An Exploration of Tourism Development Based on Genetic Algorithm and Multiple Linear Regression Modeling

This paper focuses on tourism development based on genetic algorithm and multiple linear regression model. Firstly, the data are preprocessed, including identifying outliers and processing them through QR algorithm, as well as standardizing and normalizing the data for transformation. Second, polynomial regression models were constructed for tourism development analysis, defining target variables and input features, and ridge regression models were built for prediction through polynomial feature generation, etc., and model effects were evaluated through residual and error analysis; meanwhile, multiple linear regression models were constructed to analyze glacier recession, defining relevant variables and evaluating parameters. Finally, in order to achieve the comprehensive optimization objectives of maximizing tourism revenue and minimizing glacier recession, genetic algorithm is used to optimize it, and through a series of genetic operations, synergistic optimization of multiple parameters is achieved to effectively deal with the trade-off situation between tourism development and glacier recession.