基于基因表达式编程的抗噪声数据的函数挖掘方法
n Anti-noise Method for Function Mining Based on GEP
用基因表达式编程(GEP)技术挖掘函数关系，有助于在实验数据上提炼数学模型、揭示事物本质规律。传统的GEP适应度机制容易受到噪声干扰，导致结果失真，甚至整个GEP算法失败。本文做了如下探索：（1）借鉴生物具有的“趋利避害”天性，提出了GEP的“弱适应模型”(Weak-Adaptive Model)，以实现在含噪声的数据集上挖掘函数关系；（2）提出新概念“带内集”、“带外集”并用于划分训练数据集；（3）设计了在弱适应模型下基于相对误差计算适应度的算法REFA；（4）用详尽的实验验证了REFA的有效性，当测量数据的噪声率为3.33%时，与传统方法相比，REFA方法成功率提高了3倍，产生结果的平均相对误差降低了29.37%。
ining functions from experimental data based on Gene Expression Programming (GEP) technique can help scientists to build mathematic model and discover the essential rules hidden in the objects. Traditional GEP fitness mechanism falls short in handling noises, which may lead anamorphic results or even make the whole GEP fail. The contributions of this paper include: (1) Proposing a new concept called Weak-Adaptive Model (WAM) based on GEP to break the limitation, which is enlightened by the biologic nature known as "seek advantage, avoid disadvantage"; (2) Presenting new concepts “In-Band set” and “Out-Band set” for partitioning the training data set; (3) Designing a new approach called Relative Error Fitness Algorithm (REFA) to mine functions in terms of WAM; (4) By extensive experiments demonstrating the effectiveness of REFA. The results show that when mining functions in a dataset with 3.33% noise data, REFA increases the success-probability by 3 times and decreases the average rel
钟义啸、唐常杰、左劼、段磊、陈宇
生物科学研究方法、生物科学研究技术计算技术、计算机技术
基因表达式编程，噪声数据，适应度，函数挖掘，弱适应模型
GEP Noise Fitness Function Mining Weak-Adaptive Model
钟义啸,唐常杰,左劼,段磊,陈宇.基于基因表达式编程的抗噪声数据的函数挖掘方法[EB/OL].(2004-06-28)[2025-08-02].http://www.paper.edu.cn/releasepaper/content/200406-111.点此复制
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