of learning vocal music in schools is average, accounting for 36% of the total number of respondents. 14 people think that the atmosphere for learning vocal music in schools is relatively poor, accounting for 11% of the total number of respondents. There are also 6 people who do not know about the school’s vocal music atmosphere, accounting for 5% of the total number of respondents. According to the survey data, most students believe that the learning atmosphere is good, indicating that students are generally more serious about the course.In order to improve the teaching effect, we use different genetic algorithms to optimize the design. As can be seen from the data in Figure8, from the perspective of breadth, the effect of the algorithm UCMOEA is not particularly good. But the original intention of the experiment is to obtain a nondominated solution set that is close to the Pareto front and has a more uniform distribution. The data in Table 4 show that the expected solution goal has been achieved. Algorithm UCMOEA can obtain the nondominated solution set with a more uniform distribution and closer to the Pareto front.Figure 8
Comparison of the results of the two algorithms for ZDT1 after one run.By testing several functions with different properties, the results show that both the algorithm UCMOEA and the algorithm USMOEA have good search ability compared with NSGA-II. The set of nondominated solutions obtained by the algorithm designed in this study is closer to the Pareto boundary, and the distribution of nondominated solutions is more uniform. It can be seen that in the actual vocal music teaching process, teachers can adopt this algorithm to improve the teaching effect.Survey and Analysis of Vocal Music Course Content: reasonable arrangement of the course content will help students to concentrate in class, improve classroom efficiency, and master theoretical knowledge and skills more accurately. In order to understand the arrangement of vocal music courses in place A, we conducted a random survey of students. The investigation is as follows:According to the arrangement of the course, we divide it into four levels. According to the data in Figure9, there are 27 people who think the course arrangement is very reasonable, accounting for 20% of the total number of respondents. There were 31 people who thought the course arrangement was more reasonable, accounting for 23% of the total number of respondents. There were 19 people who thought that the course arrangement was very general, which accounted for 14% of the total number of respondents. There were 58 people who thought the course arrangement was very unreasonable, which accounted for 43% of the total surveyed. According to the data, the number of people who do not agree with the curriculum arrangement is the highest. It can be seen that students generally believe that the school’s curriculum needs to be adjusted.Figure 9
Analysis of vocal course content survey.In addition to the classroom arrangement, the teaching content of the vocal music classroom is also very important. In order to understand the content of local vocal music teaching, we conducted a brief survey of it. According to the survey data, there are 45 people who think that songs should be appreciated in the classroom, accounting for 34% of the total number of respondents. There were 28 people who thought that song training should be carried out in the classroom, accounting for 21% of the total number of respondents. There were 52 people who thought that the classroom should be tested, accounting for 39% of the total number of respondents. Eight other students indicated that other methods could be taken. From these data, it can be seen that most people generally hope that they can carry out actual testing and training in the classroom to correct their deficiencies in time.Investigation and Analysis of Classroom Teaching Content Types: vocal music classes need to appreciate and train music in the classroom. To understand student attitudes toward classroom instruction, we surveyed the types of music students would like to see in the classroom. The details are as follows:According to the data in Figure10, students want teachers to choose a variety of different types of music mixed teaching in the classroom, accounting for 76% of the total number of respondents. 16 students indicated that they can use children’s repertoire properly in the classroom, accounting for 12% of the total surveyed. 9 students hope that teachers can choose popular songs as training repertoires in the classroom, accounting for 7% of the total number of respondents. 6 students hope that teachers can choose popular and folk songs as training repertoires in the classroom, accounting for 5% of the total number of respondents. According to these data, most students hope that the training repertoire in the classroom can be diversified.Figure 10
Analysis of classroom teaching content type survey.Vocal practice is the basic learning of vocal music. When we surveyed vocal practice, we found that 94 students thought vocal practice was very necessary, accounting for 70% of the total surveyed. Twenty-five students considered vocal practice necessary, accounting for 19% of the total surveyed. Twelve students considered vocal practice to be optional. Three other students felt that vocal training was unnecessary. According to the data, most students attach great importance to vocal practice. However, there are still some students with cognitive biases that need to be corrected.


## 5. Conclusion

This study discusses the current deficiencies in the field of vocal music education and uses genetic algorithms and visual thinking technology to combine vocal music teaching, so that the teaching methods are in line with the characteristics of students and improve teaching efficiency. Vocal music teaching is an important part of music art, and improving vocal skills is of great help to music singing. In the existing market, although various singing methods emerge in an endless stream, there are still many problems in the singing of songs. For example, unsteady breathing. This study points out that, in the process of vocal music teaching, attention should be paid to the teaching atmosphere, teachers’ teaching methods, and teaching content, especially the students’ attitude toward vocal music teaching, so as to correct the problem ideologically. By combining genetic algorithm and visual thinking with vocal music teaching, the existing teaching method is improved, so that the teaching method is consistent with the actual situation, which is conducive to the intuitive display of vocal music skills. This method provides a reference for future vocal music teaching methods, which is conducive to improving teaching results and improving vocal music skills. Although this study discusses vocal music teaching to a certain extent, it still has shortcomings: due to the lack of personal experience, many studies are only superficial research, just a simple overview of singing skills.



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