基于通用多核处理器平台的并行基因表达式编程算法

基因表达式编程(Gene Expression Programming, GEP)是一种计算量大且通用性强的新型进化算法,其传统计算形式不能充分利用目前主流的多核处理器。为提高算法效率,提出了基于通用多核处理器平台的并行基因表达式编程算法(Parallel Gene Expression Programming Based on General Multi-core Processor, PGEP-MP)。主要工作包括:O)分析通用多核处理器平台下并行基因表达式编程算法的机理;(2)利用MPI和()pcnMP混合编程模型设计基于通用多核处理器平台的基因表达式编程算法的粗粒度与细粒度相结合的并行模型;(3)提出改进PEEP-MP算法效率的进化策略;(4)通过对函数挖掘和分类的实验证明,PEEP-Ml〕算法提高了函数挖掘和分类的效率,在并行双核处理器数为4的情况下,PEEP-MP的平均并行加速比分别是传统GEP算法的4. 22倍和 4. 06倍。     

基于差分进化基因表达式编程的全局函数优化

为了提高基因表达式编程(Gene Expression Programming,GEP)在函数优化时的效率,将差分进化(Differ-ential Evolution,DE)引入到GEP中,提出了基于差分进化的基因表达式编程的全局优化算法DEGEPO.主要工作包括:(1)针对全局函数优化问题,根据GEP和DE的特点设计了新的基因编码;(2)设计了新的变异和交叉算子;(3)提出了DEGEPO算法并进行了算法分析;(4)实验验证了算法的有效性.相对于传统GEP,DEGEPO,优化结果精度平均提高了2～4个数量级.     

M-GEP:基于多层染色体基因表达式编程的遗传进化算法

该文提出了一种新的基于多层染色体基因表达式编程的遗传进化算法M—GEP,新算法引入了多层染色体的概念,利用染色体构建的层次调用模型对个体进行表达,在解决实际函数发现、电路进化等实际问题中取得了良好效果．该文主要贡献包括：（1）提出了基于多染色体的基因表达式编程算法（M-GEP）;（2）建立了不同染色体的层次调用模型及存储结构;（3）提出并实现了基于染色体的重组算子和基因随机重组算子．对多基因GEP和单基因GEP的对比实验结果表明,平均进化辈数仅为后者的29％～81％．     

基于GEP与小生境的关联规则挖掘的研究*

如何高效而正确地挖掘关联规则是数据挖掘领域的一个重要研究方向。在现有工作的基础上结合基因表达式编程进化算法提出了一种新的挖掘强关联规则的算法框架;提出并实现了基于小生境技术的基因表达式编程算法(niche-gene expression programming,NGEP)以用于挖掘关联规则。与同类算法的对比实验结果表明,NGEP不但能更快地收敛,还进一步提高了挖掘的正确率。     

基于转基因GEP 的公式发现

在传统基因表达式编程(GEP)挖掘知识的过程中，用户只能被动等待程序连续进化若干代之后给出的结果，因此难以有效干预进化过程、质量和速度。为解决这一问题，把生物工程转基因思想引入到基于GEP的函数挖掘中，获得了一系列成果。综述了基于转基因技术的GEP研究进展，包括基因注入，转基因过程和进化干预等，通过自然选择与人工选择的融合，在一定程度上引导进化向着人们预期的方向进行。     

基于基因表达式编程的邮电业务量预测

基因表达式编程(GEP)是将进化的遗传操作和个体的适应度评价相分离的进化模型,具有比遗传编程快2-4个数量级能力.邮电业务量是反映经济发展的重要指标之一,其预测技术得到了广泛的研究,主要工作包括:阐述了GEP基本原理,以及GEP进行时间序列分析的基本方法;运用了GEP技术,对邮电业务总量进行建模研究,并进行了预测检验和分析.实验结果表明,基于GEP得到的邮电业务总量模型有较好的泛化能力,在测试数据上平均相对误差为4.44%.     

DSCE-GEP算法在PM2.5浓度预测中的应用

雾霾防治是目前空气质量保护问题研究的热点,PM2.5浓度预测是雾霾防治的关键之一;文章采用一种双系统协同进化的基因表达式编程算法(DSCE-GEP)进行PM2.5浓度预测,该算法在GEP算法中引入人工干预操作来提高算法进化速度以及解的质量;DSCE-GEP算法是对人类进化的模拟,不仅具有强大的模型学习能力,而且能得到模型的显式函数表达式;文中以西安地区逐日PM2.5浓度预测为例,将DSCE-GEP算法与传统基因表达式编程算法(GEP)、文献中分类回归树和极限学习机组合模型(CART-EELM)以及卷积神经网络和长短期记忆神经网络组合模型(CNN-LSTM)进行了对比实验;实验结果表明,DSCE-GEP算法拟合度更高,是一种具有竞争力的智能预测算法.     

基因表达式编程算法及其应用综述

基因表达式编程GEP(Gene Expression Programming)是一种基于生物基因结构和功能发明的一种新型自适应演化算法.阐述算法的基本原理、技术特点,并对算法的性能进行了分析;同时介绍了算法的主要应用领域及国内外研究现状;最后探讨了GEP的研究方向.     

基于条件云的基因表达式编程算法

标准基因表达式编程算法(GEP)在挖掘知识时采用恒定的变异和交叉率,没有考虑进化中个体适应度的变化,依然存在难以摆脱局部最优和收敛速度问题.为了解决这一问题,提出了将X条件云模型应用到基因表达式编程的算法(XCC-GEP).该算法在进化前期采用固定变异率和交叉率;一旦处于收敛状态时,根据个体的当前适应度,借助X条件云,动态调整其变异率和交叉率,以跳出早熟收敛.实验表明了算法的有效性.     

DC-GEP:基因表达式编程早熟预警——多样性贡献策略

基因表达式编程(gene expression programming,GEP)是函数发现的有力工具,但传统的GEP存在早熟缺陷,为了解决这一问题,已有的研究方法均是在早熟发生后采取补救的措施,导致效率的降低。采用了预警思路来防止GEP早熟现象的发生。主要工作如下:(1)通过对种群个体多样性的评价,提出基于多样性贡献的最优个体选择机制,设计了基于融合适应度和多样性贡献的GEP进化算法diversity contribution-GEP(DC-GEP);(2)提出了一种进化辈数自动确定的方法,实现了自适应的DC-GEP进化算法;(3)实验表明,DC-GEP对于复杂函数挖掘的算法性能比标准GEP在进化效率上平均提高了70%。     

基于开放读码框架的GEP遗传算子

基因表达式编程（GEP）采用的已有单点重组、两点重组、插串等遗传操作有很大概率发生在基因的非编码区,导致搜索过程中遗传操作前后的基因解码成相同的表达式树,这在一定程度上影响了GEP的搜索性能。为解决这一问题,提出了一类基于开放读码框架的遗传算子,这类算子从基因的编码区中选取作用点,以保证遗传操作将改变编码区中的基因片段,从而使遗传操作后的基因能解码成不同的表达式树。实验结果表明,与已有的同类遗传算子相比,提出的遗传算子缩短了GEP算法进化代数,提高了算法的成功率。     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.     

Efficient graph-based genetic programming representation with multiple outputs

In this work, we explore and study the implication of having more than one output on a genetic programming （GP） graph-representation. This approach, called multiple interactive outputs in a single tree （MIOST）, is based on two ideas. First, we defined an approach, called interactivity within an individual （IWI）, which is based on a graph-GP representation. Second, we add to the individuals created with the IWI approach multiple outputs in their structures and as a result of this, we have MIOST. As a first step, we analyze the effects of IWI by using only mutations and analyze its implications （i.e., presence of neutrality）. Then, we continue testing the effectiveness of IWI by allowing mutations and the standard GP crossover in the evolutionary process. Finally, we tested the effectiveness of MIOST by using mutations and crossover and conducted extensive empirical results on different evolvable problems of different complexity taken from the literature. The results reported in this paper indicate that the proposed approach has a better overall performance in terms of consistency reaching feasible solutions.     

Determining Flow Friction Factor in Irrigation Pipes Using Data Mining and Artificial Intelligence Approaches

The implicit Colebrook–White equation has been widely used to estimate the friction factor for turbulent fluid in irrigation pipes. A fast, accurate, and robust resolution of the Colebrook–White equation is, in particular, necessary for scientific intensive computations. In this study, the performance of some artificial intelligence approaches, including gene expression programming (GEP), which is a variant of genetic programming (GP); adaptive neurofuzzy inference system (ANFIS); and artificial neural network (ANN) has been compared to the M5 model tree, which is a data mining technique and, to most available approximations, is based on root mean squared error (RMSE), mean absolute error (MAE) and correlation coefficient (R). Results show that Serghides and Buzzelli approximations with RMSE (0.00002), MAE (0.00001), and R (0.99999) values had the best performances. Among the data mining and artificial intelligence approaches, the GEP with RMSE (0.00032), MAE (0.00026), and R (0.99953) values performed better. However, all 20 explicit approximations except Wood, Churchill (full range of turbulence including laminar regime) and Rau and Kumar estimated the friction factor more accurately than the GEP.     

Development of GEP-based functional relationship for sediment transport in tropical rivers

This study presents gene expression programming (GEP), which is an extension to genetic programming (GP), as an alternative approach to modeling the functional relationships for the River Kurau, River Langat, and River Muda of the Malaysia. A functional relation has been developed using GEP with non-dimensional variables. The development of a GEP non-dimensional model is described. This paper compares current prediction equation with the existing GEP model for the same rivers (Zakaria et al. in Sci Total Environ 408:5078–5085, (2010). The presented model in this study is a less input GEP model and that predicts good performance. The proposed GEP approach gives satisfactory results compared to existing predictors.     

基于GEP的最小二乘支持向量机模型参数选择

针对最小二乘支持向量机的多参数寻优问题,提出了一种基于基因表达式编程的最小二乘支持向量机参数优选方法.该算法将最小二乘支持向量机参数(C,σ)样本作为GEP的基因,按其变异算子随着进化代数和染色体所含基因数目动态变化的机制执行,其收敛速度和精确度大大提高.并与基于粒子群算法和遗传算法参数优选方法比较,通过标准测试函数验证了该算法的拟合误差最低.最后用其建立氧化铝生产蒸发过程参数预测模型,应用工业生产数据进行验证,实验结果表明该方法有效且获得了满意的效果.     

Genetic-based modeling of uplift capacity of suction caissons

In this study, classical tree-based genetic programming (TGP) and its recent variants, namely linear genetic programming (LGP) and gene expression programming (GEP) are utilized to develop new prediction equations for the uplift capacity of suction caissons. The uplift capacity is formulated in terms of several inflecting variables. An experimental database obtained from the literature is employed to develop the models. Further, a conventional statistical analysis is performed to benchmark the proposed models. Sensitivity and parametric analyses are conducted to verify the results. TGP, LGP and GEP are found to be effective methods for evaluating the horizontal, vertical and inclined uplift capacity of suction caissons. The TGP, LGP and GEP models reach a prediction performance better than or comparable with the models found in the literature.     

一种基于GEP的分类规则挖掘算法

基于一种新的自动程序设计方法基因表达式程序设计（GEP），通过设计适应函数、初始化群体的优化、增加新的遗传算子以及采用演化策略中的（λ+μ）淘汰策略等对原始GEP算法进行有效的改进，设计出一种新的数据挖掘算法。采用UCI机器学习知识库中的数据集对该算法进行了实验，并通过与C4.5及文献[3]的比较，检验了该算法的准确性。     

四维飞行轨迹预测方法的研究

通过对空中交通运输管理中目前常用的轨迹预测算法的研究比较和分析,提出了利用遗传算法的从历史数据中进行函数挖掘的思想.针对四维轨迹数据特征的分析和传统的单一函数挖掘的局限性,提出了基于基因表达式编程的频繁函数集挖掘的建模方法.该模型方法通过对历史飞行数据进行遗传算法的操作挖掘出数据集中对应的函数关系集合,用较好的函数模型预测未来航迹.以某一航班雷达数据为训练集做实验,结果表明了应用该方法的准确性和可用性.