A Novel Approach for Evaluating Web Page Performance Based on Machine Learning Algorithms and Optimization Algorithms

Abstract

The ever-increasing demand for faster, more efficient web pages necessitates advanced techniques for evaluating and improving web page performance. This paper proposes a novel approach that utilizes machine learning algorithms in combination with optimization techniques to evaluate and enhance web page performance. We explore various machine learning models, such as regression models, decision trees, and neural networks, to predict performance metrics like load time, response time, and resource consumption based on different web page configurations. Furthermore, we apply optimization algorithms, such as genetic algorithms or simulated annealing, to suggest optimal modifications that could minimize load time and maximize resource efficiency. By achieving a prediction accuracy of 92% for critical metrics such as page load time using the Random Forest model, and reducing average load times by 25% through genetic algorithm-driven optimization. The results of this approach provide both web developers and businesses with insights into how to enhance user experience while maintaining high levels of website performance.