检索结果-内蒙古大学图书馆

Prediction of cementation factor for low-permeability Iranian carbonate reservoirs using particle swarm optimization-artificial neural network model and genetic programming algorithm

引用

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 197卷 108102-108102页

作者： Mahmoodpour, Soran Kamari, Ehsan Esfahani, Mohammad Reza Mehr, Amir Karimi Petr Univ Technol Tehran Iran Res Inst Petr Ind RIPI Dept Petr Engn Tehran Iran

cementation factor is a crucial parameter that has a significant influence on the estimation of reservoir parameters. Laboratory measurements for cementation factor are available for occasional cases because experimental special core analyses for determination of cementation factor values are expensive and time-consuming. While this factor plays a significant role in determining water saturation, there is no comprehensive and precise relationship for the case of Iranian carbonate reservoirs. In this article, a unique model was used based on a powerful combination of artificial neural network (ANN) and particle swarm optimization (PSO) algorithm to model the cementation factor. In the second phase of simulation, a correlation for the cementation factor was discovered by genetic programming (GP) algorithm. Both the PSO-ANN model and GP algorithm are trained by input variables such as porosity, permeability, and grain density derived from 175 routine core analysis (RCAL) samples of 21 carbonated oil fields. To determine the relative impact of the independent variables on cementation factor the sensitivity analysis was carried out for both models. The comparison between the PSO-ANN model output and the experimental cementation factor data clearly demonstrated that the built model can predict the cementation factor with great precision;the mean square error between the model predictions and the experimental data was less than 0.07. The root mean square error of training and testing data sets for the new developed correlation using GP algorithm were 0.0902 and 0.0727 respectively. Finally, to evaluate the validity and reliability of the developed models, a comparison was implemented between these two models and other empirical models over an external employment data set (21 data point). This comparison revealed that the GP algorithm and PSO-ANN model deliver a higher performance capacity compared to other proposed correlations for predicting cementation exponent.

关键词： The cementation factor of carbonate reservoirs RCAL data Hybrid PSO-ANN model genetic programming algorithm

来源：评论

学校读者我要写书评

暂无评论

Floating Data Window Movement Influence to genetic programming algorithm Efficiency 1

引用

3rd Conference on Computational Methods in Systems and Software (CoMeSySo)

作者： Brandejsky, Tomas Univ Pardubice Studentska 95 Pardubice 53210 Czech Republic

ISBN: (数字)9783030313623

ISBN: (纸本)9783030313623;9783030313616

Presented paper deals with problem of large data series modeling by genetic programming algorithm. The need of repeated evaluation constraints size of training data set in standard genetic programming algorithms (GPAs) because it causes unacceptable number of fitness function evaluations. Thus, the paper discusses possibility of floating data window use and brings results of tests on large training data vector containing 1 million rows. Used floating window is small and for each cycle of GPA it changes its position. This movement allows to incorporate information contained in large number of samples without the need to evaluate all data points contained in training data in each GPA cycle. Behaviors of this evaluation concept are demonstrated on symbolic regression of Lorenz attractor system equations from precomputed training data set calculated from original difference equations. As expected, presented results points that the algorithm is more efficient than evaluating of whole data set in each cycle of GPA.

关键词： genetic programming algorithm Floating data window Fitness function evaluation scheme Efficiency Floating data window movement

来源：评论

学校读者我要写书评

暂无评论

Predicting submerged vegetation drag with a machine learning-based method

引用

Journal of Hydrodynamics 2024年第3期36卷 534-545页

作者： Meng-yang Liu Hong-wu Tang Sai-yu Yuan Jing Yan College of Water Conservancy and Hydropower Engineering Hohai UniversityNanjing210098China Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources Hohai UniversityNanjing210098China

Accurate estimation of the drag forces generated by vegetation stems is crucial for the comprehensive assessment of the impact of aquatic vegetation on hydrodynamic processes in aquatic *** coupling relationship between vegetation layer flow velocity and vegetation drag makes precise prediction of submerged vegetation drag forces particularly *** present study utilized published data on submerged vegetation drag force measurements and employed a genetic programming(GP)algorithm,a machine learning technique,to establish the connection between submerged vegetation drag forces and flow and vegetation *** using the bulk velocity,U,as the reference velocity scale to define the drag coefficient,C_(d),and stem Reynolds number,the GP runs revealed that the drag coefficient of submerged vegetation is related to submergence ratio(H^(*)),aspect ratio(d^(*)),blockage ratio(ψ^(*)),and vegetation density(λ).The relation between vegetation stem drag forces and flow velocity is implicitly embedded in the definition of C_(d).Comparisons with experimental drag force measurements indicate that using the bulk velocity as the reference velocity,as opposed to using the vegetation layer average velocity,U_(v),eliminates the need for complex iterative processes to estimate U_(v)and avoids introducing additional errors associated with U_(v)*** approach significantly enhances the model’s predictive capabilities and results in a simpler and more user-friendly formula expression.

关键词： Drag coefficient genetic programming algorithm submerged vegetation flow resistance cylinder array

来源：评论

学校读者我要写书评

暂无评论

Optimization of a New Integrated Separation Process for Azeotropes Based on genetic programming

引用

CHEMICAL ENGINEERING & TECHNOLOGY 2021年第12期44卷 2355-2364页

作者： Wang, Xiao-Hong Ding, Xin Du, Peng Tian, Zeng-Hu Chen, Jing-Xuan Qingdao Univ Sci & Technol Coll Chem Engn 53 Zhengzhou Rd Qingdao 266042 Peoples R China

A solution strategy of a distillation-membrane separation process based on genetic programming algorithm (GP) is proposed. It can automatically match diverse membrane materials according to different azeotropic systems and generate various integrated processes. For the membrane, which is still in the experimental research stage, a theoretical prediction method of membrane cost is recommended. Taking the benzene-cyclohexane system as an example, a GO-AgNPs/PI membrane and polyurethane membrane were matched, respectively, and the optimal integrated processes can be obtained. The GP strategy provides a strong guidance for the comprehensive design and optimization of distillation-membrane separation by using various new membranes.

关键词： Azeotrope separation Distillation-membrane separation genetic programming algorithm

来源：评论

学校读者我要写书评

暂无评论

Versatile Function GPA 17

Versatile Function GPA

引用

17th IEEE International Scientific Conference on Informatics, INFORMATICS 2024

作者： Brandejsky, Tomas Merta, Jan University of Pardubice Faculty of Electrical Engineering and Cybernetics Pardubice Czech Republic

ISBN: (纸本)9798350387674

This herein presented paper is dedicated to the introduction of the continuous versatile function genetic programming algorithm (GPA) developed with respect to BigData processing. The basic structure of this hierarchical evolutionary algorithm and examples of versatile functions are *** the basis of experiments with the hybrid evolutionary algorithm (hybrid EA) providing symbolic regression of precomputed Lorenz attractor system data representing hybrid EA's behaviour, a discussion of examples of an obtained solution is presented. The versatile function concept GPA is applicable, but it requires the hybrid evolutionary algorithm application, as is demonstrated in the paper. © 2024 IEEE.

关键词： BigData genetic programming algorithm hybrid evolutionary algorithm symbolic regression versatile function GPA

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems

引用

algorithmS 2022年第10期15卷 351-351页

作者： Karaseva, Tatiana Semenkin, Eugene Reshetnev Siberian State Univ Sci & Technol Dept Syst Anal & Operat Res Krasnoyarsk 660037 Russia Siberian Fed Univ Dept Business Informat & Business Proc Modeling Krasnoyarsk 660041 Russia

Evolutionary approaches are widely applied in solving various types of problems. The paper considers the application of EvolODE and EvolODES approaches to the identification of dynamic systems. EvolODE helps to obtain a model in the form of an ordinary differential equation without restrictions on the type of the equation. EvolODES searches for a model in the form of an ordinary differential equation system. The algorithmic basis of these approaches is a modified genetic programming algorithm for finding the structure of ordinary differential equations and differential evolution to optimize the values of numerical constants used in the equation. Testing for these approaches on problems in the form of ordinary differential equations and their systems was conducted. The influence of noise present in the data and the sample size on the model error was considered for each of the approaches. The symbolic accuracy of the resulting equations was studied. The proposed approaches make it possible to obtain models in symbolic form. They will provide opportunities for further interpretation and application.

关键词： identification of dynamic systems evolutionary algorithms genetic programming algorithm ordinary differential equation ordinary differential equation system

来源：评论

学校读者我要写书评

暂无评论

Identifying Spam Patterns in SMS using genetic programming Approach

Identifying Spam Patterns in SMS using Genetic Programming A...

引用

International Conference on Intelligent Computing and Control Systems (ICCS)

作者： Sharma, Dimple Sharaff, Aakanksha Natl Inst Technol Raipur Dept Comp Sci & Engn Raipur Madhya Pradesh India

ISBN: (纸本)9781538681138

SMS spam, also known as mobile spam, has become a prevalent and an ever growing issue due to the availability of bulk SMS services at nominal costs. These spam messages may not only be commercial but also pose a great deal of financial threats to the users. To fight against SMS spam, a variety of solutions have been proposed including content-based filtering, semantic indexing, machine learning classifiers, etc. However, in this regard evolutionary algorithms have not been utilized. Since the nature of SMS is contemporary, the representation of text messages keep evolving with the help of slangs, symbols, misspelled words, abbreviations and acronyms. Hence, such a solution is required which can accommodate these changes, also keeping the length of SMS in consideration. The model proposed in this paper generates regular expressions as individuals of population, using genetic programming Approach. These regular expressions so generated are used for the classification purpose. The application of genetic programming in the domain of SMS spam filtering has not been explored widely. It is able to eliminate False Positive errors, thus saving legitimate messages from being misclassified. The performance tends to improve with higher number of generations. Performance and confusion matrix for different number of generations are tabulated.

关键词： SMS spam filtering regular expressions evolutionary algorithm genetic programming algorithm spam classification

来源：评论

学校读者我要写书评

暂无评论

Estimation of mass matrix in machine tool's weak components research by using symbolic regression

引用

COMPUTERS & INDUSTRIAL ENGINEERING 2019年 127卷 998-1011页

作者： Liu, Hongqi Lin, Hai Jiang, Xuchu Mao, Xinyong Liu, Quanxin Li, Bin Huazhong Univ Sci & Technol Sch Mech Sci & Engn Natl NC Syst Engn Res Ctr Room B416East Bldg1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Natl Engn Res Ctr Mfg Equipment Digitizat Room C608East Bldg1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Modal mass is the important dynamic parameter in weak component research of machine tool structure and also for its control design and load design. Modal mass matrix is defined as the multiplication of mass matrix of a machine tool and its corresponding modal shape matrix. Currently, the big problem is that the mass matrix is hard to get for the calculation of modal mass matrix. Traditional method such as the finite element method cannot acquire the mass matrix well because the overall mass matrix of complex systems cannot be given by experience and the mass matrix in finite element analysis is so large that the computer hard disk will be blasted. In addition to finite element method, the UMM method is used commonly but the noise contained in the mode of the data processing is mixed into the mass matrix, resulting in the inaccurate result and even failure in severe cases. So, there is an urgent need for a method of directly obtaining the mass matrix based on a general equation of multi-degree-of-freedom vibration system from a data source, and then used to calculate the modal mass. In this paper, genetic programming algorithm (GP) in symbolic regression as an evolution computation method is used to search out the equation expression structure and its coefficients among a group of variances including displacement, velocity, acceleration and external excitation force. And the mass matrix is contained in the equations' coefficients. In addition, its performance is compared with LRA method and PSO method.

关键词： Weak component research Modal mass matrix Mass matrix genetic programming algorithm Symbolic regression algorithm

来源：评论

学校读者我要写书评

暂无评论

GPA-ES algorithm Modification for Large Data 1

引用

3rd Conference on Computational Methods in Systems and Software (CoMeSySo)

作者： Brandejsky, Tomas Univ Pardubice Studentska 95 Pardubice 53210 Czech Republic

ISBN: (数字)9783030303297

ISBN: (纸本)9783030303297;9783030303280

This paper discusses improvement of genetic programming algorithm to large data sets with respect to future extension to big data applications. On the beginning it summarizes requirements on evolutionary system to be applicable in the area of big data and ways of their satisfaction. Then GPAs and especially their improvements by solution constant optimization (so called hierarchical and hybrid genetic programming algorithms) are discussed in this paper. After a discussion of few experiment results of introduced novel evaluation scheme approach with floating data window is presented. Novel evaluation scheme applies floating data window to fitness function evaluation. After one evaluation step of GPA including tuning of parameters (solution constants) by embedded Evolutionary Strategy algorithm data window moves to new position. Presented results demonstrate that this strategy can be faster and more efficient than evolution of whole training data set in each evolutionary step of GPA algorithm. This modification can be starting point of future applications of GPA in the field of large and big data analytic.

关键词： Big data Fixed position data window Floating data window genetic programming algorithm Symbolic regression Data analytic

来源：评论

学校读者我要写书评

暂无评论

Influence of two different crossover operators use onto GPA efficiency

Influence of two different crossover operators use onto GPA ...

引用

International Conference on Control, Artificial Intelligence, Robotics and Optimization (ICCAIRO)

作者： Brandejsky, Tomas Univ Pardubice Dept Electrotech Ans Informat Pardubice Czech Republic

ISBN: (纸本)9781538695760

Increasing capabilities of today computers, especially size of memory and computational power open new application areas to genetic programming algorithms [1]. Unfortunately, efficiency of these algorithms is not big and decreases with solved problem complexity. Thus, its increase is extremely important for opening of new application domains. There exists three main areas that should potentially influence GPA efficiency. They are algorithms, pseudo-random number generator behaviours and evolutionary operators. genetic programming algorithms use two basic evolutionary operators mutation and crossover in the sense of Darwinian evolution. Non-looking to the fact, that it is possible to define additional operators like e.g. application defined operators [2], there are many different implementations of both basic evolutionary operators [3] and each of them is sometimes useful in artificial evolutionary process. Thus, the main question solved in this paper is that it might bring some advance to use two randomly executed different crossover operators in GPA. The study is focused to symbolic regression problem and as GPA is used GPA-ES, because it is capable to eliminate influence of solution parameters (constants) identification and thus to produce more clear results. (Abstract)

关键词： genetic programming algorithm evolutionary operators crossover mutation multiple crossover implementations

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：