Comparative Research of Hyper-Parameters Mathematical Optimization Algorithms for Automatic Machine Learning in New Generation Mobile Network

Under the configuration of the new generation communication network, the algorithm based on machine learning has been widely used in network optimization and mobile user behavior prediction. Therefore, the optimization method with hyper-parameters will have a huge development space in the field of mobile communication network. However, for non-professionals, the bottleneck that restricts the further development and application of the whole machine learning is the selection of suitable machine learning algorithm and the determination of suitable algorithm hyper-parameters. Researchers have proposed to use automatic machine learning algorithm to solve this remarkable problem. This article forms a technical manual that can be easily searched by researchers with summarizing related hyper-parameter optimization methods and proposing the corresponding algorithm framework. Moreover, through the comparison of related optimization methods, we highlight the characteristics and deficiencies of related algorithms in the new generation of mobile networks, and put forward suggestions for future improvement.

Automated summary

Under the configuration of the new generation communication network, machine learning-based algorithm has been widely applied in network optimization and mobile user behavior prediction. This creates a significant development space for optimization methods with hyper-parameters in the field of mobile communication network. However, for non-professionals, the bottleneck that restricts the further development and application of machine learning lies in the selection of suitable machine learning algorithm and the determination of suitable algorithm hyper-parameters. Researchers propose the use of automatic machine learning algorithm to address this issue. This article presents a technical manual that can be easily searched by researchers, summarizing related hyper-parameter optimization methods and proposing the corresponding algorithm framework. Additionally, through the comparison of related optimization methods, the characteristics and deficiencies of algorithms in the new generation of mobile networks are highlighted, with suggestions for future improvement.