Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

A worldwide SPT-based soil liquefaction triggering analysis utilizing gene expression programming and Bayesian probabilistic method

In this context,two different approaches of soil liquefaction evaluation using a soft computing technique based on the worldwide standard penetration test(SPT) databases have been studied.Gene expression programming(GEP) as a gray-box modeling approach is used to develop different deterministic models in order to evaluate the occurrence of soil liquefaction in terms of liquefaction field performance indicator(LI) and factor of safety(FS) in logistic regression and classification concepts.The comparative plots illustrate that the classification concept-based models show a better performance than those based on logistic regression.In the probabilistic approach,a calibrated mapping function is developed in the context of Bayes’ theorem in order to capture the failure probabilities(PL) in the absence of the knowledge of parameter uncertainty.Consistent results obtained from the proposed probabilistic models,compared to the most well-known models,indicate the robustness of the methodology used in this study.The probability models provide a simple,but also efficient decision-making tool in engineering design to quantitatively assess the liquefaction triggering thresholds.

Scheduling Rules Based on Gene Expression Programming for Resource-Constrained Project Scheduling Problem

In order to minimize the project duration of resourceconstrained project scheduling problem( RCPSP), a gene expression programming-based scheduling rule( GEP-SR) method is proposed to automatically discover and select the effective scheduling rules( SRs) which are constructed using the project status and attributes of the activities. SRs are represented by the chromosomes of GEP, and an improved parallel schedule generation scheme( IPSGS) is used to transform the SRs into explicit schedules. The framework of GEP-SR for RCPSP is designed,and the effectiveness of the GEP-SR approach is demonstrated by comparing with other methods on the same instances.

A true triaxial strength criterion for rocks by gene expression programming

Rock strength is a crucial factor to consider when designing and constructing underground projects.This study utilizes a gene expression programming(GEP)algorithm-based model to predict the true triaxial strength of rocks,taking into account the influence of rock genesis on their mechanical behavior during the model building process.A true triaxial strength criterion based on the GEP model for igneous,metamorphic and magmatic rocks was obtained by training the model using collected data.Compared to the modified Weibols-Cook criterion,the modified Mohr-Coulomb criterion,and the modified Lade criterion,the strength criterion based on the GEP model exhibits superior prediction accuracy performance.The strength criterion based on the GEP model has better performance in R2,RMSE and MAPE for the data set used in this study.Furthermore,the strength criterion based on the GEP model shows greater stability in predicting the true triaxial strength of rocks across different types.Compared to the existing strength criterion based on the genetic programming(GP)model,the proposed criterion based on GEP model achieves more accurate predictions of the variation of true triaxial strength(s1)with intermediate principal stress(s2).Finally,based on the Sobol sensitivity analysis technique,the effects of the parameters of the three obtained strength criteria on the true triaxial strength of the rock are analysed.In general,the proposed strength criterion exhibits superior performance in terms of both accuracy and stability of prediction results.

Modeling viscosity of methane,nitrogen,and hydrocarbon gas mixtures at ultra-high pressures and temperatures using group method of data handling and gene expression programming techniques

Accurate gas viscosity determination is an important issue in the oil and gas industries.Experimental approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas mixtures.It is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 K.Also,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the literature.Average absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,respectively.In addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT conditions.Also,using leverage technique,valid,suspected and outlier data points were determined.Finally,trends of gas viscosity models at different conditions were evaluated.

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors.

Security Risk Assessment of Cyber Physical Power System Based on Rough Set and Gene Expression Programming

Risk assessment is essential for the safe and reliable operation of cyber physical power system. Traditional security risk assessment methods do not take integration of cyber system and physical system of power grid into account. In order to solve this problem, security risk assessment algorithm of cyber physical power system based on rough set and gene expression programming is proposed. Firstly, fast attribution reduction based on binary search algorithm is presented. Secondly, security risk assessment function for cyber physical power system is mined based on gene expression programming. Lastly, security risk levels of cyber physical power system are predicted and analyzed by the above function model. Experimental results show that security risk assessment function model based on the proposed algorithm has high efficiency of function mining, accuracy of security risk level prediction and strong practicality. © 2014 Chinese Association of Automation.

Hybrid Gene Expression Programming-Based Sensor Data Correlation Mining

This paper deals with the reflectance estimation model issue to improve the estimation accuracy. We propose a model containing two core procedures: dimensionality reduction and model mining. First, the dimensionality reduction algorithm of hyperspectral data based on dependence degree(DRNDDD) is proposed to reduce the redundant hyperspectral band. DRND-DD solves the selection of suitable hyperspectral band via rough set theory. Furthermore, to improve the computation speed and accuracy of the model, based on DRND-DD, this paper proposes reflectance estimation model mining of leaf nitrogen concentration(LNC) for hyperspectral data by using hybrid gene expression programming(REMLNC-HGEP). Experimental results on three datasets demonstrate that the DRND-DD algorithm can obtain good results with a very short running time compared with principal component analysis(PCA), singular value decomposition(SVD), a dimensionality reduction algorithm based on the positive region(AR-PR) and a dimensionality reduction algorithm based on a discernable matrix(ARDM), and REMLNC-HGEP has low average time-consumption, high model mining success ratio and estimation accuracy. It was concluded that the REMLNC-HGEP performs better than the regression methods.

Rules Mining-Based Gene Expression Programming for the Multi-Skill Resource Constrained Project Scheduling Problem

Themulti-skill resource-constrained project scheduling problem(MS-RCPSP)is a significantmanagement science problem that extends from the resource-constrained project scheduling problem(RCPSP)and is integrated with a real project and production environment.To solve MS-RCPSP,it is an efficient method to use dispatching rules combined with a parallel scheduling mechanism to generate a scheduling scheme.This paper proposes an improved gene expression programming(IGEP)approach to explore newly dispatching rules that can broadly solve MS-RCPSP.A new backward traversal decoding mechanism,and several neighborhood operators are applied in IGEP.The backward traversal decoding mechanism dramatically reduces the space complexity in the decoding process,and improves the algorithm’s performance.Several neighborhood operators improve the exploration of the potential search space.The experiment takes the intelligent multi-objective project scheduling environment(iMOPSE)benchmark dataset as the training set and testing set of IGEP.Ten newly dispatching rules are discovered and extracted by IGEP,and eight out of ten are superior to other typical dispatching rules.

An expert system for predicting shear stress distribution in circular open channels using gene expression programming

The shear stress distribution in circular channels was modeled in this study using gene expression programming(GEP). 173 sets of reliable data were collected under four flow conditions for use in the training and testing stages. The effect of input variables on GEP modeling was studied and 15 different GEP models with individual, binary, ternary, and quaternary input combinations were investigated. The sensitivity analysis results demonstrate that dimensionless parameter y/P, where y is the transverse coordinate, and P is the wetted perimeter, is the most influential parameter with regard to the shear stress distribution in circular channels. GEP model 10, with the parameter y/P and Reynolds number(Re) as inputs, outperformed the other GEP models, with a coefficient of determination of 0.7814 for the testing data set. An equation was derived from the best GEP model and its results were compared with an artificial neural network(ANN) model and an equation based on the Shannon entropy proposed by other researchers. The GEP model, with an average RMSE of 0.0301, exhibits superior performance over the Shannon entropy-based equation, with an average RMSE of 0.1049, and the ANN model, with an average RMSE of 0.2815 for all flow depths.

Determining the Scour Dimensions Around Submerged Vanes in a 180°Bend with the Gene Expression Programming Technique

Submerged vanes are installed on rivers and channel beds to protect the outer bank bends from scouring.Also,local scouring occurs around the submerged vanes over time,and identifying the effective factors on the scouring phenomena around these submerged vanes is one of the important issues in river engineering.The most important aimof this study is investigation of scour pattern around submerged vanes located in 180°bend experimentally and numerically.Firstly,the effects of various parameters such as the Froude number(Fr),angle of submerged vanes to the flow(α),angle of submerged vane location in the bend(θ),distance between submerged vanes(d),height(H),and length(L)of the vanes on the dimensionless volume of the scour hole were experimentally studied.The submerged vanes were installed on a 180°bend whose central radius and channel width were 2.8 and 0.6 m,respectively.By reducing the Froude number,the scour hole volume decreased.For all Froude numbers,the biggest scour hole formed atθ=15°.In all models,by increasing the Froude number,the scour hole volume significantly increases.In addition,by increasing the submerged vanes’length and height,the scour hole dimensions also grow.Secondly,using gene expression programming(GEP),a relationship for determining the scour hole volume around the submerged vanes was provided.For this model,the determination coefficients(R2)for the training and test modes were computed as 0.91 and 0.9,respectively.In addition,this study performed partial derivative sensitivity analysis(PDSA).According to the results,the PDSA was calculated as positive for all input variables.

Fetal and neonatal programming of postnatal growth and feed efficiency in swine

Maternal undernutrition or overnutrition during pregnancy alters organ structure, impairs prenatal and neonatal growth and development, and reduces feed efficiency for lean tissue gains in pigs. These adverse effects may be carried over to the next generation or beyond. This phenomenon of the transgenerational impacts is known as fetal programming, which is mediated by stable and heritable alterations of gene expression through covalent modifications of DNA and histones without changes in DNA sequences(namely, epigenetics). The mechanisms responsible for the epigenetic regulation of protein expression and functions include chromatin remodeling; DNA methylation(occurring at the 5′-position of cytosine residues within CpG dinucleotides); and histone modifications(acetylation, methylation, phosphorylation, and ubiquitination). Like maternal malnutrition, undernutrition during the neonatal period also reduces growth performance and feed efficiency(weight gain:feed intake; also known as weightgain efficiency) in postweaning pigs by 5–10%, thereby increasing the days necessary to reach the market bodyweight. Supplementing functional amino acids(e.g., arginine and glutamine) and vitamins(e.g., folate) play a key role in activating the mammalian target of rapamycin signaling and regulating the provision of methyl donors for DNA and protein methylation. Therefore, these nutrients are beneficial for the dietary treatment of metabolic disorders in offspring with intrauterine growth restriction or neonatal malnutrition. The mechanism-based strategies hold great promise for the improvement of the efficiency of pork production and the sustainability of the global swine industry.

基于AGEP-DNN的水下聚能装药比冲量预测模型

聚能装药比冲量是表征水下爆炸中冲击波对目标破坏作用的重要参数。为实现水下聚能装药比冲量智能预测,提出一种自适应基因表达式编程(adaptive gene expression programming, AGEP)优化深度神经网络(deep neural network, DNN)的聚能装药比冲量预测模型(AGEP-DNN)。考虑装药结构与比冲量数值之间的复杂非线性关系,通过AUTODYN软件建立有限元模型,对水下爆炸过程进行仿真,采用经验公式验证仿真数据的有效性;基于仿真实验数据,设计AGEP算法优化DNN超参数,构建AGEP-DNN模型,对比冲量进行智能预测。实验结果显示,AGEP-DNN聚能装药比冲量预测模型在9种对比智能预测模型中具有最优的预测精度。

基于高频组合片段-基因表达式编程算法的轨道交通地面沉降预测模型

[目的]地面沉降预测和控制是轨道交通盾构法隧道施工中最为关注的问题之一。为了解决现有地面沉降预测和控制中存在的模型表达过于复杂且缺乏解释性的问题,需要一种既简洁清晰,又能够描述复杂问题的可解释模型,GEP(基因表达式编程)算法提供了这种可能性,因此需对基于HFS(高频组合片段)-GEP算法的轨道交通地面沉降预测模型进行深入研究。[方法]以杭绍城际铁路某区段盾构隧道工程为依托,选取盾构施工过程中的土舱压力、刀盘扭矩、刀盘转速、推进速度、总推力、隧道埋深及盾尾注浆量等参数作为关键输入型施工参数,地面沉降作为输出型施工参数,通过备选公式集筛选以及HFS选取,建立基于HFS-GEP算法的轨道交通地面沉降预测模型。利用该模型对第180环—第210环区段的关键施工参数进行优化调整,分析盾构施工参数变化对地面最终沉降的影响效果。[结果及结论]基于HFS-GEP算法的地面沉降预测模型可以反映盾构施工参数与地面最终沉降的显式关系;相较于传统GEP算法的地面沉降预测模型,该模型准确度更高,结构更为简洁,且收敛速度更快。通过对盾构关键施工参数进行优化调整,该模型可将第180环—第210环区段的最终沉降量控制在10 mm以内。

基于VE-GEP算法的PM_(2.5)浓度预测

准确预测PM_(2.5)浓度对于公众健康和环境保护具有重要意义,但其非线性、多变性以及复杂性的特点导致难以准确预测.基于此,本文针对传统GEP存在的不足,提出了一种基于病毒进化的基因表达式编程算法(VE-GEP)来预测PM_(2.5)浓度.该算法在GEP的基础上引入了复活机制与诱变重启机制.复活机制能去除种群中的劣质个体,改善种群中个体的质量;诱变重启机制通过引入优质基因和新的个体,提高种群的多样性,增强算法的寻优能力.实验结果表明, VE-GEP算法相较于GEP、DSCE-GEP和CNN-LSTM在春季、夏季和秋季中的预测模型均有不同程度的提高,拟合度分别提高1.28%/0.1%/0.13%、1.86%/1.29%/0.42%、0.57%/0.24%/0.29%,为PM_(2.5)浓度预测研究提供了新的思路和方法.

Hypothesizing “Reward” Gene Polymorphisms May Predict High Rates of Injury and Addiction in the Workforce: A Nutrient and Electrotherapeutic Based Solution

We hypothesize that individuals with genetic predisposition to Substance Use Disorder (SUD) may have greater likelihood of experiencing work related accidents. We further hypothesize that high risk populations will carry single or multiple polymorphisms associated with brain reward circuitry and/or brain reward cascade, including: Dopaminergic (i.e. DRD2 receptor genes);Serotonergic (i.e. 5-HTT2 receptor genes);Endorphinergic (i.e. pre-enkephalin genes);Gabergic (i.e. GABAA receptor genes);Neurotransmitter Metabolizing genes (i.e. MAO and COMT genes) among others (GARSRXTM). Analgesic addiction as well as “pseudoaddiction” must be treated to improve pain control and its management. We propose that non-pharmacological alternatives to pain relief, in high risk, addiction-prone individuals, are Electrotherapeutic Device(s) and Programs. We further propose patented KB220Z, a nutraceutical designed to release dopamine at the nucleus accumbens, will reduce craving behavior, in genetically programmed individuals. By utilizing both alternatives in DNA analyzed injured workers, a reduction in analgesic addiction (genuine or pseudo) leads to improved health and quicker return to work. We also hypothesize that this novel approach will impact costs related to injuries in the workforce. Effective management of chronic pain, especially in high addiction-prone workforce populations, is possible in spite of being particularly elusive. A series of factors encumber pain assessment and management, including analgesia addiction, pharmacogenomic response to pain medications, and genetically inherited factors involving gene polymorphisms. Additional research is required to test these stipulated hypotheses related to genetic proneness to addiction, but also proneness to accidents in the workplace and reduction of craving behavior. Our hypothesis that genotyping coupled with both KB220ZTM and the pharmaceutical-free Electrotherapy, will reduce iatrogenic induced analgesia addiction. This approach will achieve attainable effective pain management and quicker return to work. We propose outcomes such as the Reward Deficiency System SolutionTM may become an adjunct in the war against iatrogenic pain medication addiction.

基于基因表达式编程算法的数据驱动型平板闸门流量计算方法

灌区是国家水网建设的重要内容,精准量测和控制过闸流量过程是灌区用水管理的关键。为实时获取高精度的平板闸门过流量,以物理试验为基础,首先采用仿真模型分析了不同工况下闸前闸后流线与流速分布、水深-流量变化关系,其次,以仿真模型的模拟结果作为训练集,采用基因表达式编程算法(Gene Expression Programming,GEP)构建了融合闸前/后水深与开度的闸门过流量算法,并以更多的仿真模型模拟结果作为测试集,对比分析了该算法的性能。结果表明:①fluent数值模拟能在不同闸门开度和流量组合下重现复杂流线和流速分布过程,且模拟的流量过程与实测结果拟合良好,但效率低下,无法满足实际闸控的实时性需求;②GEP算法能够保持仿真模型的精度,且能达到实时计算;③与存在多个率定参数的经典闸孔出流分段公式相比,无任何率定参数的GEP算法的精度更高;④以仿真模型的模拟结果为基准,GEP算法比BP神经网络的精度更高、泛化性更好。因此,GEP算法更适宜平板闸门过流量的计算,可为灌区用水管理提供技术支撑。

PD-1抑制剂联合一线化疗方案对晚期驱动基因阴性肺腺癌的效果及安全性分析

目的探讨程序性细胞死亡蛋白-1(PD-1)抑制剂联合一线化疗方案治疗晚期驱动基因阴性肺腺癌的效果及安全性。方法回顾性收集2019年1月至2021年12月收治的60例晚期驱动基因阴性的肺腺癌患者病历资料,依据治疗方式不同分组,将30例接受培美曲塞、顺铂联合PD-1抑制剂(信迪利单抗)治疗的病历资料纳入观察组另30例接受培美曲塞联合顺铂治疗的病历资料纳入对照组。对比2组患者治疗前、治疗30 d时2组临床疗效[客观缓解率(ORR)、疾病控制率(DCR)]、免疫功能[CD_(4)^(+)、CD_(8)^(+)、CD_(4)^(+)/CD_(8)^(+)]、肿瘤标志物[癌胚抗原(CEA)、细胞角蛋白19片段(CYFRA21-1)]、不良反应[恶心、粒细胞减少、肝功能异常、甲状腺功能异常、肺炎、皮疹、血小板降低]。结果观察组ORR显著高于对照组,差异有统计学意义(P<0.05);2组DCR比较差异无统计学意义(P>0.05);治疗后,观察组CD_(4)^(+)、CD_(4)^(+)/CD_(8)^(+)水平高于治疗前,且高于对照组(P<0.05);治疗后,2组CYFRA21-1、CEA水平均下降,且观察组低于对照组(P<0.05)。观察组甲状腺功能异常、肺炎、皮疹等发生例数显著多于对照组,差异有统计学意义(P<0.05)。结论PD-1抑制剂联合化疗一线治疗可有效改善晚期驱动基因阴性肺腺癌患者免疫功能,并降低肿瘤标志物水平,疗效确切。

Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

基于机器学习探究子宫内膜癌免疫微环境与基因表达的关联及其对预后的预测价值

目的:探究子宫内膜癌(Endometrial Cancer,EC)免疫微环境与基因表达的关联及其对预后的预测价值,通过生物信息学分析与机器学习技术,识别关键的免疫相关基因,构建预后模型,以期为EC的个性化治疗提供新的方向。方法:基于TCGA数据库,采用DESeq2、edgeR和limma工具筛选差异表达基因,结合ImmPort数据库筛选免疫相关基因。利用Lasso回归、单变量特征选择、Boruta和随机森林等机器学习算法进行特征基因筛选。通过单因素和多因素Cox回归分析评估基因的预后价值,并构建风险评分模型。采用CIBERSORT算法分析肿瘤免疫浸润,通过免疫组化验证关键基因表达。结果:通过3种差异分析结果与免疫相关基因的交集,确定62个差异表达的免疫基因,并使用多种机器学习模型筛选得到25个潜在生物标志物,其中被机器学习筛选为预后相关基因。单因素和多因素Cox回归分析证实,INHBE、SLURP1和TNFSF11基因与EC患者的生存期显著相关。构建的风险评分模型能够有效区分不同预后组别的生存率,且与免疫细胞浸润程度相关。免疫组化分析进一步验证这些基因在肿瘤与正常组织间的表达差异。结论:INHBE、SLURP1和TNFSF11是EC免疫微环境中关键的预后生物标志物,其表达水平与免疫细胞浸润及患者生存率密切相关,为EC的精准医疗提供理论基础。