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Research on optimal allocation of accounting teaching resources in cloud computing environment based on genetic algorithm framework design

Qianbao Zheng

Qianbao Zheng

School of Financial Management, Chengdu Ginkgo Hotel Management CollegeChengdu, China
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Zheng, Qianbao
 y 
He Huang

He Huang

School of Financial Management, Chengdu Ginkgo Hotel Management CollegeChengdu, China
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Huang, He
  
19 mar 2025
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Publicado en línea: 19 mar 2025
Recibido: 15 nov 2024
Aceptado: 18 feb 2025
DOI: https://doi.org/10.2478/amns-2025-0452
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© 2025 Qianbao Zheng et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Intelligent group volume flow chart
Intelligent group volume flow chart

Figure 2.

Flow chart of population initialization
Flow chart of population initialization

Figure 3.

Improves the genetic algorithm to solve the group volume process
Improves the genetic algorithm to solve the group volume process

Figure 4.

GA and AGA average fitness chart
GA and AGA average fitness chart

Figure 5.

AGA and this method average fitness chart
AGA and this method average fitness chart

Figure 6.

The adaptive crossover and probability of the AG algorithm
The adaptive crossover and probability of the AG algorithm

Figure 7.

The adaptive crossover and probability of the this algorithm
The adaptive crossover and probability of the this algorithm

AGA group volume information

Serial number Iteration number Group time/ms Expectation difficulty Actual difficulty Accuracy rate
1 300 254 48.8 44.5 91.2%
2 300 258 49.5 44.4 89.7%
3 300 265 48.8 44.1 90.4%
4 300 263 49.5 44.6 90.1%

Examination paper information sheet

Serial number Simplicity Simpler Medium Harder Difficulty Expectation difficulty
1 6 5 10 4 5 48.8
2 6 5 10 4 5 49.5
3 6 15 18 3 3 48.8
4 6 15 18 3 3 49.5

System parameter information table

Classification Entry Concretely
Hardware parameter Operating system Windows10
Running memory 10G
processor i7-7500U
Software parameter Running language Python3
Running tool Visual Studio Code

GA group volume information

Serial number Iteration number Group time/ms Expectation difficulty Actual difficulty Accuracy rate
1 300 235 48.8 42.5 87.1%
2 300 257 49.5 43.4 87.7%
3 300 256 48.8 44.1 90.4%
4 300 252 49.5 42.6 86.1%

This algorithm group volume information

Serial number Iteration number Group time/ms Expectation difficulty Actual difficulty Accuracy rate
1 300 260 48.8 45.8 93.9%
2 300 268 49.5 46.4 93.7%
3 300 264 48.8 46.9 96.1%
4 300 268 49.5 45.8 92.5%

Model ability estimation accuracy analysis index

Test category Two test mean Last traditional test
Minimum grade -0.25 51
Average performance 1.08 78
Top score 2.26 100
ABS 0.265 /
RMSE 0.158 /
RMSD 0.144 /

Group volume quality analysis index

Test category Two test mean Last traditional test
Test quantity 11.56 1:1:1
Content expectation ratio 21 4:5:1
Test overlap 0.25 1
The topic is time-consuming (mean) 0.05s Over a week
Panel rationality Problem difficulty Mean 1.02 1.25
SD 0.93 13.56
Expected capacity Mean 1.01 125
SD 0.95 7.58
Correlation coefficient 0.001 0.001
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