Back to Search Start Over

Protection and Inheritance of Traditional Culture in Urbanization Construction Based on Genetic Algorithm under the Concept of Environmental Protection.

Authors :
Guo, Lin
Source :
Journal of Environmental & Public Health. 6/16/2022, p1-10. 10p.
Publication Year :
2022

Abstract

Chinese traditional culture is a typical "moral culture," and traditional morality is the core and essence of culture. However, in recent years, the phenomenon of following the trend of rural construction has been particularly serious, and many villages have lost their original features. To solve the above problems, the genetic algorithm can be used to further explore the traditional culture of urbanization construction. A genetic algorithm is a natural evolutionary process that imitates natural selection and genetic operation in nature to obtain optimal solution, in which genetic operation mainly includes the processes of gene replication, crossover, and mutation. This paper studies the traditional culture of urbanization construction based on the genetic algorithm under the concept of environmental protection. Among the accuracy of urban construction land expansion, in 2018, the accuracy of ant colony algorithm, data mining algorithm, and particle swarm optimization algorithm is 58%, 51.8%, and 56.7%, respectively. The accuracy of this genetic algorithm is as high as 58.8%. It can be seen that the genetic algorithm in this paper has the highest accuracy in the expansion of urban construction land. Therefore, in the process of large-scale urbanization based on the genetic algorithm, we should pay attention to not being separated from traditional culture, not letting farmers lose their regional culture, local culture, and grassroots culture, and protecting the cultural-ecological environment on which these cultures depend. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
16879805
Database :
Academic Search Index
Journal :
Journal of Environmental & Public Health
Publication Type :
Academic Journal
Accession number :
157490892
Full Text :
https://doi.org/10.1155/2022/5844732