The article uses the Spss statistical analysis software to establish a multiple linear regression model of short-term stock price changes in domestic agricultural listed companies. It uses a stable time series based on the ARMA model for stable agricultural value-added, fiscal expenditure and market interest rates. The regression method is used to study its impact on the stock price index. Compared with the existing stock forecasting methods, this method has simple data collection and no specific requirements for data selection, and the prediction results have a high degree of fit. Therefore, this method is suitable for most stocks.

#### Keywords

- K-GA algorithm
- illustration art
- art design
- operator optimisation
- cluster optimisation

#### MSC 2010

- I5J21

In the traditional art design process, if designers have inspiration and ideas, they must use tools to transfer their design to reality. This requires human labour, so the quantity and quality of creation can be limited. living. Moreover, multiple works cannot be produced at the same time, which is a disadvantage of traditional creative methods. Now, because computers provide the level of technology needed in the field of art creation, they can help designers develop their inspirations well, and they can use less time to realise their inspirations [1].

The so-called evolutionary design refers to the use of a new evolutionary computing method in the concept of computer design, which can be fully used in the field of design. The first computer evolution program ‘Blind Watchmaker’ in history was proposed by Rudolph; it is mainly used to simulate tree clusters. Fogarty puts forward the theory that genetic algorithms (GAs) can be used in the improvement and optimisation of design, and explains the theoretical framework of the entire design [2]. His design system can be used for the production of vehicles and seats. The use of computer-aided evolutionary design in the field has opened a new door for evolutionary art. In addition to this, there are people who have applied computer simulation technology in more fields, such as table lamps and sculptures in artworks, making the application of this technology more extensive. There are also people who use GAs in the design of building plans. Andrew Row bottom wrote a program called Form, which for the first time turned evolutionary design into a three-dimensional (3D) representation [3]. Matthew Lewis used the technology of evolutionary models in the fields of compositing colours, making cartoon characters, changing fonts and designing exterior shapes of cars, and successfully created interactive design systems. Domestic research and development scholars such as Liu Hong, Tang Mingxi and Liu Xiyu use computers to support innovative design of appearance modelling. Sun Shouqian, Zhang Lishan, Huang Qi and others used CAD to realise the problems of colour expression, design sketches and structural patterns [4].

In order to optimise the standard GA and improve some existing shortcomings, this paper uses a design model of illustration art, and the model is based on cluster optimisation and operator GA, and the model is simulated through more innovative experiments [5].

These equations are shaped as

In some cases, Volterra integral equations of the first type can usually be transformed into solutions of the Volterra integral equations of the second type. Generally, the two sides of the equation are differentiated when the equations

The form of the second type of equation is:

Iteration: Hypothesis

Then:
_{n}

Suppose the equation has a solution of the form

Iteration: Hypothesis

Then
_{n}

The form of the nonlinear Volterra integral equation of the second kind is as follows:

It is shaped as:

For the first type of Volterra integral equation, that is, Equation

The solution of the nonlinear Volterra integral equation Laplace transform is

Here we mainly discuss the numerical calculation of

To calculate the integral _{1} < _{2} < ⋯ < _{n}_{+1} ≤ _{1}), _{2}), . . . , _{n}_{+1}) and you can use the linear combination of _{1}), _{2}), . . . , _{n}_{+1}) to get the integral An approximate solution of _{n}_{n}_{1} = _{2} =

We assume that the localisation of the function in the integral in question is [_{1} < _{2} < ⋯ < _{n}_{+1} = _{i}_{+1},_{i}

Here, we will discuss the role of a complete function system in _{2}(_{i}^{2}[_{i}_{i}^{n}_{i}φ_{i}

The integral kernel series expansion method, also known as the degenerate kernel approximation method, uses a certain expansion method to expand the non-degraded kernel into an approximately degraded kernel. The general expansion methods include Taylor series and Fourier series expansions. A linearly independent function system for the approximate expansion of a known function in ^{2} [

The following estimation methods are used to approximate the error of the integral equation kernel with a degenerate kernel:

The solution kernel

_{i}_{m}_{im}_{i}, x_{m}_{i}_{i}

We divide the integration interval [_{i},x_{i}_{+1}],

First, in a _{i}_{n}

In order to verify the feasibility of the algorithm, the unimodal and multimodal functions are used to improve the standard GA and the improvements proposed in this paper. The algorithm K-GA is tested. The unimodal function is as follows:

Algorithm simulation results of test functions.

_{1} |
_{2} |
||||
---|---|---|---|---|---|

Cross probability | 1 | 56.28 | 7.32 | 12.67 | 4.71 |

0.9 | 57.03 | 7.84 | 13.61 | 5.38 | |

0.8 | 60.05 | 8.09 | 13.88 | 6.37 | |

0.7 | 61.44 | 8.67 | 15.58 | 6.94 | |

0.6 | 62.49 | 9.52 | 19.71 | 7.54 | |

0.5 | 63.58 | 10.49 | 20.85 | 8.06 |

It can be seen from Table 1 that for the two functions, when the crossover probability is set to 1, for any one test function, the average running generation number is the least, and the average running generation number decreases gradually as the crossover probability increases. Therefore, the greater the crossover probability, the greater the probability of producing outstanding individuals, which improves the performance of the GA.

Taking the illustration design of the hexagon fractal as an example, the design results using the standard GA and the improved algorithm proposed in this paper are shown in Figures 1 and 2:

By comparing Picture 1 and Picture 2, we can find that the optimised algorithm mentioned in the article can be used in illustration art and is innovative.

Until now, the development of computer technology has continued to advance globally, and the use of evolutionary technology to help product innovation and design is an important way. How to improve the algorithm of this computer technology and how this technology can be better used and practiced in the field of design has been valued and studied by more and more people. In this article, I wrote out some shortcomings of some standard GAs, and conducted an illustration art design experiment on the operator and cluster optimisation GA. It was explained after the experiment that the algorithm after optimisation is more creative than design algorithms without optimisation.

#### Algorithm simulation results of test functions.

_{1} |
_{2} |
||||
---|---|---|---|---|---|

Cross probability | 1 | 56.28 | 7.32 | 12.67 | 4.71 |

0.9 | 57.03 | 7.84 | 13.61 | 5.38 | |

0.8 | 60.05 | 8.09 | 13.88 | 6.37 | |

0.7 | 61.44 | 8.67 | 15.58 | 6.94 | |

0.6 | 62.49 | 9.52 | 19.71 | 7.54 | |

0.5 | 63.58 | 10.49 | 20.85 | 8.06 |

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