基于量子遗传的超参数自动调优算法的设计与实现

Design and implementation of an automatic hyper-parameter tuning algorithm based on quantum genetics

机器学习伴随着海量数据的支持以及强大的计算能力为其提供了强有力的保证下不断地向前发展,训练过程变得更加高效便捷。在此基础上,机器学习算法的超参数对其性能的影响是非常巨大的,因此对众多的超参数进行优化选择就自然有了强烈的需求。由此本文提出了一种基于量子遗传的超参数自动调优算法,实验表明,在针对多种机器学习模型的超参数调优问题上,既解决了一般随机算法的不稳定性的问题,也解决了一般进化算法迭代缓慢、收敛速度较低的问题,并且通过实验结果表明取得了不错的效果。

With the development of big data and the improvement of computing power,machine learning has been continuously developing,and the training process has become more efficient and convenient.On this basis,the hyper-parameter of machine learning algorithms has a great impact on their performance,so there is a strong demand for optimizing and selecting a large number of hyper-parameter.Thus this paper puts forward a kind of automatic tuning super parameter based on Quantum Genetic Algorithm.The experiments show that for a variety of machine learning model on the above parameters tuning problem,the instability of both randomized algorithms of the general problem is solved,also the problems of slow iteration and low convergence speed of the general evolutionary algorithm are solved,therefore good results have been achieved.