Performance metrics of comparative experiment.

Gene expression programming (GEP) is one of the most prominent algorithms in function mining. In order to obtain a more accurate function model in configuration parameters-execution efficiency (CP-EE) of map-reduce job in the high-speed railway catenary monitoring system, this paper proposes a novel algorithm, called GEP based on multi-strategy (MS-GEP). Compared to traditional GEP, the proposed algorithm can escape premature convergence and jump out of local optimum. First, an adaptive mutation rate is designed according to the evolutionary generations, population diversity, and individual fitness values. A manual intervention strategy is then proposed to determine whether the algorithm enters the dilemma of local optimum based on the generations of population evolutionary stagnation. Finally, the average quality of the population is changed by randomly replacing individuals, and the ancestral population is traced to change the evolutionary direction. The experimental results on the benchmarks of function mining show that the proposed MS-GEP has better solution quality and higher population diversity than other GEP algorithms. Furthermore, the proposed MS-GEP has higher accuracy on the function model of CP-EE of high-speed railway catenary monitoring system than other commonly used algorithms in the field of function mining.

基因表达式编程（Gene expression programming，GEP）是函数挖掘领域最具代表性的算法之一。为了在高速铁路接触网监测系统的MapReduce作业配置参数-执行效率（CP-EE）场景下获取更为精准的函数模型，本文提出一种新型算法，命名为基于多策略的基因表达式编程（MS-GEP）。相较于传统GEP，所提算法能够摆脱早熟收敛问题，跳出局部最优解。首先，本文依据进化代数、种群多样性以及个体适应度值设计了自适应变异率；随后，提出人工干预策略，基于种群进化停滞的代数判断算法是否陷入局部最优困境；最后，通过随机替换个体改变种群平均质量，并追溯祖先种群以调整进化方向。在函数挖掘基准测试集上的实验结果表明，所提MS-GEP相较于其他GEP类算法拥有更优的解质量与更高的种群多样性。此外，在高速铁路接触网监测系统的CP-EE函数模型构建任务中，所提MS-GEP的预测精度亦优于该领域其他常用的函数挖掘算法。