Automatic search of machine learning models based on intelligent computing

This paper focuses on the research of automatic search of machine learning models using intelligent computing technology. In the current era of big data and artificial intelligence, selecting and optimizing the right machine learning model is critical to improving the accuracy of data analysis and prediction. However, traditional manual methods are time-consuming and inefficient when dealing with a large number of models and hyperparameter combinations. In this paper, an automatic search strategy for machine learning models based on intelligent computing is proposed. By integrating reinforcement learning, evolutionary algorithm and meta-learning, the model structure and hyperparameter space are effectively explored. This strategy aims to find the optimal model configuration adaptively according to the specific task requirements and data characteristics, thus breaking through the limitations of manual model setting and significantly improving the efficiency and performance of model training. The experimental results show that the automatic search method proposed in this study can automatically discover highly optimized machine learning models on multiple public data sets, and outperforms traditional manual parameter tuning and some existing automatic tuning methods on multiple evaluation indicators. This research achievement not only helps to promote the popularization and deepening of machine learning model application, but also provides a new idea and practice path for the construction of intelligent and automated machine learning system in the future. At the same time, it also reveals the great potential and broad application prospect of intelligent computing in solving complex optimization problems.