Urban Street Landscape Design System Driven by Optimized Genetic Algorithm

Abstract

With the acceleration of urbanization in China, urban and rural construction land continues to expand. In order to avoid “urban street view disease” in the process of urbanization, dynamic simulation of urban street view land is of great significance. The dynamic model of urban street view land can simulate the dynamic expansion process of street view concretely and effectively, and predict the distribution and form of urban street view land in the future more accurately. How to improve the accuracy of urban street view dynamic model based on geographic cellular automata has always been the direction of unremitting exploration in academic circles. In view of the limitations of the traditional genetic algorithm model, this study constructs a genetic algorithm-logistic regression model, and takes Chengdu-Chongqing Economic Zone as a case, and optimizes the best regression coefficient of the logistic regression model through genetic algorithm fitting, so as to realize the accurate simulation of the dynamic change of urban street view land.