一个改进的基因表达编程算法用于函数挖掘的地图-减少在连锁监控系统中的工作执行
Jin Ding1, Tianyu Jiang1, Ping Tan1
1School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou, China.
PloS one
|November 16, 2023
概括
一个新的多策略基因表达编程 (MS-GEP) 算法提高了高速铁路系统的函数挖掘精度. 这种新的方法通过避免局部最佳情况和增加人口多样性来提高地图减少工作效率.
科学领域:
- 计算智能是一种计算智能.
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 基因表达编程 (GEP) 是功能挖掘中的一个关键算法.
- 准确的功能模型对于优化地图缩小工作中的配置参数执行效率 (CP-EE) 至关重要,特别是在高速铁路连锁监控系统中.
研究的目的:
- 开发一种新的算法,多策略基因表达编程 (MS-GEP),用于CP-EE中更准确的功能建模.
- 增强GEP逃脱过早收和局部优化的能力.
主要方法:
- 该MS-GEP算法结合了基于进化世代,人口多样性和健康价值的适应性突变率.
- 手动干预策略识别并解决基于进化停滞的局部最佳情况.
- 人口多样性是通过随机的个体替代和祖先人口追踪来改变进化方向来管理的.
主要成果:
- 与传统的GEP算法相比,MS-GEP在基准函数挖掘任务上展示了优越的解决方案质量和增加的人口多样性.
- 该算法在建模高速铁路连锁监控系统的CP-EE方面取得了更高的准确性.
结论:
- 在函数挖掘任务中,MS-GEP比标准GEP提供了显著的改进.
- 提出的策略有效地防止了局部优化,并提高了整体模型的准确性,显示了对复杂系统优化的承诺.
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