# Artificial Intelligence-Based Soccer Sports Training Function Extraction: Application of Improved Genetic Algorithm to Soccer Training Path Planning

Authors: Weiwei Zhang
Journal: Journal of Sensors
 (2022)

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## Abstract

Artificial intelligence has given a new dimension to the sport and mentality of soccer by tracking and planning the path of soccer and analyzing the learning process. Along with the rapid development of artificial intelligence technology, it is also used in sports events. Soccer, the world’s number one ball game, has always received worldwide attention. Although soccer is full of realism, it does not mean that its behavior cannot be predicted. By taking advantage of the role of artificial intelligence, model data from training is used to perform a deeper analysis of the correlated role of a soccer team’s players in the game, which is very helpful to improve the team’s training efficiency and tactics. As an emerging intelligent algorithm, the improved genetic algorithm can seek the global optimal solution based on the results of the algorithm execution and apply it to the planning of soccer training paths, which has a strong application value for improving the team’s training level and helping the team to formulate reasonable and effective offensive and defensive strategies. In this paper, we use artificial intelligence as the research background to improve the genetic algorithm by extracting the soccer training function and apply the improved algorithm to the soccer training path planning to get the global optimal solution, so as to help the players find the reasonable and effective optimal path of passing or shooting and help the team to build the tactical planning and winning strategy of offense and defense to meet the earnest expectation of soccer fans.


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## Body

## 1. Introduction

With the development of the times and the advancement of technology, modern technologies such as GPS trackers, computer vision algorithms, and sensors are applied to soccer training, and the amount of data contained in the sport of soccer is uncovered. Artificial intelligence has given a new dimension to the sport of soccer and mentality by tracking and planning the path of soccer and analyzing the learning process, that is, in the sport of soccer as a form of psychomotor. For the research experts who apply artificial intelligence to sports training, the soccer institute has given them a sufficiently challenging environment to test their research results. Daley, an internationally renowned soccer coach, has said, “The sport of soccer is not only fun, but contains a relational component of competition and cooperation.” The game of soccer contains many possibilities, with unpredictable changes on the field and many uncertainties, and unlike chess and Go, the game is played in a world full of realism, which is a new challenge for the application aspect of artificial intelligence [1].Although soccer is full of realism, it does not mean that its behavior cannot be predicted. By taking advantage of the role of artificial intelligence, model data from training can be used to provide a deeper analysis of the correlated role of a soccer team’s players in the game, which can be very helpful in improving the team’s training efficiency and tactics. Through the use of artificial intelligence, the functional features of soccer training can be extracted, and the results can be applied to soccer training planning to extract data for certain specific actions such as passing, shoveling, or shooting to simulate and analyze the size of a player’s contribution to a pass or goal. This feature can be applied to postmatch analysis to show players what actions to make in certain situations, such as whether to pass or shoot to help the team score, and can be used to develop training plans based on the intensity of player performance data and health status, track player fatigue, avoid players from being injured during training due to high intensity and overload, and reduce player injury status [2].For the study of soccer training path planning, we have to mention “gravimetric shadow.” The term “gravity” refers to the application of an alternative track to the actual track. As in soccer video games, the trajectory of the soccer ball changes through the use of different joysticks to be used in conjunction with each other, and the coordination of different manipulation controls causes the path distance of the soccer ball to change. This technology can be used to predict and study the chemical effects of tactical changes to control the pace of the game. For example, if a team’s star player is injured on the field of play, how the entire team will reformulate its tactics to play the game is something that both coaches and players need to be aware of. Artificial intelligence expert Touhill stated that the purpose of applying artificial intelligence to soccer is not to replace the coach, but to assist the coach’s coaching by using intelligent technology to process data that cannot be handled manually, so as to better find solutions to problems [3].As an emerging intelligent algorithm, the improved genetic algorithm can seek the global optimal solution based on the results of the algorithm execution and apply it to the planning of soccer training paths, which has a strong application value for improving the team’s training level and helping the team to develop reasonable and effective offensive and defensive strategies.


## 2. Research Background

In the 1950s, soccer enthusiast Reippe conducted a study on goal scoring. According to his calculations, most goals were scored by at least four players, and their research created a passing style in soccer. In the past, artificial intelligence has given completely wrong answers in other areas. For example, AI trained in video games has won by breaking the rules of the game and the laws of science. Soccer players trained in soccer data will organize various passing routes for passing attacks based on scientific training path planning and grasp the opportunity to shoot effectively, whether it is a long pass ball or a triangle attack tactic, to carry out a well-traveled, offensive, and defensive battle plan. Artificial intelligence will not replace soccer coaches, but its influence will be more evident in the years to come. The use of AI in soccer training is to form a seamless system that better combines players from all positions on the field through a linkage effect to create the perfect play of offense and defense as one. In the short term, AI may not be able to give full play to its advantages, but in the next 10 or even 5 years, AI technology will be perfectly integrated into soccer training route planning, and some tools will be more mature, even such AI products. With the advent of the “coaching assistant video machine,” it is possible to analyze not only pre- and postgame situations but also to view changes in data strategy in the first and second half [4].Artificial intelligence, as a high-end field, is based on the combination of computer science, statistical science, systems science, and humanities. It is the science of simulating, extending, and propagating human emotions and intelligence [5]. The application areas of artificial intelligence are vast, as shown in Figure 1.Figure 1
Application areas of artificial intelligence.The development potential of the market size of artificial intelligence chips is very huge, and the data in Figure2 shows that the market size of artificial intelligence chips is expected to exceed $70 billion in 2025. The global artificial intelligence chip market size from 2018 to 2025 and its forecast results are shown in Figure 2.Figure 2
Global artificial intelligence chip market size and its forecast results.Corporate investment in the field of artificial intelligence continues to grow, and the industry is gradually maturing, and it can be inferred from Figure3 that the development of artificial intelligence has strong financial support. Enterprise investment in this field market in recent years is shown in Figure 3.Figure 3
The market enterprise investment in this field in recent years.The research results for artificial intelligence have extended both the function of human brain and the practical labor ability of human. The emergence of artificial intelligence makes the science and technology revolution new life and vitality, opening up the era of intelligence. The introduction of artificial intelligence technology in many fields has also led to new directions and new research points in the industry. Along with the rapid progress of computer technology, the level of artificial intelligence technology has also risen to a new level. Its application to various studies in the field of sports competitions has also become a unique shining point in the field of artificial intelligence. The application of artificial intelligence to sports events can not only enhance the audience’s sensory experience of sports events and capture the wonderful moments on the field but also make an objective and fair evaluation of the game process, and to a certain extent, avoid the occurrence of referee disputes between judges and athletes due to unspecified penalties on the field. It also meets the high expectations of coaches, athletes, and sports event staff for artificial intelligence technology and injects new vitality and vigor into various sports events, especially for the world’s number one sport, soccer, showing the way for soccer event staff, soccer coaches, players, and soccer fans in general [6].In soccer, the touchline referee plays an important role in the field rules and in the awarding of the right to validate a goal. However, since the subjective consciousness of the referee can lead to controversies, the referee is equipped with an electronic generator that can detect and check the reasonableness of the referee’s penalty in time and then inform the referee of the result. As a kind of electronic timely judgment system in the field of soccer, “soccer electronic referee” can make timely judgment on each goal and whether a player is offside in a soccer match and maintain the fairness of the match. As a product of artificial intelligence, the system components of soccer electronic referee consist of two systems, hardware and software, and are equipped with new high-end technology. The software system includes a variety of advanced software tools, including chips and related software, to rationalize the use of these tools [7]. The hardware system includes a soccer ball positioning transmitter, a soccer ball path tracker, a ball speed tester, an onside ball positioning transmitter, a player positioning transmitter, and a referee data receiver, and its structure is shown in Figure 4.Figure 4
Hardware system structure.The definition of offside position varies in different national and regional soccer leagues. According to FIFA, the decision of an excess penalty is made in conjunction with the pattern of the soccer field to maintain the penalty rules in soccer. Based on the extrapolated pitch feature line, the pitch level is set in the two-dimensional coordinates based on the two-dimensional coordinate model to control the player position and the speed of the soccer ball to achieve a reasonable judgment of the Vietnam soccer game. Among them, the offside detection algorithm set according to the rules of the game is based on the plane coordinates of the players, so to achieve the recovery and interpretation of the plane coordinates of the pitch, only a single camera needs to be selected to shoot the projection matrix to solve the goal. This eliminates many unnecessary processes and complicated computational steps compared to traditional algorithms that use multiple cameras for analysis. After the extraction and interpretation of the video image, a two-dimensional model of the soccer field coordinates can be obtained in binary form using the implementation of an optimization algorithm for stadium detection and extraction. The morphological optimization is based on the 2D image of the soccer field, and then, noise is removed, and data is extracted in open mode [8]. On the basis of area analysis, the nonactive field area is removed, and the two-dimensional image of the playing field area is superimposed on the original image of the RGB image of the soccer field. After adding the circular image and the double-valued image, the stored templates contained in the master computer and the role of the stadium information construction template are combined with each other to extract and analyze the scenes and offside penalties that appear in the video and to complete the result detection and reasonable and effective analysis performed on the soccer path and the effective data of player attack and defense. After completing the above steps, the entire stadium area is reconstructed in two-dimensional coordinates again, and subsequently, the autonomous discriminatory algorithm of soccer offside is completed with reasonable modifications in order to better detect the offside situations appearing in future soccer matches, reduce the occurrence of controversial scenarios in the stadium caused by penalty errors, and maintain the fairness of soccer matches [9].


## 3. Research Methods and Materials

### 3.1. Introduction of Genetic Algorithm

#### 3.1.1. Principle of Genetic Algorithm

The roots of genetic algorithms are among the principles of biogenetics. According to the relevant expertise in the field of biogenetics, species preserve their own traits through DNA during their own evolution and inherit the good traits they contain to the next generation of the species through the genes contained in DNA through biogenetic principles [10]. As early as the 1960s, German university professor Hardman and British geneticist Craig analyzed the complex properties contained in biological evolution. Genetic algorithms have been widely studied since 1967, when a young man named Dirk Berogh formally introduced the concept of “genetic algorithms,” and since then, they have been used as an important scientific algorithm in various scientific fields and have achieved a series of fruitful results. Since genetic algorithms are based on the principles of biogenetics, there is no major difference between the principles of genetic algorithms and genetic inheritance. According to the genetic principle of Mendel, the father of biogenetics, the essence of species evolution is the rearrangement of genes and the very low probability of genetic mutation, so that the characteristics of the previous generation will be inherited to the next generation to achieve the continuity of species in the biological world and enhance the excellence of species to enhance the survival ability of species to adapt to the environment. The basic operation of genetic algorithms, as a related principle originated from biogenetics, coincides with the essence of biogenetic species evolution described above. The first step of the genetic algorithm process is the calculation of the fitness value for the initialized population, followed by the determination of whether the fitness satisfies the convergence condition. If the fitness value satisfies the convergence condition, the direct evolutionary process ends; if the fitness value does not satisfy the convergence condition, selected operations are performed on individual adaptive functions, and then, crossover operations are performed based on the probability of change. After performing the selection, crossover, and mutation, the results are transferred to the first step to continue the calculation of the fitness value for the initialized population until the calculated fitness value satisfies the convergence condition; otherwise, the cycle of selection, crossover, and mutation steps continues [11]. The process of genetic algorithm execution is shown in Figure 5.Figure 5
The process of genetic algorithm execution.


#### 3.1.2. Definition and Basic Theorem of Genetic Algorithm

When using genetic algorithms to plan the path of soccer movement, what steps to go through to ensure that the children obtained after the iterative relationships performed by the control algorithm can maintain a good trend of growth and how to determine the global optimal solution are key issues to be considered when using genetic algorithms for path planning. At this point, it is necessary to mention two fundamental theorems of genetic algorithms: the exponential growth theorem and the average fitness value theorem. To have a deeper understanding and analysis of these two theorems, it is necessary to first clarify the definitions of genetic algorithms [12].Definition 1.
Each genetic operator located in the extended bit-string space, etc., corresponds to the corresponding bit-string space, and the correspondence is in accordance with the basic principles of genetic algorithms. The genetic operator is a specific model for the set of character bit strings, reflecting the fact that the position of the string occurrence in that string set has a considerable correlation with the genetic operator [13].Definition 2.
The number of gene bits in different positions possesses different orders of the pattern, and the number of two identified gene bits in the pattern containing 1 and 2 is defined as the basic order of the pattern, which will be denoted asDH. During the operation of the genetic algorithm, the increase in the number of iterations executed causes the order of the pattern to change in the same direction as the deterministic come of the pattern. That is, as the number of orders increases, the determinism of the pattern gradually increases. Along with the decrease in the number of orders, the determinacy of the pattern also gradually decreases, and the number of samples will become more and more [14].Definition 3.
The number of bit strings appearing in the pattern can be considered as the dimensionality of the pattern, which will be denoted asBH. In genetic algorithms, the number of bits appearing in the pattern shows an inverse relationship with the change in the number of samples. That is, as the number of bit strings appearing in the pattern increases, the number of samples gradually decreases; conversely, a decrease in the number of bit strings leads to an increase in the number of sample purposes [15].Definition 4.
The distance between the first definite bit appearing in a pattern and the last definite bit appearing in the pattern can be considered as the order distance of the pattern, which will be denoted asMH. The order distance represents the length of the defined distance of the pattern and reflects the time difference between the first time identified as an occurrence and the last time identified as an occurrence [16].(1) Exponential Growth Theorem. The operation process of the genetic algorithm goes through many steps, and the initial population is subjected to the execution of algorithms such as selection, crossover, and mutation of genetic operators without satisfying the convergence conditions to continue the calculation of fitness values for the initialized population. During the execution of the above algorithm, the patterns whose children contain low order, whose defined length does not meet the standard length, and whose fitness does not reach the average level of fitness of the population will grow at a very high rate in the initial population to achieve the higher order evolution of the patterns, and their growth rate can reach exponential levels, hence the name exponential growth theorem [17].(2) Average Fitness Value Theorem. During the operation of the genetic algorithm, the patterns in the population that are at the lower order level can be evolved to the higher order after the execution of algorithms such as selection, crossover, and mutation of genetic operators. This is known as the average fitness value theorem. This theorem is more beneficial to obtain the global optimal solution to accomplish the goal of the algorithm while ensuring the normal operation of the algorithm [18].Using the initial population as a starting point, when applying genetic algorithms to the study of path planning, it is not necessary to solve the set of solutions separately for those sets for which the solution set is already known. These known solutions are used as optimal planning paths in soccer training, and the initial population is constructed by extracting individual known feasible path solutions in the scope of the problem solution space according to the principle of equal extraction. The starting moment is set tot0, so that t0=0, and the number of individuals is represented by n. Then, the n individual changes together constitute the initial population Ct, and usually, the fitness of the individuals contained in the initial population is at a low level, which needs to be improved by the genetic algorithm to simulate the biological evolution process in order to find the final optimal solution more easily to realize the optimal path planning for soccer training, thus improving the team training efficiency and helping to match the team tactical system [19].


### 3.2. Improved Genetic Algorithm

#### 3.2.1. Optimizing the Parameter Selection Method

The selection of parameters occupies a key position in the genetic algorithm. The choice of different control parameters will affect the convergence effect of the algorithmic process and even the performance of the whole genetic algorithm. There are many parameters embedded in the algorithm, which include selection probability pm, crossover probability pa, variation probability pn, and population sizeN. The selection of the parameters in the algorithm should be done with the sensitivity of the algorithm process in mind, and the selection of the parameters should be done appropriately without affecting the performance of the algorithm [20].The variation operator dominates the optimization process and is usually described as an improvement of the genetic algorithm. During the execution of the crossover operator, some genetic genes may not be retained, when the variation operator can replenish and repair the lost genes and also prevent the genetic algorithm from creating distortions during the convergence process. The frequency of mutation operations is controlled by the mutation probability, so that at a high level of mutation probability, although more individuals will be produced to make the population diversity flourish, the higher probability of mutation makes the originally good pattern becomes a poorer performance pattern. In the case where the probability of variation is at a low level, the variation operation is not conducive to the generation of new individuals, and the population diversity cannot be expanded. Although the good model can continue to maintain its performance without being destroyed, the lack of population diversity will affect the acquisition of optimal solutions, thus affecting the rational planning of soccer paths. In the actual situation, it is also easy to see that when the variation probability pn is at a low level, the stability of the solution population is in a standard level state, and when capturing the local extremes, it is difficult to obtain the global optimal solution due to the strict control of the convergence effect, and the assimilation effect of the solution population may be affected, and the diversity of the solution space is in a stable state, which is favorable to the convergence. Therefore, when improving the genetic algorithm, the variational operator pn must be controlled within a reasonable interval to ensure that its convergence is not affected.The optimization of the crossover operator also plays an irreplaceable role in improving the genetic algorithm. The crossover operator is always controlled by the crossover probability; therefore, the reasonableness of the crossover probability setting directly affects the operation of the crossover operator. If the level of crossover probability is not set reasonably, it will cause the genetic algorithm to run obstructively. The frequency of crossover operations is also controlled by the crossover probability. If the crossover probability is at a high level, the offspring will cross over sufficiently, but this does not mean that the goodness of the population will be improved. On the contrary, the good patterns in the population will be destroyed by the high frequency of crossover operations, resulting in a large generation gap. If the crossover probability is at a low level, the crossover frequency of each generation will be reduced, which will produce a smaller generation gap and thus maintain the continuity of the solution space and facilitate the acquisition of the global optimal solution to a greater extent. However, too low crossover frequency will lead to slow evolution, and even more individuals will be copied directly to the next generation, thus causing evolutionary stagnation. Therefore, when improving the genetic algorithm, it is also necessary to control the crossover operator pa within a reasonable range to prevent the serious consequences of its being too large or too small.Although not the main way to generate new individuals, the mutation operation plays a dominant role in the improvement of genetic algorithms, especially for global search ability. Crossover operations, as the main way to generate new individuals, play an irreplaceable role in improving the local search ability of genetic algorithms. In the process of improving the genetic algorithm, the probability of both is controlled within a reasonable range, and the two operators are promoted to be used in conjunction with each other, so as to find a balance between satisfying the global search ability and the local search ability, and to improve the search performance of the genetic algorithm as a whole, and then find the global optimal solution.


#### 3.2.2. Improvement of Algorithm Termination Conditions

As a complex algorithm simulating the genetic performance of biological genes, the genetic algorithm cannot keep the algorithm going indefinitely without the optimal solution being obtained. To ensure the integrity and reliability of the algorithm, it is necessary to set a reasonable indicator for the algorithm so that its execution process terminates within the given indicator. The original criteria for setting the termination condition of the genetic algorithm are not conducive to a virtuous cycle of the algorithm. Therefore, while retaining the goodness of the genetic algorithm termination condition, four termination criteria are introduced: convergence performance metric, iteration count metric, time measure metric, and human expectation metric. The details are shown in Figure6.Figure 6
Algorithm termination conditions.Convergence performance metrics: in the execution of such criteria, a reasonable judgment is made on the magnitude of convergence performance. When the algorithm is executed to the extent that the size of the degree of convergence is at a high level, it means that the algorithm has been executed to a certain extent, when the size of the individual strings in the population is almost the same, and in this case, the result obtained by terminating the algorithm is close to the global optimal solutionIteration count indicator: the size of the number of iterations of the algorithm affects the efficiency of the algorithm. When the number of iterations performed by the algorithm is at a reasonable number, the accuracy of the algorithm has reached a fairly high level. Continuing to execute iterations not only does not guarantee a higher level of accuracy but also decreases the efficiency of the algorithm, and even the results obtained gradually deviate from the global optimal solutionTime metrics: any algorithm must be completed within a specified time frame; therefore, to ensure the integrity and reliability of the algorithm, it is necessary to set a reasonable time frame for the execution of the algorithm. The time mentioned here refers not only to the length of the computation but also should include the total number of generated individuals and the iteration timeHuman expectation indicators: in addition to the above three objective indicators, human subjective indicators should also be set according to the specific situation. When the execution level of the algorithm reaches the mental expectation standard, the algorithm can be flexibly selected to terminate the algorithm, which not only saves the unnecessary amount of data operations but also saves the computing time and improves the computing efficiency


## 3.1. Introduction of Genetic Algorithm

### 3.1.1. Principle of Genetic Algorithm

The roots of genetic algorithms are among the principles of biogenetics. According to the relevant expertise in the field of biogenetics, species preserve their own traits through DNA during their own evolution and inherit the good traits they contain to the next generation of the species through the genes contained in DNA through biogenetic principles [10]. As early as the 1960s, German university professor Hardman and British geneticist Craig analyzed the complex properties contained in biological evolution. Genetic algorithms have been widely studied since 1967, when a young man named Dirk Berogh formally introduced the concept of “genetic algorithms,” and since then, they have been used as an important scientific algorithm in various scientific fields and have achieved a series of fruitful results. Since genetic algorithms are based on the principles of biogenetics, there is no major difference between the principles of genetic algorithms and genetic inheritance. According to the genetic principle of Mendel, the father of biogenetics, the essence of species evolution is the rearrangement of genes and the very low probability of genetic mutation, so that the characteristics of the previous generation will be inherited to the next generation to achieve the continuity of species in the biological world and enhance the excellence of species to enhance the survival ability of species to adapt to the environment. The basic operation of genetic algorithms, as a related principle originated from biogenetics, coincides with the essence of biogenetic species evolution described above. The first step of the genetic algorithm process is the calculation of the fitness value for the initialized population, followed by the determination of whether the fitness satisfies the convergence condition. If the fitness value satisfies the convergence condition, the direct evolutionary process ends; if the fitness value does not satisfy the convergence condition, selected operations are performed on individual adaptive functions, and then, crossover operations are performed based on the probability of change. After performing the selection, crossover, and mutation, the results are transferred to the first step to continue the calculation of the fitness value for the initialized population until the calculated fitness value satisfies the convergence condition; otherwise, the cycle of selection, crossover, and mutation steps continues [11]. The process of genetic algorithm execution is shown in Figure 5.Figure 5
The process of genetic algorithm execution.


### 3.1.2. Definition and Basic Theorem of Genetic Algorithm

When using genetic algorithms to plan the path of soccer movement, what steps to go through to ensure that the children obtained after the iterative relationships performed by the control algorithm can maintain a good trend of growth and how to determine the global optimal solution are key issues to be considered when using genetic algorithms for path planning. At this point, it is necessary to mention two fundamental theorems of genetic algorithms: the exponential growth theorem and the average fitness value theorem. To have a deeper understanding and analysis of these two theorems, it is necessary to first clarify the definitions of genetic algorithms [12].Definition 1.
Each genetic operator located in the extended bit-string space, etc., corresponds to the corresponding bit-string space, and the correspondence is in accordance with the basic principles of genetic algorithms. The genetic operator is a specific model for the set of character bit strings, reflecting the fact that the position of the string occurrence in that string set has a considerable correlation with the genetic operator [13].Definition 2.
The number of gene bits in different positions possesses different orders of the pattern, and the number of two identified gene bits in the pattern containing 1 and 2 is defined as the basic order of the pattern, which will be denoted asDH. During the operation of the genetic algorithm, the increase in the number of iterations executed causes the order of the pattern to change in the same direction as the deterministic come of the pattern. That is, as the number of orders increases, the determinism of the pattern gradually increases. Along with the decrease in the number of orders, the determinacy of the pattern also gradually decreases, and the number of samples will become more and more [14].Definition 3.
The number of bit strings appearing in the pattern can be considered as the dimensionality of the pattern, which will be denoted asBH. In genetic algorithms, the number of bits appearing in the pattern shows an inverse relationship with the change in the number of samples. That is, as the number of bit strings appearing in the pattern increases, the number of samples gradually decreases; conversely, a decrease in the number of bit strings leads to an increase in the number of sample purposes [15].Definition 4.
The distance between the first definite bit appearing in a pattern and the last definite bit appearing in the pattern can be considered as the order distance of the pattern, which will be denoted asMH. The order distance represents the length of the defined distance of the pattern and reflects the time difference between the first time identified as an occurrence and the last time identified as an occurrence [16].(1) Exponential Growth Theorem. The operation process of the genetic algorithm goes through many steps, and the initial population is subjected to the execution of algorithms such as selection, crossover, and mutation of genetic operators without satisfying the convergence conditions to continue the calculation of fitness values for the initialized population. During the execution of the above algorithm, the patterns whose children contain low order, whose defined length does not meet the standard length, and whose fitness does not reach the average level of fitness of the population will grow at a very high rate in the initial population to achieve the higher order evolution of the patterns, and their growth rate can reach exponential levels, hence the name exponential growth theorem [17].(2) Average Fitness Value Theorem. During the operation of the genetic algorithm, the patterns in the population that are at the lower order level can be evolved to the higher order after the execution of algorithms such as selection, crossover, and mutation of genetic operators. This is known as the average fitness value theorem. This theorem is more beneficial to obtain the global optimal solution to accomplish the goal of the algorithm while ensuring the normal operation of the algorithm [18].Using the initial population as a starting point, when applying genetic algorithms to the study of path planning, it is not necessary to solve the set of solutions separately for those sets for which the solution set is already known. These known solutions are used as optimal planning paths in soccer training, and the initial population is constructed by extracting individual known feasible path solutions in the scope of the problem solution space according to the principle of equal extraction. The starting moment is set tot0, so that t0=0, and the number of individuals is represented by n. Then, the n individual changes together constitute the initial population Ct, and usually, the fitness of the individuals contained in the initial population is at a low level, which needs to be improved by the genetic algorithm to simulate the biological evolution process in order to find the final optimal solution more easily to realize the optimal path planning for soccer training, thus improving the team training efficiency and helping to match the team tactical system [19].


## 3.1.1. Principle of Genetic Algorithm

The roots of genetic algorithms are among the principles of biogenetics. According to the relevant expertise in the field of biogenetics, species preserve their own traits through DNA during their own evolution and inherit the good traits they contain to the next generation of the species through the genes contained in DNA through biogenetic principles [10]. As early as the 1960s, German university professor Hardman and British geneticist Craig analyzed the complex properties contained in biological evolution. Genetic algorithms have been widely studied since 1967, when a young man named Dirk Berogh formally introduced the concept of “genetic algorithms,” and since then, they have been used as an important scientific algorithm in various scientific fields and have achieved a series of fruitful results. Since genetic algorithms are based on the principles of biogenetics, there is no major difference between the principles of genetic algorithms and genetic inheritance. According to the genetic principle of Mendel, the father of biogenetics, the essence of species evolution is the rearrangement of genes and the very low probability of genetic mutation, so that the characteristics of the previous generation will be inherited to the next generation to achieve the continuity of species in the biological world and enhance the excellence of species to enhance the survival ability of species to adapt to the environment. The basic operation of genetic algorithms, as a related principle originated from biogenetics, coincides with the essence of biogenetic species evolution described above. The first step of the genetic algorithm process is the calculation of the fitness value for the initialized population, followed by the determination of whether the fitness satisfies the convergence condition. If the fitness value satisfies the convergence condition, the direct evolutionary process ends; if the fitness value does not satisfy the convergence condition, selected operations are performed on individual adaptive functions, and then, crossover operations are performed based on the probability of change. After performing the selection, crossover, and mutation, the results are transferred to the first step to continue the calculation of the fitness value for the initialized population until the calculated fitness value satisfies the convergence condition; otherwise, the cycle of selection, crossover, and mutation steps continues [11]. The process of genetic algorithm execution is shown in Figure 5.Figure 5
The process of genetic algorithm execution.


## 3.1.2. Definition and Basic Theorem of Genetic Algorithm

When using genetic algorithms to plan the path of soccer movement, what steps to go through to ensure that the children obtained after the iterative relationships performed by the control algorithm can maintain a good trend of growth and how to determine the global optimal solution are key issues to be considered when using genetic algorithms for path planning. At this point, it is necessary to mention two fundamental theorems of genetic algorithms: the exponential growth theorem and the average fitness value theorem. To have a deeper understanding and analysis of these two theorems, it is necessary to first clarify the definitions of genetic algorithms [12].Definition 1.
Each genetic operator located in the extended bit-string space, etc., corresponds to the corresponding bit-string space, and the correspondence is in accordance with the basic principles of genetic algorithms. The genetic operator is a specific model for the set of character bit strings, reflecting the fact that the position of the string occurrence in that string set has a considerable correlation with the genetic operator [13].Definition 2.
The number of gene bits in different positions possesses different orders of the pattern, and the number of two identified gene bits in the pattern containing 1 and 2 is defined as the basic order of the pattern, which will be denoted asDH. During the operation of the genetic algorithm, the increase in the number of iterations executed causes the order of the pattern to change in the same direction as the deterministic come of the pattern. That is, as the number of orders increases, the determinism of the pattern gradually increases. Along with the decrease in the number of orders, the determinacy of the pattern also gradually decreases, and the number of samples will become more and more [14].Definition 3.
The number of bit strings appearing in the pattern can be considered as the dimensionality of the pattern, which will be denoted asBH. In genetic algorithms, the number of bits appearing in the pattern shows an inverse relationship with the change in the number of samples. That is, as the number of bit strings appearing in the pattern increases, the number of samples gradually decreases; conversely, a decrease in the number of bit strings leads to an increase in the number of sample purposes [15].Definition 4.
The distance between the first definite bit appearing in a pattern and the last definite bit appearing in the pattern can be considered as the order distance of the pattern, which will be denoted asMH. The order distance represents the length of the defined distance of the pattern and reflects the time difference between the first time identified as an occurrence and the last time identified as an occurrence [16].(1) Exponential Growth Theorem. The operation process of the genetic algorithm goes through many steps, and the initial population is subjected to the execution of algorithms such as selection, crossover, and mutation of genetic operators without satisfying the convergence conditions to continue the calculation of fitness values for the initialized population. During the execution of the above algorithm, the patterns whose children contain low order, whose defined length does not meet the standard length, and whose fitness does not reach the average level of fitness of the population will grow at a very high rate in the initial population to achieve the higher order evolution of the patterns, and their growth rate can reach exponential levels, hence the name exponential growth theorem [17].(2) Average Fitness Value Theorem. During the operation of the genetic algorithm, the patterns in the population that are at the lower order level can be evolved to the higher order after the execution of algorithms such as selection, crossover, and mutation of genetic operators. This is known as the average fitness value theorem. This theorem is more beneficial to obtain the global optimal solution to accomplish the goal of the algorithm while ensuring the normal operation of the algorithm [18].Using the initial population as a starting point, when applying genetic algorithms to the study of path planning, it is not necessary to solve the set of solutions separately for those sets for which the solution set is already known. These known solutions are used as optimal planning paths in soccer training, and the initial population is constructed by extracting individual known feasible path solutions in the scope of the problem solution space according to the principle of equal extraction. The starting moment is set tot0, so that t0=0, and the number of individuals is represented by n. Then, the n individual changes together constitute the initial population Ct, and usually, the fitness of the individuals contained in the initial population is at a low level, which needs to be improved by the genetic algorithm to simulate the biological evolution process in order to find the final optimal solution more easily to realize the optimal path planning for soccer training, thus improving the team training efficiency and helping to match the team tactical system [19].


## 3.2. Improved Genetic Algorithm

### 3.2.1. Optimizing the Parameter Selection Method

The selection of parameters occupies a key position in the genetic algorithm. The choice of different control parameters will affect the convergence effect of the algorithmic process and even the performance of the whole genetic algorithm. There are many parameters embedded in the algorithm, which include selection probability pm, crossover probability pa, variation probability pn, and population sizeN. The selection of the parameters in the algorithm should be done with the sensitivity of the algorithm process in mind, and the selection of the parameters should be done appropriately without affecting the performance of the algorithm [20].The variation operator dominates the optimization process and is usually described as an improvement of the genetic algorithm. During the execution of the crossover operator, some genetic genes may not be retained, when the variation operator can replenish and repair the lost genes and also prevent the genetic algorithm from creating distortions during the convergence process. The frequency of mutation operations is controlled by the mutation probability, so that at a high level of mutation probability, although more individuals will be produced to make the population diversity flourish, the higher probability of mutation makes the originally good pattern becomes a poorer performance pattern. In the case where the probability of variation is at a low level, the variation operation is not conducive to the generation of new individuals, and the population diversity cannot be expanded. Although the good model can continue to maintain its performance without being destroyed, the lack of population diversity will affect the acquisition of optimal solutions, thus affecting the rational planning of soccer paths. In the actual situation, it is also easy to see that when the variation probability pn is at a low level, the stability of the solution population is in a standard level state, and when capturing the local extremes, it is difficult to obtain the global optimal solution due to the strict control of the convergence effect, and the assimilation effect of the solution population may be affected, and the diversity of the solution space is in a stable state, which is favorable to the convergence. Therefore, when improving the genetic algorithm, the variational operator pn must be controlled within a reasonable interval to ensure that its convergence is not affected.The optimization of the crossover operator also plays an irreplaceable role in improving the genetic algorithm. The crossover operator is always controlled by the crossover probability; therefore, the reasonableness of the crossover probability setting directly affects the operation of the crossover operator. If the level of crossover probability is not set reasonably, it will cause the genetic algorithm to run obstructively. The frequency of crossover operations is also controlled by the crossover probability. If the crossover probability is at a high level, the offspring will cross over sufficiently, but this does not mean that the goodness of the population will be improved. On the contrary, the good patterns in the population will be destroyed by the high frequency of crossover operations, resulting in a large generation gap. If the crossover probability is at a low level, the crossover frequency of each generation will be reduced, which will produce a smaller generation gap and thus maintain the continuity of the solution space and facilitate the acquisition of the global optimal solution to a greater extent. However, too low crossover frequency will lead to slow evolution, and even more individuals will be copied directly to the next generation, thus causing evolutionary stagnation. Therefore, when improving the genetic algorithm, it is also necessary to control the crossover operator pa within a reasonable range to prevent the serious consequences of its being too large or too small.Although not the main way to generate new individuals, the mutation operation plays a dominant role in the improvement of genetic algorithms, especially for global search ability. Crossover operations, as the main way to generate new individuals, play an irreplaceable role in improving the local search ability of genetic algorithms. In the process of improving the genetic algorithm, the probability of both is controlled within a reasonable range, and the two operators are promoted to be used in conjunction with each other, so as to find a balance between satisfying the global search ability and the local search ability, and to improve the search performance of the genetic algorithm as a whole, and then find the global optimal solution.


### 3.2.2. Improvement of Algorithm Termination Conditions

As a complex algorithm simulating the genetic performance of biological genes, the genetic algorithm cannot keep the algorithm going indefinitely without the optimal solution being obtained. To ensure the integrity and reliability of the algorithm, it is necessary to set a reasonable indicator for the algorithm so that its execution process terminates within the given indicator. The original criteria for setting the termination condition of the genetic algorithm are not conducive to a virtuous cycle of the algorithm. Therefore, while retaining the goodness of the genetic algorithm termination condition, four termination criteria are introduced: convergence performance metric, iteration count metric, time measure metric, and human expectation metric. The details are shown in Figure6.Figure 6
Algorithm termination conditions.Convergence performance metrics: in the execution of such criteria, a reasonable judgment is made on the magnitude of convergence performance. When the algorithm is executed to the extent that the size of the degree of convergence is at a high level, it means that the algorithm has been executed to a certain extent, when the size of the individual strings in the population is almost the same, and in this case, the result obtained by terminating the algorithm is close to the global optimal solutionIteration count indicator: the size of the number of iterations of the algorithm affects the efficiency of the algorithm. When the number of iterations performed by the algorithm is at a reasonable number, the accuracy of the algorithm has reached a fairly high level. Continuing to execute iterations not only does not guarantee a higher level of accuracy but also decreases the efficiency of the algorithm, and even the results obtained gradually deviate from the global optimal solutionTime metrics: any algorithm must be completed within a specified time frame; therefore, to ensure the integrity and reliability of the algorithm, it is necessary to set a reasonable time frame for the execution of the algorithm. The time mentioned here refers not only to the length of the computation but also should include the total number of generated individuals and the iteration timeHuman expectation indicators: in addition to the above three objective indicators, human subjective indicators should also be set according to the specific situation. When the execution level of the algorithm reaches the mental expectation standard, the algorithm can be flexibly selected to terminate the algorithm, which not only saves the unnecessary amount of data operations but also saves the computing time and improves the computing efficiency


## 3.2.1. Optimizing the Parameter Selection Method

The selection of parameters occupies a key position in the genetic algorithm. The choice of different control parameters will affect the convergence effect of the algorithmic process and even the performance of the whole genetic algorithm. There are many parameters embedded in the algorithm, which include selection probability pm, crossover probability pa, variation probability pn, and population sizeN. The selection of the parameters in the algorithm should be done with the sensitivity of the algorithm process in mind, and the selection of the parameters should be done appropriately without affecting the performance of the algorithm [20].The variation operator dominates the optimization process and is usually described as an improvement of the genetic algorithm. During the execution of the crossover operator, some genetic genes may not be retained, when the variation operator can replenish and repair the lost genes and also prevent the genetic algorithm from creating distortions during the convergence process. The frequency of mutation operations is controlled by the mutation probability, so that at a high level of mutation probability, although more individuals will be produced to make the population diversity flourish, the higher probability of mutation makes the originally good pattern becomes a poorer performance pattern. In the case where the probability of variation is at a low level, the variation operation is not conducive to the generation of new individuals, and the population diversity cannot be expanded. Although the good model can continue to maintain its performance without being destroyed, the lack of population diversity will affect the acquisition of optimal solutions, thus affecting the rational planning of soccer paths. In the actual situation, it is also easy to see that when the variation probability pn is at a low level, the stability of the solution population is in a standard level state, and when capturing the local extremes, it is difficult to obtain the global optimal solution due to the strict control of the convergence effect, and the assimilation effect of the solution population may be affected, and the diversity of the solution space is in a stable state, which is favorable to the convergence. Therefore, when improving the genetic algorithm, the variational operator pn must be controlled within a reasonable interval to ensure that its convergence is not affected.The optimization of the crossover operator also plays an irreplaceable role in improving the genetic algorithm. The crossover operator is always controlled by the crossover probability; therefore, the reasonableness of the crossover probability setting directly affects the operation of the crossover operator. If the level of crossover probability is not set reasonably, it will cause the genetic algorithm to run obstructively. The frequency of crossover operations is also controlled by the crossover probability. If the crossover probability is at a high level, the offspring will cross over sufficiently, but this does not mean that the goodness of the population will be improved. On the contrary, the good patterns in the population will be destroyed by the high frequency of crossover operations, resulting in a large generation gap. If the crossover probability is at a low level, the crossover frequency of each generation will be reduced, which will produce a smaller generation gap and thus maintain the continuity of the solution space and facilitate the acquisition of the global optimal solution to a greater extent. However, too low crossover frequency will lead to slow evolution, and even more individuals will be copied directly to the next generation, thus causing evolutionary stagnation. Therefore, when improving the genetic algorithm, it is also necessary to control the crossover operator pa within a reasonable range to prevent the serious consequences of its being too large or too small.Although not the main way to generate new individuals, the mutation operation plays a dominant role in the improvement of genetic algorithms, especially for global search ability. Crossover operations, as the main way to generate new individuals, play an irreplaceable role in improving the local search ability of genetic algorithms. In the process of improving the genetic algorithm, the probability of both is controlled within a reasonable range, and the two operators are promoted to be used in conjunction with each other, so as to find a balance between satisfying the global search ability and the local search ability, and to improve the search performance of the genetic algorithm as a whole, and then find the global optimal solution.


## 3.2.2. Improvement of Algorithm Termination Conditions

As a complex algorithm simulating the genetic performance of biological genes, the genetic algorithm cannot keep the algorithm going indefinitely without the optimal solution being obtained. To ensure the integrity and reliability of the algorithm, it is necessary to set a reasonable indicator for the algorithm so that its execution process terminates within the given indicator. The original criteria for setting the termination condition of the genetic algorithm are not conducive to a virtuous cycle of the algorithm. Therefore, while retaining the goodness of the genetic algorithm termination condition, four termination criteria are introduced: convergence performance metric, iteration count metric, time measure metric, and human expectation metric. The details are shown in Figure6.Figure 6
Algorithm termination conditions.Convergence performance metrics: in the execution of such criteria, a reasonable judgment is made on the magnitude of convergence performance. When the algorithm is executed to the extent that the size of the degree of convergence is at a high level, it means that the algorithm has been executed to a certain extent, when the size of the individual strings in the population is almost the same, and in this case, the result obtained by terminating the algorithm is close to the global optimal solutionIteration count indicator: the size of the number of iterations of the algorithm affects the efficiency of the algorithm. When the number of iterations performed by the algorithm is at a reasonable number, the accuracy of the algorithm has reached a fairly high level. Continuing to execute iterations not only does not guarantee a higher level of accuracy but also decreases the efficiency of the algorithm, and even the results obtained gradually deviate from the global optimal solutionTime metrics: any algorithm must be completed within a specified time frame; therefore, to ensure the integrity and reliability of the algorithm, it is necessary to set a reasonable time frame for the execution of the algorithm. The time mentioned here refers not only to the length of the computation but also should include the total number of generated individuals and the iteration timeHuman expectation indicators: in addition to the above three objective indicators, human subjective indicators should also be set according to the specific situation. When the execution level of the algorithm reaches the mental expectation standard, the algorithm can be flexibly selected to terminate the algorithm, which not only saves the unnecessary amount of data operations but also saves the computing time and improves the computing efficiency


## 4. Results and Discussion

### 4.1. Evaluation of Improved Genetic Algorithm Applied to Soccer Training Path Planning

For example, as a global search algorithm, the genetic algorithm can simplify the analysis of some complex problems that occur in the actual situation, and through the reasonable execution of the number of iterations of the algorithm, the degree of convergence of the system is controlled within a reasonable range, so as to seek the global optimal solution to get the best path for soccer training and to indicate the direction for future team training. The algorithm can be used to find the optimal path for future team training. However, the genetic algorithm does have many shortcomings, for example, the problem of population size setting. If the population size is set too large, it will increase the amount of data computation, thus increasing the global search time and reducing the search efficiency. If the population size is set too small, although it can improve the convergence speed, it may cause the results to gradually deviate from the global optimal solution. In addition, the reasonableness of the coding settings and the design of the genetic operator affect the evolutionary effect and the search ability. If there are poor coding and genetic operator design problems, it will lead to insignificant evolutionary effects, which will affect the global search ability. The genetic algorithm will be prone to errors in manual operation, and unreasonable termination criteria will affect the normal evolution of the population and reduce the degree of excellence of the genetic algorithm. And there are many changes in soccer training paths, which have high requirements on the accuracy of the genetic algorithm and require the genetic algorithm to play its excellent performance to get the best soccer training path.In the evaluation of the improved genetic algorithm, there are two indicators for evaluating the performance of the algorithm: the adaptation performance indicator and the average accumulation of performance indicator.


#### 4.1.1. Adaptability Performance Index

The fitness performance metric is the average value of fitness developed from the first generation to the current generation during the operation of the genetic algorithm. Noting the fitness performance index asZex, Zex can be expressed as
(1)Zex=1T∑iTFet.In Equation (1), Zex represents the fitness performance of strategy x under background e, where Fet denotes the average fitness function value at moment t or generation t in the population and fet is the result of Fet after conducting the derivation.


#### 4.1.2. Performance Average Accumulation Index

Performance average accumulation index refers to the average of performance accumulation during the operation of the genetic algorithm when it is in the best performance. The performance average accumulation index can be written asLex, and its function value can be expressed in
(2)Lex=1T∑1Tfet.In Equation (2), Lex represents the average performance of strategy x under background e, where T denotes the iteration period and fet is the result of Fet after performing derivation.After the above analysis, it can be concluded that the fitness performance index reflects the overall dynamic evolution of the average fitness of the population during the genetic process of the genetic algorithm in future generations. The performance average accumulation index reflects the overall best performance of the cumulative change process in the process of genetic algorithm, which reflects the characteristics of genetic algorithm with strong convergence.In the process of executing genetic algorithm using genetic operators, there will be a large number of parent individuals and a series of offspring individuals, and how to judge the relationship between these individuals is a problem that must be considered in executing genetic algorithm, which involves the value of the correlation coefficient of genetic operators. In the operation of genetic operators, the correlation function values are obtained by simulating the evolution of the genetic operator on one or more parents. As with biogenetic principles, there are many connections between the two, and they are strongly correlated. This correlation can be expressed by the correlation coefficient, as shown in(3)PSF,SZ=CovSF,SZσSFσSz.In the above equation,SZ represents the average condition of the offspring. COVSF,SZ represents the covariance, and ßSF represents the standard deviation of the condition under random conditions. The ability of a new individual to approach a random search depends on the absolute value of the correlation coefficient. If the correlation coefficient of the operator is more absolute, then the ability to generate new individuals will gradually deviate from the random search. If the absolute value of the correlation coefficient is higher, then the ability of the genetic operator to create new individuals will be higher. Therefore, the correlation factor can be used to evaluate the searchability of the genetic operator.


### 4.2. Soccer Training Path Planning Based on Improved Genetic Algorithm

The first step to consider when planning a soccer training path is to encode the parameters of the problem. The field of play is constructed as a two-dimensional planar coordinate map, and the positions of the players and the soccer ball as well as the goal frame can be measured in terms of coordinates. The center point of the pitch where the ball is kicked off is the origin of the coordinates. All paths from the starting point to the goal point can be represented as vectors, so that not only the size of the path distance can be observed but also its direction and location characteristics can be determined. A reasonable path is not the shorter the better, but its effectiveness in passing or shooting should be considered. The optimal paths under various tactical systems can be obtained by the operation of genetic algorithms.As can be understood in the section of the article on materials and methods, when using genetic algorithms for path planning, it is possible to find the optimal path for soccer training, although it is likely to use iterative genetics and the operation of genetic operators to obtain the global optimal solution. However, for a realistic sport like soccer, which is full of various possibilities, the playing field confrontation environment can change drastically at any time and is full of various unknown possibilities. The genetic algorithm needs to refine a large amount of data in order to maximize its performance, which requires a certain amount of time and obviously cannot meet the real-time needs of soccer. Based on the all-round analysis of the pitch environment, it can be understood that the positions between opponents and teammates change in real time and are unpredictable when both teams play against each other. Passes between teammates may be intercepted by opposing players who suddenly appear. In such a case, an improved genetic algorithm is applied to select the optimal path to avoid interception by opposing players when the team players pass the ball. And a simple data calculation is performed to find the global optimal solution, that is, to find the optimal path for soccer training. The improved genetic algorithm can greatly improve the efficiency of selecting the optimal path compared to the original genetic algorithm. Therefore, it is especially important to summarize, classify, and discuss the various contingencies that occur on the field of play. When the field is in a relaxed and simple environment, the control method of fuzzy logic can be used to find the best trajectory of soccer; when the field is in a complex and changing environment, the superiority of genetic algorithm can be used for path planning to find the best trajectory of soccer and help the team to develop a reasonable training strategy.


### 4.3. Genetic Algorithm Simulation Result Analysis

From the above material, it can be seen that there are different methods in selecting the optimal path for soccer trajectory in different stadium environments, which can save time, improve efficiency, and enhance the players’ resilience. Simulation training using the genetic algorithm requires initialization and assignment of many parameters that appear in the genetic algorithm. Two of the more frequently used parameters in this context are the simulation algebra and the initial population benefit, both of which affect the algorithm execution time and the algorithm results. Before the algorithm is simulated, the relevant parameters need to be prepared for assignment. The value of simulation population sizeN is set to 100, and the length of parameter string L is set to 20; when the simulation algebra T1=10, the simulation results are shown in Figure 7. With the simulation population size and parameter string length unchanged, when the simulation algebra T2=100, the simulation results are shown in Figure 8.Figure 7
Simulation results of genetic algorithm whenT1=10.Figure 8
Genetic algorithm simulation results whenT2=100.Comparing the above two simulation results, it is easy to find that the change of simulation algebra will affect the genetic algorithm when the simulation population size and string length are constant. When the value of simulation generation is small, such asT1=10, the genetic algorithm will be transient and stable within a certain range, but the solution appears only as a local optimal solution, and the global optimal solution cannot be obtained. When the value of the simulation algebra is set to a large value, such as T2=100, the genetic algorithm will run in advance of the optimal solution, so the high setting of the simulation algebra will waste memory space and a lot of data operations, which consumes time and energy. Therefore, it can be concluded that the setting of simulation algebra should be controlled within a reasonable range, and the adverse effects of too high or too low algebra on the algorithm should be avoided. In addition, the setting of the initialization size of the population also appears to be very important; when the size is too small and the known solutions are too few, even if the algorithm can roughly calculate the final result, the output result is not the global optimal solution. The improved genetic algorithm is faster than the traditional genetic algorithm when the course is in a simple and relaxed environment, reducing the amount of data operations and computing time and improving the timeliness of the system. Through the above method, the flexible algorithm operation according to the field environment can plan the path of soccer reasonably and seek the global optimal solution to help the team to build the tactical planning and winning strategy of offense and defense.


## 4.1. Evaluation of Improved Genetic Algorithm Applied to Soccer Training Path Planning

For example, as a global search algorithm, the genetic algorithm can simplify the analysis of some complex problems that occur in the actual situation, and through the reasonable execution of the number of iterations of the algorithm, the degree of convergence of the system is controlled within a reasonable range, so as to seek the global optimal solution to get the best path for soccer training and to indicate the direction for future team training. The algorithm can be used to find the optimal path for future team training. However, the genetic algorithm does have many shortcomings, for example, the problem of population size setting. If the population size is set too large, it will increase the amount of data computation, thus increasing the global search time and reducing the search efficiency. If the population size is set too small, although it can improve the convergence speed, it may cause the results to gradually deviate from the global optimal solution. In addition, the reasonableness of the coding settings and the design of the genetic operator affect the evolutionary effect and the search ability. If there are poor coding and genetic operator design problems, it will lead to insignificant evolutionary effects, which will affect the global search ability. The genetic algorithm will be prone to errors in manual operation, and unreasonable termination criteria will affect the normal evolution of the population and reduce the degree of excellence of the genetic algorithm. And there are many changes in soccer training paths, which have high requirements on the accuracy of the genetic algorithm and require the genetic algorithm to play its excellent performance to get the best soccer training path.In the evaluation of the improved genetic algorithm, there are two indicators for evaluating the performance of the algorithm: the adaptation performance indicator and the average accumulation of performance indicator.


### 4.1.1. Adaptability Performance Index

The fitness performance metric is the average value of fitness developed from the first generation to the current generation during the operation of the genetic algorithm. Noting the fitness performance index asZex, Zex can be expressed as
(1)Zex=1T∑iTFet.In Equation (1), Zex represents the fitness performance of strategy x under background e, where Fet denotes the average fitness function value at moment t or generation t in the population and fet is the result of Fet after conducting the derivation.


### 4.1.2. Performance Average Accumulation Index

Performance average accumulation index refers to the average of performance accumulation during the operation of the genetic algorithm when it is in the best performance. The performance average accumulation index can be written asLex, and its function value can be expressed in
(2)Lex=1T∑1Tfet.In Equation (2), Lex represents the average performance of strategy x under background e, where T denotes the iteration period and fet is the result of Fet after performing derivation.After the above analysis, it can be concluded that the fitness performance index reflects the overall dynamic evolution of the average fitness of the population during the genetic process of the genetic algorithm in future generations. The performance average accumulation index reflects the overall best performance of the cumulative change process in the process of genetic algorithm, which reflects the characteristics of genetic algorithm with strong convergence.In the process of executing genetic algorithm using genetic operators, there will be a large number of parent individuals and a series of offspring individuals, and how to judge the relationship between these individuals is a problem that must be considered in executing genetic algorithm, which involves the value of the correlation coefficient of genetic operators. In the operation of genetic operators, the correlation function values are obtained by simulating the evolution of the genetic operator on one or more parents. As with biogenetic principles, there are many connections between the two, and they are strongly correlated. This correlation can be expressed by the correlation coefficient, as shown in(3)PSF,SZ=CovSF,SZσSFσSz.In the above equation,SZ represents the average condition of the offspring. COVSF,SZ represents the covariance, and ßSF represents the standard deviation of the condition under random conditions. The ability of a new individual to approach a random search depends on the absolute value of the correlation coefficient. If the correlation coefficient of the operator is more absolute, then the ability to generate new individuals will gradually deviate from the random search. If the absolute value of the correlation coefficient is higher, then the ability of the genetic operator to create new individuals will be higher. Therefore, the correlation factor can be used to evaluate the searchability of the genetic operator.


## 4.1.1. Adaptability Performance Index

The fitness performance metric is the average value of fitness developed from the first generation to the current generation during the operation of the genetic algorithm. Noting the fitness performance index asZex, Zex can be expressed as
(1)Zex=1T∑iTFet.In Equation (1), Zex represents the fitness performance of strategy x under background e, where Fet denotes the average fitness function value at moment t or generation t in the population and fet is the result of Fet after conducting the derivation.


## 4.1.2. Performance Average Accumulation Index

Performance average accumulation index refers to the average of performance accumulation during the operation of the genetic algorithm when it is in the best performance. The performance average accumulation index can be written asLex, and its function value can be expressed in
(2)Lex=1T∑1Tfet.In Equation (2), Lex represents the average performance of strategy x under background e, where T denotes the iteration period and fet is the result of Fet after performing derivation.After the above analysis, it can be concluded that the fitness performance index reflects the overall dynamic evolution of the average fitness of the population during the genetic process of the genetic algorithm in future generations. The performance average accumulation index reflects the overall best performance of the cumulative change process in the process of genetic algorithm, which reflects the characteristics of genetic algorithm with strong convergence.In the process of executing genetic algorithm using genetic operators, there will be a large number of parent individuals and a series of offspring individuals, and how to judge the relationship between these individuals is a problem that must be considered in executing genetic algorithm, which involves the value of the correlation coefficient of genetic operators. In the operation of genetic operators, the correlation function values are obtained by simulating the evolution of the genetic operator on one or more parents. As with biogenetic principles, there are many connections between the two, and they are strongly correlated. This correlation can be expressed by the correlation coefficient, as shown in(3)PSF,SZ=CovSF,SZσSFσSz.In the above equation,SZ represents the average condition of the offspring. COVSF,SZ represents the covariance, and ßSF represents the standard deviation of the condition under random conditions. The ability of a new individual to approach a random search depends on the absolute value of the correlation coefficient. If the correlation coefficient of the operator is more absolute, then the ability to generate new individuals will gradually deviate from the random search. If the absolute value of the correlation coefficient is higher, then the ability of the genetic operator to create new individuals will be higher. Therefore, the correlation factor can be used to evaluate the searchability of the genetic operator.


## 4.2. Soccer Training Path Planning Based on Improved Genetic Algorithm

The first step to consider when planning a soccer training path is to encode the parameters of the problem. The field of play is constructed as a two-dimensional planar coordinate map, and the positions of the players and the soccer ball as well as the goal frame can be measured in terms of coordinates. The center point of the pitch where the ball is kicked off is the origin of the coordinates. All paths from the starting point to the goal point can be represented as vectors, so that not only the size of the path distance can be observed but also its direction and location characteristics can be determined. A reasonable path is not the shorter the better, but its effectiveness in passing or shooting should be considered. The optimal paths under various tactical systems can be obtained by the operation of genetic algorithms.As can be understood in the section of the article on materials and methods, when using genetic algorithms for path planning, it is possible to find the optimal path for soccer training, although it is likely to use iterative genetics and the operation of genetic operators to obtain the global optimal solution. However, for a realistic sport like soccer, which is full of various possibilities, the playing field confrontation environment can change drastically at any time and is full of various unknown possibilities. The genetic algorithm needs to refine a large amount of data in order to maximize its performance, which requires a certain amount of time and obviously cannot meet the real-time needs of soccer. Based on the all-round analysis of the pitch environment, it can be understood that the positions between opponents and teammates change in real time and are unpredictable when both teams play against each other. Passes between teammates may be intercepted by opposing players who suddenly appear. In such a case, an improved genetic algorithm is applied to select the optimal path to avoid interception by opposing players when the team players pass the ball. And a simple data calculation is performed to find the global optimal solution, that is, to find the optimal path for soccer training. The improved genetic algorithm can greatly improve the efficiency of selecting the optimal path compared to the original genetic algorithm. Therefore, it is especially important to summarize, classify, and discuss the various contingencies that occur on the field of play. When the field is in a relaxed and simple environment, the control method of fuzzy logic can be used to find the best trajectory of soccer; when the field is in a complex and changing environment, the superiority of genetic algorithm can be used for path planning to find the best trajectory of soccer and help the team to develop a reasonable training strategy.


## 4.3. Genetic Algorithm Simulation Result Analysis

From the above material, it can be seen that there are different methods in selecting the optimal path for soccer trajectory in different stadium environments, which can save time, improve efficiency, and enhance the players’ resilience. Simulation training using the genetic algorithm requires initialization and assignment of many parameters that appear in the genetic algorithm. Two of the more frequently used parameters in this context are the simulation algebra and the initial population benefit, both of which affect the algorithm execution time and the algorithm results. Before the algorithm is simulated, the relevant parameters need to be prepared for assignment. The value of simulation population sizeN is set to 100, and the length of parameter string L is set to 20; when the simulation algebra T1=10, the simulation results are shown in Figure 7. With the simulation population size and parameter string length unchanged, when the simulation algebra T2=100, the simulation results are shown in Figure 8.Figure 7
Simulation results of genetic algorithm whenT1=10.Figure 8
Genetic algorithm simulation results whenT2=100.Comparing the above two simulation results, it is easy to find that the change of simulation algebra will affect the genetic algorithm when the simulation population size and string length are constant. When the value of simulation generation is small, such asT1=10, the genetic algorithm will be transient and stable within a certain range, but the solution appears only as a local optimal solution, and the global optimal solution cannot be obtained. When the value of the simulation algebra is set to a large value, such as T2=100, the genetic algorithm will run in advance of the optimal solution, so the high setting of the simulation algebra will waste memory space and a lot of data operations, which consumes time and energy. Therefore, it can be concluded that the setting of simulation algebra should be controlled within a reasonable range, and the adverse effects of too high or too low algebra on the algorithm should be avoided. In addition, the setting of the initialization size of the population also appears to be very important; when the size is too small and the known solutions are too few, even if the algorithm can roughly calculate the final result, the output result is not the global optimal solution. The improved genetic algorithm is faster than the traditional genetic algorithm when the course is in a simple and relaxed environment, reducing the amount of data operations and computing time and improving the timeliness of the system. Through the above method, the flexible algorithm operation according to the field environment can plan the path of soccer reasonably and seek the global optimal solution to help the team to build the tactical planning and winning strategy of offense and defense.


## 5. Conclusion

This paper takes soccer, the world’s number one soccer sport, as the entry point and artificial intelligence as the research background, to improve the genetic algorithm by extracting soccer training functions and applying the improved algorithm to the planning of soccer training paths to help players find reasonable and effective optimal paths for passing or shooting.This paper first introduces the superiority and development status of artificial intelligence in the introduction and research background section and proposes to apply artificial intelligence technology to the research of soccer training path planning, which makes the research content of this paper have some realistic basis. Then, in Research Methods and Materials, the principles and definitions of genetic algorithms and the basic theorems are introduced, and improvements in the selection of optimization parameters and algorithm termination conditions are made to create an efficient new genetic algorithm to find the global optimal solution. In Results and Discussion of the paper, the application of the improved genetic algorithm in soccer training programs is first evaluated; then, the results of the application of the improved genetic algorithm are analyzed, and finally, the simulation results of the genetic algorithm are given. It is shown that the genetic improved algorithm is able to plan the path of soccer movement reasonably according to the flexibility of the playing field environment. In view of the limitation of research time and personal ability, there are still some incompleteness in this paper. There are still many problems to be further explored and discovered for the optimal path planning of soccer training by the improved genetic algorithm.



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*Source: 8375916-2022-09-24.xml*