基于基因表达式编程的抗噪声数据的函数挖掘方法

An Anti-Noise Method for Function Mining Based on GEP

用传统基因表达式编程 (GEP)适应度机制挖掘函数关系容易受到噪声干扰 ,导致结果失真 为此做了如下探索 :①借鉴生物具有的“趋利避害”天性 ,提出了GEP的“弱适应模型” ,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RE FA ;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方法的成功率提高了 3倍 ,产生结果的平均相对误差从 7 899%降低到 2 32 0 %

Mining functions from experimental data based on traditional gene expression programming (GEP) fitness mechanism falls short in handling noises, which may lead to anamorphic results 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; and (4)By using 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 relative error from 7 899% to 2 320% compared with the traditional approach