# Application of Multidirectional Mutation Genetic Algorithm and Its Optimization Neural Network in Intelligent Optimization of English Teaching Courses

Authors: Zi Yang
Journal: Computational Intelligence and Neuroscience
 (2021)

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## Abstract

Aiming at the problems existing in the traditional teaching mode, this paper intelligently optimizes English teaching courses by using multidirectional mutation genetic algorithm and its optimization neural network method. Firstly, this paper gives the framework of intelligent English course optimization system based on multidirectional mutation genetic BP neural network and analyses the local optimization problems existing in the traditional BP algorithm. A BP neural network optimization algorithm based on multidirectional mutation genetic algorithm (MMGA-BP) is presented. Then, the multidirectional mutation genetic BPNN algorithm is applied to the intelligent optimization of English teaching courses. The simulation shows that the multidirectional mutation genetic BP neural network algorithm can solve the local optimization problem of traditional BP neural network. Finally, a control group and an experimental group are set up to verify the role of multidirectional mutation genetic algorithm and its optimization neural network in the intelligent optimization system of English teaching courses through the combination of summative and formative teaching evaluations. The data show that MMGA-BP algorithm can significantly improve the scores of academic students in English courses and has better teaching performance. The effect of vocabulary teaching under the guidance of MMGA-BP optimization theory is very significant, which plays a certain role in the intelligent curriculum optimization of the experimental class.


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## Body

## 1. Introduction

Traditional English teaching is a one-way “training” activity for teachers to students. Teachers are the possessor of knowledge and occupy the dominant position in English classroom. Teachers are the center and students turn around teachers [1]. Traditional English classroom teaching assumes that students are just the recipients of knowledge and narrows the function of teaching to knowledge teaching but does not pay attention to students’ experience in teaching life. In the process of knowledge giving and receiving, students are deprived of their unique experience and understanding of knowledge. With the lack of emotional experience of learning, knowledge for students is just a pile of dead knowledge, false knowledge [2]. In Chinese traditional English classroom teaching, it is mainly emphasized that students’ learning is mainly based on indirect experience. Students are limited to and bound in the book world. English classroom has become a “holy Church” for imparting pure book knowledge. Students, such as saints in the “Church,” live a rigid and mechanical life far away from the real-life acquisition of scientific knowledge. Rational development has become the whole content of students’ life development. The new curriculum puts forward many new ideas, which makes the teaching world more blooming. However, some problems have also been exposed in the process of English curriculum reform. If English curriculum reform wants to achieve essential success, teaching and research experts and front-line teachers still have a long way to go. In view of these problems, teachers still need to constantly change and adjust to meet the needs of cultivating talents in the 21st century [3].Among the traditional optimization methods, the traditional teaching course optimization method is usually used to improve the optimization efficiency. However, in the optimization algorithm, we must use historical experience, teaching experience, and repeated experiments to select courses, so it does not have the overall situation, and, with the increase of personalized teaching needs, the accuracy of approximate analysis decreases sharply [4]. Recently, people have done a lot of exploration and research work in the development of new optimization methods. In the field of curriculum optimization, the introduction of neural network method is one of the exploration directions. Literature [5] puts forward a series of optimization measures for English teaching courses according to the optimization theory of teaching process, combined with the research status of graded teaching, students’ psychological and emotional problems, and teaching management problems in the implementation of graded teaching. Literature [6] points out that the combination of demand analysis theory and graded teaching is the objective requirement of foreign language teaching reform. Under the care of demand analysis theory, graded teaching shows great advantages in promoting students’ learning enthusiasm, promoting classroom teaching effect, and promoting the development of English teaching from standardized unified teaching to personalized teaching. Literature [7] discusses Babanski’s optimization theory of teaching process and holds that this theory follows the methodological principles of scientifically guiding teaching and reasonably organizing teaching process. The features of the teaching system are based on comprehensively considering teaching laws, teaching principles, teaching tasks, and forms and methods of modern teaching, as well as internal and external conditions. This theory is also one of the theoretical bases of graded teaching and can be widely used in English education. In the research of literature [8], aiming at the discussion on educational fairness and justice caused by graded teaching cases, it is proposed that we should first recognize the basic concept of graded teaching and put forward that fairness and excellence are the values that go hand in hand. We cannot ignore talent education because we advocate the balanced development of education; otherwise, our education will be mediocre education rather than high-quality education; it is not the education of a powerful country, which further clarifies the basic concept of graded teaching. Literature [9] discusses the background and definition of “i + 1” theory and then tries to combine “i + 1” theory with college English graded teaching. It points out that “ i + 1” theory is one of the most important theoretical bases for college English graded teaching and plays a positive guiding role; it also focuses on the enlightenment of “i + 1” theory to college English graded teaching and foreign language learning [10].Many domestic scholars have made in-depth research on the combination of GA and BPNN and achieved remarkable results. Literature [11] combined an improved genetic algorithm with BP neural network to solve the problem of short-term earthquake prediction. Literature [12] proposed a genetic algorithm based on BPNN and applied it to the screening of sensor array in alcohol gas recognition experiment. Literature [13] used genetic algorithm to optimize the controller parameters based on neural network structure and carried out relevant experiments. Literature [14] combines genetic algorithm with artificial neural network model to determine the maintenance strategy of a part. Literature [15] proposed GA-BPNN to solve optimization problems in complex engineering. Reference [16] proposed a high-speed channel modeling method based on artificial neural network to accurately predict the channel parameter matrix. Reference [17] trained and tested the single hidden layer feedforward neural network and limit learning machine. Through learning the sample data, the artificial neural network can simulate any nonlinear system in complex environment, so it can realize accurate modeling.However, there is no relevant literature to apply BP neural network and genetic algorithm to English teaching curriculum optimization [18]. The contributions are summarized as follows: (1) The framework of intelligent English curriculum optimization system based on multidirectional mutation genetic BP neural network is given. (2) A BP neural network optimization algorithm based on multidirectional mutation genetic algorithm (MMGA-BP) is proposed to solve the local optimization problem of traditional BP network. (3) Through the comparison between the control group and the experimental group and through the combination of summative and formative teaching evaluations, the advantages of multidirectional mutation genetic algorithm and its optimized neural network in the intelligent optimization system of English teaching courses are verified. Therefore, this paper proposes an intelligent optimization algorithm based on multidirectional mutation genetic algorithm and its optimized neural network in English teaching curriculum. This paper proposes a multidirectional mutation genetic algorithm, which mixes a variety of coding methods; it overcomes the disadvantage of the “ten” word distribution of the traditional genetic algorithm population and strengthens the local search ability of the genetic algorithm by introducing a new large mutation and small-scale search population. In this paper, a series of function optimization comparative experiments are carried out on multidirectional mutation genetic algorithm, general genetic algorithm, general two-population genetic algorithm, and PSO algorithm, and the experimental results are analysed: for the coupled function, our method has great advantages of high efficiency and good locality. In this paper, the above algorithm is applied to English teaching curriculum optimization and it achieves good results.In Section2 of this paper, design of intelligent optimization structure for English teaching courses is introduced. Section 3 introduces research on BP neural network algorithm based on multidirectional mutation genetic algorithm optimization (MMGA-BP). Section 4 is the test experiment and results analysis. Section 5 is the conclusion.


## 2. Design of Intelligent Optimization Structure for English Teaching Courses

### 2.1. Structure of Intelligent Optimization System for English Teaching

The whole system consists of management system, platform system, monitoring system, and English teaching system, as shown in Figure1. For management system and platform system, students manage and reserve courses through the management system. Based on the network, the reservation information taught by the management system is saved to the database, and their course information can be viewed on any networked computer, that is, the system to be designed and implemented in this paper. The English teaching system is installed at the entrance of the teaching place. Students entering the classroom need to swipe their cards to verify their identity and course reservation information before entering the teaching place. After learning, students need to swipe their card to leave, keep the students’ class time, and record the students’ class situation. Monitoring system consists of independent classrooms, foreign teachers, and students in each classroom. After class, students need to complete the feedback about their satisfaction with the class, and foreign teachers need to complete the class evaluation of students studying in the class. Students who are evaluated as “unqualified” cannot get the class hours of the course.Figure 1
Design of intelligent optimization structure for English teaching courses.In terms of teachers’ teaching management, teachers should establish an effective classroom environment, maintain classroom interaction, and promote the process of classroom growth. To achieve a good teaching management effect, teachers are required to use reasonable teaching methods and teaching organization forms to convey the teaching content to students in the process of classroom teaching and fully mobilize students’ enthusiasm for classroom participation. In the implementation plan of graded teaching, universities make the following requirements for teachers’ teaching management: classroom teaching adopts task-based, experiential, and project-based teaching modes. At the same time, it puts forward the teaching mode of combining traditional classroom teaching with “second classroom,” that is, emphasizing students’ independent learning outside class. Teaching management should play a role in standardizing and promoting the improvement of teaching quality. Students’ extracurricular learning is guided and managed in several ways: English learning strategy guidance project, navigation plan for students with English learning difficulties dominated by ethnic minority students, English competition activities, CET-4 and CET-6, postgraduate entrance examination English series guidance, and providing students with a series of guidance for job hunting, overseas exchange, and further study.


### 2.2. Design of English Teaching Management Platform Based on B/S Mode

The browser server structure of English practice management system is the structure, and the user interface is presented through the browser. Combined with the built-in server configuration, the program is written in database language. The system consists of client browser, browser, and database server.In the dynamic mode, the user puts forward the system request to the server through the client browser, and the server interprets the execution file through certain rules. When it needs to access the database, it submits the standard database access statement to the database server through the database access interface. The database server returns the data processing result to the server, and the server returns it to the browser in form and displays the execution result. The logic implementation in the system is mainly implemented on the server side, which simplifies the workload of client program construction. Subsequent system upgrading and expansion only need to modify the server-side code, so that the system has good scalability. The structural principle is shown in Figure2. The system adopts modular design in the overall design. The system includes ordinary client user module and client administrator module. Ordinary users include student module and foreign teacher module. Such users only complete the use process related to their own learning or teaching tasks in this system and have no internal authority management function. The administrator module needs to distinguish different permissions and carry out corresponding operations. At the same time, the framework structure is adopted in the student course reservation module, which avoids the disadvantages of turning around on different course pages in the process of browsing.Figure 2
Schematic diagram of English teaching management platform based on B/S mode.


## 2.1. Structure of Intelligent Optimization System for English Teaching

The whole system consists of management system, platform system, monitoring system, and English teaching system, as shown in Figure1. For management system and platform system, students manage and reserve courses through the management system. Based on the network, the reservation information taught by the management system is saved to the database, and their course information can be viewed on any networked computer, that is, the system to be designed and implemented in this paper. The English teaching system is installed at the entrance of the teaching place. Students entering the classroom need to swipe their cards to verify their identity and course reservation information before entering the teaching place. After learning, students need to swipe their card to leave, keep the students’ class time, and record the students’ class situation. Monitoring system consists of independent classrooms, foreign teachers, and students in each classroom. After class, students need to complete the feedback about their satisfaction with the class, and foreign teachers need to complete the class evaluation of students studying in the class. Students who are evaluated as “unqualified” cannot get the class hours of the course.Figure 1
Design of intelligent optimization structure for English teaching courses.In terms of teachers’ teaching management, teachers should establish an effective classroom environment, maintain classroom interaction, and promote the process of classroom growth. To achieve a good teaching management effect, teachers are required to use reasonable teaching methods and teaching organization forms to convey the teaching content to students in the process of classroom teaching and fully mobilize students’ enthusiasm for classroom participation. In the implementation plan of graded teaching, universities make the following requirements for teachers’ teaching management: classroom teaching adopts task-based, experiential, and project-based teaching modes. At the same time, it puts forward the teaching mode of combining traditional classroom teaching with “second classroom,” that is, emphasizing students’ independent learning outside class. Teaching management should play a role in standardizing and promoting the improvement of teaching quality. Students’ extracurricular learning is guided and managed in several ways: English learning strategy guidance project, navigation plan for students with English learning difficulties dominated by ethnic minority students, English competition activities, CET-4 and CET-6, postgraduate entrance examination English series guidance, and providing students with a series of guidance for job hunting, overseas exchange, and further study.


## 2.2. Design of English Teaching Management Platform Based on B/S Mode

The browser server structure of English practice management system is the structure, and the user interface is presented through the browser. Combined with the built-in server configuration, the program is written in database language. The system consists of client browser, browser, and database server.In the dynamic mode, the user puts forward the system request to the server through the client browser, and the server interprets the execution file through certain rules. When it needs to access the database, it submits the standard database access statement to the database server through the database access interface. The database server returns the data processing result to the server, and the server returns it to the browser in form and displays the execution result. The logic implementation in the system is mainly implemented on the server side, which simplifies the workload of client program construction. Subsequent system upgrading and expansion only need to modify the server-side code, so that the system has good scalability. The structural principle is shown in Figure2. The system adopts modular design in the overall design. The system includes ordinary client user module and client administrator module. Ordinary users include student module and foreign teacher module. Such users only complete the use process related to their own learning or teaching tasks in this system and have no internal authority management function. The administrator module needs to distinguish different permissions and carry out corresponding operations. At the same time, the framework structure is adopted in the student course reservation module, which avoids the disadvantages of turning around on different course pages in the process of browsing.Figure 2
Schematic diagram of English teaching management platform based on B/S mode.


## 3. Research on BP Neural Network Algorithm Based on Multidirectional Mutation Genetic Algorithm Optimization (MMGA-BP)

### 3.1. Design of MMGA-BP Algorithm Framework

For a variety of theories of English curriculum teaching, one is to explore the combination of various intelligences within MI theory and its teaching development strategies, so as to be impartial and coordinate the development of multiple intelligences. For example, the organic combination of language speech intelligence and mathematical logic intelligence can improve the effectiveness of students’ language expression. The combination of mathematical logical intelligence and visual spatial intelligence can construct schema association full of reason and change abstract thinking into graphical image memory. The combination of interpersonal communication intelligence and bodily kinesthetic intelligence can carry out game teaching. Any combination of intelligence and self-cognitive intelligence can strengthen self-reflective learning. The second is to explore the coincidence between MI theory and other language teaching theories, so as to design a vocabulary teaching strategy integrating multiple theories. The essence of this principle is to adhere to the organic unity of integration and optimization and give play to the teaching effect that a single theory does not have in teaching practice. Therefore, algorithm structure diagram of multidirectional mutation genetic algorithm and its optimized neural network is constructed as follows.As shown in Figure3, firstly, English teaching data and vocabulary course data are collected through the system and stored in the data center (it contains English words, sentences, grammar, logical structure, and English logic). Through feature extraction, the information of teaching experience, teaching process, teaching course, teaching attitude, and student score is extracted and sent to BPNN. Then the BPNN is optimized by multidirectional mutation genetic algorithm (MMGA-BP), which can improve the accuracy of BPNN.Figure 3
Algorithm structure diagram of MMGA-BP.


### 3.2. Research on Multidirectional Mutation Genetic Algorithm

In genetic algorithm, although binary single-point crossover or two-point crossover seems to increase the diversity of population and broaden the search space, just like biological “reproductive isolation,” random mating will destroy the genetic information of parents, which is not conducive to population evolution. Moreover, in a large number of function optimization experiments of simple genetic algorithm, the author found that the operators of simple genetic algorithm, especially the genetic operators of floating-point coding scheme, will lead to the distribution of population in the form of a kind of “+.” In the real coded genetic operator, whether it is crossover or mutation, the probability of all gene segments of chromosome changing is very low, and what usually happens is that one or several genes change, while other genes remain unchanged, which forms a “cross” distribution. In this paper, a hybrid coding genetic algorithm with floating-point crossover and binary mutation is proposed. The algorithm has the ability of multidirectional mutation, breaks the law of “ten” word distribution, and makes it relatively easy for the population to jump out of the trap and move towards the optimal search hyperplane. On this basis, this paper also proposes a dynamic coding scheme. The function of dynamic coding scheme is to ensure that individuals will not deviate the farthest from the central optimal value when genetic operation is carried out for small populations. The dynamic coding scheme is mainly aimed at the GA and MMGA algorithm. The overall structure of multidirectional mutation genetic algorithm is designed in Figure4.Figure 4
Structure of multidirectional mutation genetic algorithm.Before the operation of the whole algorithm, it is necessary to determine the population size popsize, the maximum cyclic algebra maxgen, the crossover ratepc, mutation rate pm of the two populations, the floating-point coding chromosome length nvars, the binary coding chromosome length chromlen, and the dynamic coding mutation range interval and set the current evolutionary algebra generation to 0. (1) Initialization: the algorithm reads in the algorithm decision variable definition fields upper [nvars] and lower [nvars] and randomly initializes the population according to the variable definition field. Although the algorithm adopts mixed coding method, in order to facilitate implementation, floating-point number is mainly used as coding method. When other coding operations are required, floating-point number can be converted to corresponding coding. In the whole solution space, popsize individuals are randomly generated as population A, and then an individual is randomly generated. All individuals of population B copy this individual; that is, population B takes 1 point as the search starting point. (2) Elitist preservation strategy: the improved algorithm proposed in this paper will also use this elitist preservation strategy, but the optimal individuals of the two populations will be saved in their own interior. The optimal individual information will be saved in the respective memory spaces population [popsize] and constructor [popsize]. (3) In this paper, the maximum evolutionary algebra is used as the stopping condition. In practical application, conditions can be added appropriately according to specific conditions, which does not affect the performance of the algorithm. (4) This paper adopts roulette selection method, also known as fitness proportion method. Each individual in the population calculates the selection probability in proportion according to its fitness value. (5) Crossover operator and mutation operator: the crossover operator of this algorithm adopts the single-point crossover method, where, for the two parent individuals that meet the crossover conditions, a point is randomly selected and the gene segments are exchanged before and after the point, so as to produce two new individuals, namely, the chromosome of the offspring. As mentioned in the previous chapter, the crossover operator of the two populations in this paper adopts floating-point coding to ensure that the genetic information of the parent generation can be completely retained to the offspring. Population A adopts binary coding, which can effectively avoid the population distribution in the form of “cross” and increase the global search ability. Population B uses dynamic coding to mutate in a fixed small range to enhance local search ability. It is worth mentioning that because the search range of population B is very small, the variation rate PCB of the population can be appropriately improved. Large variation rate combined with small variation range is more conducive to local optimization. (6) Migration operator: it is larger than the population [popsize] of population A and the constructor [popsize] of population B. If the population [popsize] is larger, it means that population A finds a better individual. At this time, all individuals of population B are copied as population [popsize], and this individual is used as the new search starting point.


### 3.3. Research on MMGA-BP Solution

In this paper, MMGA-BP is carried out in a serial way; that is, the GA is utilized to find the optimum weight and threshold of BPNN to provide the initialization input close to the optimal method; then the gradient descent method is used to train the BPNN, and the local search ability of BP algorithm is used to further find the optimal parameters. The MMGA-BP framework is designed in Figure5.Figure 5
The sketch map for MMGA-BP method.Neural network training test samples can be input one by one or as a whole batch of samples. The whole batch of samples input method is helpful to reduce the impact of sample input sequence on the training process. To better test the effect on the fitting function problem of neural network, only the first method is used here; that is, the sample-by-sample input method is used to train BPNN. This is to reduce the favourable factors from the neural network and highlight the multidirectional search ability of MMGA when analysing the experimental results. The flowchart of MMGA-BP method is shown in Figure6.Figure 6
MMGA-BP flowchart.To complete the training of BPNN, we need to complete the four following steps:Step 1: Initialize the weight of BP neural network by MMGA.Step 2: Select the sample data randomly:Ak=a1k,a1k,…,ank and Yk=y1k,y1k,…,ynk.Step 3: According to the neuron structure design, the weight parameters are randomly produced:(1)bj=∑i=1nwijai+θj,cj=gbj,lt=∑j=1pwjtbj+γt,st=glt,wherebj and cj are input and output of hidden neuron, respectively; lt and st are the input and output for output layer, respectively.Step 4: Back-propagation mechanism is utilized to adjust the parameters of BP network. When the error value is less than the set threshold, the training ends.(2)dtk=1+stytk+st,t=1,2,…,1,ejk=∑t=1qdtwjt1−cjcj,j=1,2,…,p,wjtN+1=wjtN−α⋅dtk⋅cj,θtN+1=θtN−a⋅dtk,0<α<1,wijN+1=wijN−βejkaik,θjN+1=θjN−βejk,0<β<1,E=12m∑k=0m∑t=1qytk+stk2.


## 3.1. Design of MMGA-BP Algorithm Framework

For a variety of theories of English curriculum teaching, one is to explore the combination of various intelligences within MI theory and its teaching development strategies, so as to be impartial and coordinate the development of multiple intelligences. For example, the organic combination of language speech intelligence and mathematical logic intelligence can improve the effectiveness of students’ language expression. The combination of mathematical logical intelligence and visual spatial intelligence can construct schema association full of reason and change abstract thinking into graphical image memory. The combination of interpersonal communication intelligence and bodily kinesthetic intelligence can carry out game teaching. Any combination of intelligence and self-cognitive intelligence can strengthen self-reflective learning. The second is to explore the coincidence between MI theory and other language teaching theories, so as to design a vocabulary teaching strategy integrating multiple theories. The essence of this principle is to adhere to the organic unity of integration and optimization and give play to the teaching effect that a single theory does not have in teaching practice. Therefore, algorithm structure diagram of multidirectional mutation genetic algorithm and its optimized neural network is constructed as follows.As shown in Figure3, firstly, English teaching data and vocabulary course data are collected through the system and stored in the data center (it contains English words, sentences, grammar, logical structure, and English logic). Through feature extraction, the information of teaching experience, teaching process, teaching course, teaching attitude, and student score is extracted and sent to BPNN. Then the BPNN is optimized by multidirectional mutation genetic algorithm (MMGA-BP), which can improve the accuracy of BPNN.Figure 3
Algorithm structure diagram of MMGA-BP.


## 3.2. Research on Multidirectional Mutation Genetic Algorithm

In genetic algorithm, although binary single-point crossover or two-point crossover seems to increase the diversity of population and broaden the search space, just like biological “reproductive isolation,” random mating will destroy the genetic information of parents, which is not conducive to population evolution. Moreover, in a large number of function optimization experiments of simple genetic algorithm, the author found that the operators of simple genetic algorithm, especially the genetic operators of floating-point coding scheme, will lead to the distribution of population in the form of a kind of “+.” In the real coded genetic operator, whether it is crossover or mutation, the probability of all gene segments of chromosome changing is very low, and what usually happens is that one or several genes change, while other genes remain unchanged, which forms a “cross” distribution. In this paper, a hybrid coding genetic algorithm with floating-point crossover and binary mutation is proposed. The algorithm has the ability of multidirectional mutation, breaks the law of “ten” word distribution, and makes it relatively easy for the population to jump out of the trap and move towards the optimal search hyperplane. On this basis, this paper also proposes a dynamic coding scheme. The function of dynamic coding scheme is to ensure that individuals will not deviate the farthest from the central optimal value when genetic operation is carried out for small populations. The dynamic coding scheme is mainly aimed at the GA and MMGA algorithm. The overall structure of multidirectional mutation genetic algorithm is designed in Figure4.Figure 4
Structure of multidirectional mutation genetic algorithm.Before the operation of the whole algorithm, it is necessary to determine the population size popsize, the maximum cyclic algebra maxgen, the crossover ratepc, mutation rate pm of the two populations, the floating-point coding chromosome length nvars, the binary coding chromosome length chromlen, and the dynamic coding mutation range interval and set the current evolutionary algebra generation to 0. (1) Initialization: the algorithm reads in the algorithm decision variable definition fields upper [nvars] and lower [nvars] and randomly initializes the population according to the variable definition field. Although the algorithm adopts mixed coding method, in order to facilitate implementation, floating-point number is mainly used as coding method. When other coding operations are required, floating-point number can be converted to corresponding coding. In the whole solution space, popsize individuals are randomly generated as population A, and then an individual is randomly generated. All individuals of population B copy this individual; that is, population B takes 1 point as the search starting point. (2) Elitist preservation strategy: the improved algorithm proposed in this paper will also use this elitist preservation strategy, but the optimal individuals of the two populations will be saved in their own interior. The optimal individual information will be saved in the respective memory spaces population [popsize] and constructor [popsize]. (3) In this paper, the maximum evolutionary algebra is used as the stopping condition. In practical application, conditions can be added appropriately according to specific conditions, which does not affect the performance of the algorithm. (4) This paper adopts roulette selection method, also known as fitness proportion method. Each individual in the population calculates the selection probability in proportion according to its fitness value. (5) Crossover operator and mutation operator: the crossover operator of this algorithm adopts the single-point crossover method, where, for the two parent individuals that meet the crossover conditions, a point is randomly selected and the gene segments are exchanged before and after the point, so as to produce two new individuals, namely, the chromosome of the offspring. As mentioned in the previous chapter, the crossover operator of the two populations in this paper adopts floating-point coding to ensure that the genetic information of the parent generation can be completely retained to the offspring. Population A adopts binary coding, which can effectively avoid the population distribution in the form of “cross” and increase the global search ability. Population B uses dynamic coding to mutate in a fixed small range to enhance local search ability. It is worth mentioning that because the search range of population B is very small, the variation rate PCB of the population can be appropriately improved. Large variation rate combined with small variation range is more conducive to local optimization. (6) Migration operator: it is larger than the population [popsize] of population A and the constructor [popsize] of population B. If the population [popsize] is larger, it means that population A finds a better individual. At this time, all individuals of population B are copied as population [popsize], and this individual is used as the new search starting point.


## 3.3. Research on MMGA-BP Solution

In this paper, MMGA-BP is carried out in a serial way; that is, the GA is utilized to find the optimum weight and threshold of BPNN to provide the initialization input close to the optimal method; then the gradient descent method is used to train the BPNN, and the local search ability of BP algorithm is used to further find the optimal parameters. The MMGA-BP framework is designed in Figure5.Figure 5
The sketch map for MMGA-BP method.Neural network training test samples can be input one by one or as a whole batch of samples. The whole batch of samples input method is helpful to reduce the impact of sample input sequence on the training process. To better test the effect on the fitting function problem of neural network, only the first method is used here; that is, the sample-by-sample input method is used to train BPNN. This is to reduce the favourable factors from the neural network and highlight the multidirectional search ability of MMGA when analysing the experimental results. The flowchart of MMGA-BP method is shown in Figure6.Figure 6
MMGA-BP flowchart.To complete the training of BPNN, we need to complete the four following steps:Step 1: Initialize the weight of BP neural network by MMGA.Step 2: Select the sample data randomly:Ak=a1k,a1k,…,ank and Yk=y1k,y1k,…,ynk.Step 3: According to the neuron structure design, the weight parameters are randomly produced:(1)bj=∑i=1nwijai+θj,cj=gbj,lt=∑j=1pwjtbj+γt,st=glt,wherebj and cj are input and output of hidden neuron, respectively; lt and st are the input and output for output layer, respectively.Step 4: Back-propagation mechanism is utilized to adjust the parameters of BP network. When the error value is less than the set threshold, the training ends.(2)dtk=1+stytk+st,t=1,2,…,1,ejk=∑t=1qdtwjt1−cjcj,j=1,2,…,p,wjtN+1=wjtN−α⋅dtk⋅cj,θtN+1=θtN−a⋅dtk,0<α<1,wijN+1=wijN−βejkaik,θjN+1=θjN−βejk,0<β<1,E=12m∑k=0m∑t=1qytk+stk2.


## 4. Test Experiment and Results Analysis

### 4.1. Teaching Experiment Setting and Parameter Setting

The teaching experiment period of this paper is from March 1, 2020, to June 31, 2020. Excluding the students’ vacation time and examination time, the cumulative effective experimental period is 15 weeks. Firstly, the evaluation object of teaching experiment is the ordinary class of Science in grade two of senior high school. The main reasons for choosing senior two are as follows: It takes some time to be familiar with the characteristics of senior high school English teaching and find their problems and difficulties in the process of English learning; senior three students face the pressure of college entrance examination, and it is not easy for teachers to give up the traditional teaching mode to carry out teaching experiments; after a year of high school life, senior two students have also highlighted various problems in English learning. English teachers urgently need to change the current situation of vocabulary teaching.Secondly, the effect evaluation of this teaching experiment is based on the monthly test paper organized by the school. Taking the English score of the first monthly test as the pretest data, the significancet-test of the difference between the small sample mean and the overall mean is carried out for the ordinary classes of Science (classes 1–8 and 11–18). The statistical indicators of English performance show that class 4 and class 5 are very close in terms of mean, standard error, and standard deviation, and the p values of these two classes are greater than 0.05, indicating that English performance of class 4 and class 5 and the average level of grade English performance are similar. In order to further verify that class 4 and class 5 are teaching parallel classes, the results show that the significance probability of variance homogeneity test is 0.759, which is greater than 0.05, so we accept the hypothesis that the score is at the level of square difference homogeneity. The two-tailed significance probability sig. of the equal mean test is 0.9618.


#### 4.1.1. MMGA-BP Parameter Setting

After many experiments, the parameters in genetic algorithm include mutation probabilitym, population size, and exchange probability C. The optimal values of three parameters of genetic algorithm are M = 0.096, n = 60, and C = 0.91. The number of hidden layer nodes is determined according to the training accuracy of hidden layer BPNN, and the number of hidden layer nodes P is continuously adjusted until the accuracy value is reached. After analysis and debugging, the BPNN model is 6-17-3.The experimental part is mainly carried out from two angles: (1) verify the superiority of MMGA-BP algorithm over GA-BP algorithm; the experiment mainly compares the convergence speed and accuracy; (2) based on the advantages of English curriculum optimization of MMGA-BP, we count the students of traditional teaching schemes.


### 4.2. Advantage Verification of MMGA-BP Algorithm

In order to further verify the advantages of MMGA-BP algorithm under optimal network weights, the experimental results are compared through a simulation example. Table1 shows the results of MMGA-BP, where H is the number of hidden layer nodes; S is the learning rate; M is momentum factor; ES is the sum of squares of errors. It means that when the number of hidden layer nodes is 17, MMGA-BP network has the best performance. Therefore, the number of hidden nodes is 17.Table 1
Convergence comparison of BP and MMGA-BP method.
GroupPerformanceHSMES1170.96350.56640.000892100.88740.58780.005363130.95410.51120.0020234150.90340.46570.001345200.92310.47580.003220The process of searching the initial weight of genetic algorithm is shown in Figure7(a). The MMGA has high efficiency in searching the weight of neural network. In the 400 generations, the error can be reduced to less than 0.5. The multidirectional mutation ability enables the algorithm to quickly find a new search hyperplane and improve the performance of the algorithm. After 1000 generations, the error decreased steadily, indicating that the local search of search subpopulation can play a role. The above process shows that MMGA can play a good preliminary search role and reduce the training time of neural network for the problem of neural network fitting high-dimensional and nonlinear coupling function.Figure 7
Convergence figure of MMGA and MMGA-BP method. (a) Convergence comparison between MMGA and GA. (b) Convergence performance of MMGA-BP.
(a)(b)The convergence diagram of MMGA-BP algorithm is shown in Figure7(b). After the genetic algorithm search, the weight of the neural network should be near the optimal solution. At this time, the gradient descent method is adopted. In the figure, after 0 generations, the error rises rapidly to 0.6, decreases at a certain rate, and makes the error quickly close to 0. The possible reason for this situation is that although the genetic algorithm evolves the weight solution of the neural network near the best advantage, it does not give the descending direction of the neural network. Therefore, when the neural network is trained at the beginning, there is overfitting due to ignorance of information. This needs to be further studied and discussed and is left to be solved in the future. After that, the neural network can continue to decline and the error is less than the original, which shows that the initialization solution of genetic algorithm does need the retraining of BPNN.


### 4.3. Verification of Teaching Effect and Superiority

To explain the advantages and superiority of the English teaching curriculum optimization algorithm Based on MMGA-BP algorithm, the teaching experiment in this paper takes the subsequent monthly test results organized by the school as the posttest data, extracts the English scores from the monthly test total score database, selects 50 people in each class as a reference, and uses SPSS 19.0 statistical software for statistical analysis of relevant data indicators.Firstly, a small sample was tested for significance of difference. It can be seen from Figure8 that the English score p value of the first monthly test shows that control group and the experimental group have the same English level. In the second monthly test, the average score of the experimental class (93.7) was 0.9 points higher than that of the control class (92.8), the standard deviation of the experimental class (8.261) was slightly lower than that of the control class (8.813), and the optimization effect of English teaching curriculum based on MMGA-BP was not obvious at the beginning of the experiment. In the third and fourth monthly tests, the mean difference between the experimental class and the control class has shown an extremely significant level. In the middle and late stages of the experiment, the English performance of the experimental class has obviously surpassed that of the control class. The teaching advantage of English teaching curriculum optimization based on MMGA-BP is becoming more and more obvious. In order to eliminate the influence of factors such as students’ English learning effort level and the difficulty of test papers on this experiment and further verify the vocabulary teaching effect of English teaching curriculum optimization based on MMGA-BP, the significance t-test of the difference between the mean value of small samples (each class) and the mean value of the whole (grade) is carried out.Figure 8
Comparison of teaching effects between experimental group and control group.In order to eliminate the influence of factors such as students’ English learning effort level and the difficulty of test papers on this experiment and further verify the teaching effect based on MMGA-BP English curriculum optimization system, next, at-test is conducted for the significance of the difference between the small sample (each class) mean and the overall (grade) mean.As shown in Table2, after more than two months of teaching experiment, the average English scores of the experimental class and the control class are above the grade level. However, the p value shows that there is no significant difference between the average scores of the control class and the grade level, which is highly statistically significant. The results of the experimental class have been ahead of the grade level and ranked first compared with other evaluation classes. The dispersion degree of the experimental class (SD = 7.654) has been significantly lower than the fluctuation level of the grade (SD = 9.978), and the dispersion degree of the control class (SD = 9.773) is slightly higher than the grade level. This shows that, in the middle of the experiment, the effect of vocabulary teaching guided by MMGA-BP optimization theory is very significant, and the intelligent curriculum optimization of students in the experimental class has played a role.Table 2
Comparison of the third examination results of each class.
NumberScoreMean errorSDt valuep valueClass 15092.11.4629.6540.5360.823Class 25091.71.5139.3680.6420.764Class 35091.91.4879.7680.6480.846Class 4 (EP)5096.31.0797.6548.6470.317Class 5 (CP)5091.81.3979.9780.7810.598Class 65092.41.5029.6520.7060.941Class 75092.31.4369.3460.7930.873


## 4.1. Teaching Experiment Setting and Parameter Setting

The teaching experiment period of this paper is from March 1, 2020, to June 31, 2020. Excluding the students’ vacation time and examination time, the cumulative effective experimental period is 15 weeks. Firstly, the evaluation object of teaching experiment is the ordinary class of Science in grade two of senior high school. The main reasons for choosing senior two are as follows: It takes some time to be familiar with the characteristics of senior high school English teaching and find their problems and difficulties in the process of English learning; senior three students face the pressure of college entrance examination, and it is not easy for teachers to give up the traditional teaching mode to carry out teaching experiments; after a year of high school life, senior two students have also highlighted various problems in English learning. English teachers urgently need to change the current situation of vocabulary teaching.Secondly, the effect evaluation of this teaching experiment is based on the monthly test paper organized by the school. Taking the English score of the first monthly test as the pretest data, the significancet-test of the difference between the small sample mean and the overall mean is carried out for the ordinary classes of Science (classes 1–8 and 11–18). The statistical indicators of English performance show that class 4 and class 5 are very close in terms of mean, standard error, and standard deviation, and the p values of these two classes are greater than 0.05, indicating that English performance of class 4 and class 5 and the average level of grade English performance are similar. In order to further verify that class 4 and class 5 are teaching parallel classes, the results show that the significance probability of variance homogeneity test is 0.759, which is greater than 0.05, so we accept the hypothesis that the score is at the level of square difference homogeneity. The two-tailed significance probability sig. of the equal mean test is 0.9618.


### 4.1.1. MMGA-BP Parameter Setting

After many experiments, the parameters in genetic algorithm include mutation probabilitym, population size, and exchange probability C. The optimal values of three parameters of genetic algorithm are M = 0.096, n = 60, and C = 0.91. The number of hidden layer nodes is determined according to the training accuracy of hidden layer BPNN, and the number of hidden layer nodes P is continuously adjusted until the accuracy value is reached. After analysis and debugging, the BPNN model is 6-17-3.The experimental part is mainly carried out from two angles: (1) verify the superiority of MMGA-BP algorithm over GA-BP algorithm; the experiment mainly compares the convergence speed and accuracy; (2) based on the advantages of English curriculum optimization of MMGA-BP, we count the students of traditional teaching schemes.


## 4.1.1. MMGA-BP Parameter Setting

After many experiments, the parameters in genetic algorithm include mutation probabilitym, population size, and exchange probability C. The optimal values of three parameters of genetic algorithm are M = 0.096, n = 60, and C = 0.91. The number of hidden layer nodes is determined according to the training accuracy of hidden layer BPNN, and the number of hidden layer nodes P is continuously adjusted until the accuracy value is reached. After analysis and debugging, the BPNN model is 6-17-3.The experimental part is mainly carried out from two angles: (1) verify the superiority of MMGA-BP algorithm over GA-BP algorithm; the experiment mainly compares the convergence speed and accuracy; (2) based on the advantages of English curriculum optimization of MMGA-BP, we count the students of traditional teaching schemes.


## 4.2. Advantage Verification of MMGA-BP Algorithm

In order to further verify the advantages of MMGA-BP algorithm under optimal network weights, the experimental results are compared through a simulation example. Table1 shows the results of MMGA-BP, where H is the number of hidden layer nodes; S is the learning rate; M is momentum factor; ES is the sum of squares of errors. It means that when the number of hidden layer nodes is 17, MMGA-BP network has the best performance. Therefore, the number of hidden nodes is 17.Table 1
Convergence comparison of BP and MMGA-BP method.
GroupPerformanceHSMES1170.96350.56640.000892100.88740.58780.005363130.95410.51120.0020234150.90340.46570.001345200.92310.47580.003220The process of searching the initial weight of genetic algorithm is shown in Figure7(a). The MMGA has high efficiency in searching the weight of neural network. In the 400 generations, the error can be reduced to less than 0.5. The multidirectional mutation ability enables the algorithm to quickly find a new search hyperplane and improve the performance of the algorithm. After 1000 generations, the error decreased steadily, indicating that the local search of search subpopulation can play a role. The above process shows that MMGA can play a good preliminary search role and reduce the training time of neural network for the problem of neural network fitting high-dimensional and nonlinear coupling function.Figure 7
Convergence figure of MMGA and MMGA-BP method. (a) Convergence comparison between MMGA and GA. (b) Convergence performance of MMGA-BP.
(a)(b)The convergence diagram of MMGA-BP algorithm is shown in Figure7(b). After the genetic algorithm search, the weight of the neural network should be near the optimal solution. At this time, the gradient descent method is adopted. In the figure, after 0 generations, the error rises rapidly to 0.6, decreases at a certain rate, and makes the error quickly close to 0. The possible reason for this situation is that although the genetic algorithm evolves the weight solution of the neural network near the best advantage, it does not give the descending direction of the neural network. Therefore, when the neural network is trained at the beginning, there is overfitting due to ignorance of information. This needs to be further studied and discussed and is left to be solved in the future. After that, the neural network can continue to decline and the error is less than the original, which shows that the initialization solution of genetic algorithm does need the retraining of BPNN.


## 4.3. Verification of Teaching Effect and Superiority

To explain the advantages and superiority of the English teaching curriculum optimization algorithm Based on MMGA-BP algorithm, the teaching experiment in this paper takes the subsequent monthly test results organized by the school as the posttest data, extracts the English scores from the monthly test total score database, selects 50 people in each class as a reference, and uses SPSS 19.0 statistical software for statistical analysis of relevant data indicators.Firstly, a small sample was tested for significance of difference. It can be seen from Figure8 that the English score p value of the first monthly test shows that control group and the experimental group have the same English level. In the second monthly test, the average score of the experimental class (93.7) was 0.9 points higher than that of the control class (92.8), the standard deviation of the experimental class (8.261) was slightly lower than that of the control class (8.813), and the optimization effect of English teaching curriculum based on MMGA-BP was not obvious at the beginning of the experiment. In the third and fourth monthly tests, the mean difference between the experimental class and the control class has shown an extremely significant level. In the middle and late stages of the experiment, the English performance of the experimental class has obviously surpassed that of the control class. The teaching advantage of English teaching curriculum optimization based on MMGA-BP is becoming more and more obvious. In order to eliminate the influence of factors such as students’ English learning effort level and the difficulty of test papers on this experiment and further verify the vocabulary teaching effect of English teaching curriculum optimization based on MMGA-BP, the significance t-test of the difference between the mean value of small samples (each class) and the mean value of the whole (grade) is carried out.Figure 8
Comparison of teaching effects between experimental group and control group.In order to eliminate the influence of factors such as students’ English learning effort level and the difficulty of test papers on this experiment and further verify the teaching effect based on MMGA-BP English curriculum optimization system, next, at-test is conducted for the significance of the difference between the small sample (each class) mean and the overall (grade) mean.As shown in Table2, after more than two months of teaching experiment, the average English scores of the experimental class and the control class are above the grade level. However, the p value shows that there is no significant difference between the average scores of the control class and the grade level, which is highly statistically significant. The results of the experimental class have been ahead of the grade level and ranked first compared with other evaluation classes. The dispersion degree of the experimental class (SD = 7.654) has been significantly lower than the fluctuation level of the grade (SD = 9.978), and the dispersion degree of the control class (SD = 9.773) is slightly higher than the grade level. This shows that, in the middle of the experiment, the effect of vocabulary teaching guided by MMGA-BP optimization theory is very significant, and the intelligent curriculum optimization of students in the experimental class has played a role.Table 2
Comparison of the third examination results of each class.
NumberScoreMean errorSDt valuep valueClass 15092.11.4629.6540.5360.823Class 25091.71.5139.3680.6420.764Class 35091.91.4879.7680.6480.846Class 4 (EP)5096.31.0797.6548.6470.317Class 5 (CP)5091.81.3979.9780.7810.598Class 65092.41.5029.6520.7060.941Class 75092.31.4369.3460.7930.873


## 5. Conclusion

Aiming at the problems existing in the traditional teaching mode, this paper intelligently optimizes English teaching courses by using multidirectional mutation genetic algorithm and its optimization neural network method. Firstly, this paper gives the framework of intelligent English course optimization system based on MMGA-BP algorithm; and we analyse the local optimization problems existing in the traditional BP algorithm, and MMGA-BP is presented. MMGA-BP algorithm solves the local optimization problem of traditional BP algorithm. In addition, MMGA significantly improves the efficiency of GA algorithm. At the same time, the statistics of English scores in previous monthly examinations show that, in the early stage of teaching experiment, the teaching advantage of English curriculum optimization system based on MMGA-BP is not significant. In the middle and later stages of the experiment, the teaching practice of the English courses optimization system based on MMGA-BP and the students’ English learning ability are continuously strengthened, the progress rate of the experimental class is accelerating, the control class is basically standing still, and the progress of other evaluation classes is slow. In the later stage of the experiment, the experimental class has become the focus class in the eyes of teachers and the star class in the school. Next, we will use other heuristic optimization algorithms (like monarch butterfly optimization (MBO), earthworm optimization algorithm (EWA), elephant herding optimization (EHO), moth search (MS) algorithm, Slime mould algorithm (SMA), and Harris hawks optimization (HHO)) to further improve English curriculum education.



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*Source: 4297600-2021-12-10.xml*