检索结果-内蒙古大学图书馆

Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2010年第5-8期50卷 729-747页

作者： Nie, Li Shao, Xinyu Gao, Liang Li, Weidong Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China Coventry Univ Fac Engn & Comp Coventry W Midlands England

The paper considers the problems of scheduling n jobs that are released over time on a machine in order to optimize one or more objectives. The problems are dynamic single-machine scheduling problems (DSMSPs) with job release dates and needed to be solved urgently because they exist widely in practical production environment. gene expression programming-based scheduling rules constructor (GEPSRC) was proposed to construct effective scheduling rules (SRs) for DSMSPs with job release dates automatically. In GEPSRC, gene expression programming (GEP) worked as a heuristic search to search the space of SRs. Many experiments were conducted, and comparisons were made between GEPSRC and some previous methods. The results showed that GEPSRC achieved significant improvement.

关键词： Single machine scheduling Dynamic scheduling Release dates Scheduling rules gene expression programming

来源：评论

学校读者我要写书评

暂无评论

An investigation for predicting the effect of green roof utilization on temperature decreasing over the roof surface with gene expression programming

引用

ENERGY AND BUILDINGS 2017年 139卷 254-262页

作者： Ayata, Tahir Erdemir, Dogan Ozkan, Omer Tayfun Erciyes Univ KMY TR-38039 Kayseri Turkey Erciyes Univ Dept Mech Engn TR-38039 Kayseri Turkey

This study presents that the effect of green roof usage on the temperature decreasing for Turkey's cities has been investigated with gene expression programming (GEP). Training data for GEP model has been taken from summer climatic data of nine different cities around the world, which have different climate regions. Thus, wide range of climatic conditions have been considered in GEP modeling. GEP model has been used for predicting the temperature decreasing over a green roof in Turkey cities. GEP model has given sufficient result with 2.2894% RMSE and 94.01% R-2. When the effect of green roof usage in Turkey cities is compared to World cities, it is found that temperature decreasing value for Turkey's cities and World cities has shown same trend. The maximum temperature increasing is 2.91 degrees C in Kars at 08:00 am. and the maximum temperature decreasing is 29.61 degrees C in Izmir at 06:00 p.m. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Green roof gene expression programming Temperature decreasing Comfort condition

来源：评论

学校读者我要写书评

暂无评论

Efficient Evolution of Accurate Classification Rules Using a Combination of gene expression programming and Clonal Selection

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2008年第6期12卷 662-678页

作者： Karakasis, Vasileios K. Stafylopatis, Andreas Natl Tech Univ Athens Sch Elect & Comp Engn Athens 15780 Greece

A hybrid evolutionary technique is proposed for data mining tasks, which combines a principle inspired by the immune system, namely the clonal selection principle, with a more common, though very efficient, evolutionary technique, gene expression programming (GEP). The clonal selection principle regulates the immune response in order to successfully recognize and confront any foreign antigen, and at the same time allows the amelioration of the immune response across successive appearances of the same antigen. On the other hand, gene expression programming is the descendant of genetic algorithms and genetic programming and eliminates their main disadvantages, such as the genotype-phenotype coincidence, though it preserves their advantageous features. In order to perform the data mining task, the proposed algorithm introduces the notion of a data class antigen, which is used to represent a class of data. the produced rules are evolved by our clonal selection algorithm (CSA), which extends the recently proposed CLONALG algorithm. In CSA, among other new features, a receptor editing step has been incorporated. Moreover, the rules themselves are represented as antibodies that are coded as GEP chromosomes in order to exploit the flexibility and the expressiveness of such encoding. The proposed hybrid technique is tested on a set of benchmark problems in comparison to GEP. In almost all problems considered, the results are very satisfactory and outperform conventional GEP both in terms of prediction accuracy and computational efficiency.

关键词： Artificial immune systems clonal selection principle data mining gene expression programming

来源：评论

学校读者我要写书评

暂无评论

A new predictive model for furrow irrigation infiltration using gene expression programming

引用

COMPUTERS AND ELECTRONICS IN AGRICULTURE 2016年 122卷 168-175页

作者： Yassin, Mohamed A. Alazba, A. A. Mattar, Mohamed A. King Saud Univ Alamoudi Water Res Chair Riyadh Saudi Arabia King Saud Univ Dept Agr Engn Riyadh Saudi Arabia Agr Res Ctr Agr Engn Res Inst AEnRI Giza Egypt

This study investigates the ability of gene expression programming (GEP) in modeling of the infiltrated water volume (Z) under furrow irrigation. Field data were collected in the literature study for modeling Z covering wide range of opportunity time. Five variables were used as input parameters;inflow rate (Q(o)), furrow length (L), waterfront advance time at the end of the furrow (T-L), infiltration opportunity time (T-o) and cross-sectional area of the inflow (A(o)). The following statistical parameters that coefficient of determination (R-2), overall index of the model performance (OI), root mean square errors (RMSE) and mean absolute errors (MAE) are used as comparing criteria for the evaluation of the models' performances. The best value of the statistical parameters which range in training, testing and validation processes as the following (R-2 = 95-97%;OI = 94-97%;RMSE = 0.013-0.009 m(3) m(-1);and MAE = 0.011-0.007 m(3) m(-1)) implies that the GEP model provides an excellent fit for the measured data. A comparison is made between the estimates provided by the GEP and the two-point method. The comparison results reveal that the GEP models are superior to two-point method. Furthermore, the remarkable advantage of GEP was that it resulted in an explicit equation for the estimation of the Z under furrow irrigation. (C) 2016 Elsevier B.V. All rights reserved.

关键词： gene expression programming Furrow irrigation Infiltrated water volume

来源：评论

学校读者我要写书评

暂无评论

An optimized gene expression programming model for forecasting the national CO₂ emissions in 2030 using the metaheuristic algorithms

引用

APPLIED ENERGY 2018年 228卷 808-820页

作者： Hong, Taehoon Jeong, Kwangbok Koo, Choongwan Yonsei Univ Dept Architecture & Architectural Engn Seoul 03722 South Korea Kyonggi Univ Dept Architectural Engn Suwon 16227 South Korea

To cope with the approaching POST-2020 scenario, the national CO2 emission in the building sector, which accounts for 25.5% of the total CO2 emissions, should be managed effectively and efficiently. To do this, it is essential to forecast the national CO2 emissions in the building sector by region. As the South Korean government does not currently do this by region, regional characteristics are rarely taken into consideration when managing the national CO2 emissions in the building sector. Towards this end, this study developed an optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms. Compared to the forecasting performance of the gene expression programming model, the forecasting performance of the optimized gene expression programming harmony search optimization model has improved by 7.11, 2.05, and 2.06% in terms of the mean absolute error, root mean square error, and mean absolute percentage error, respectively. Various national CO2 emissions scenarios in the building sector were established in order to better analyze the variation range of the national CO2 emissions in the building sector. Compared to the national CO2 emissions in 2016 (i.e., scenario 1: 41,337 ktCO(2);scenario 2: 45,373 ktCO(2);scenario 3: 46,024 ktCO(2)) in multi-family housing complexes, the national CO2 emissions in 2030 (i.e., scenario 1: 37,579 ktCO(2);scenario 2: 37,736 ktCO(2);scenario 3: 37,754 ktCO(2)) in multi-family housing complexes are forecasted to increase by 10.00-21.91%. The developed optimized gene expression programming harmony search optimization model will potentially be able to assist policymakers in central and local governments forecast the national CO2 emissions in 2030. Through this, national CO2 emission management that more closely reflects the characteristics at the regional or national level can be supported.

关键词： National CO2 emissions gene expression programming Harmony search optimization algorithm Particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Sediment transport modeling in open channels using neuro-fuzzy and gene expression programming techniques

引用

WATER SCIENCE AND TECHNOLOGY 2019年第12期79卷 2318-2327页

作者： Kargar, Katayoun Safari, Mir Jafar Sadegh Mohammadi, Mirali Samadianfard, Saeed Urmia Univ Dept Civil Engn Orumiyeh Iran Yasar Univ Dept Civil Engn Izmir Turkey Univ Tabriz Dept Water Engn Fac Agr Tabriz Iran

Deposition of sediment is a vital economical and technical problem for design of sewers, urban drainage, irrigation channels and, in general, rigid boundary channels. In order to confine continuous sediment deposition, rigid boundary channels are designed based on self-cleansing criteria. Recently, instead of using a single velocity value for design of the self-cleansing channels, more hydraulic parameters such as sediment, fluid, flow and channel characteristics are being utilized. In this study, two techniques of neuro-fuzzy (NF) and gene expression programming (GEP) are implemented for particle Froude number (Fr-p) estimation of the non-deposition condition of sediment transport in rigid boundary channels. The models are established based on laboratory experimental data with wide ranges of sediment and pipe sizes. The developed models' performances have been compared with empirical equations based on two statistical factors comprising the root mean square error (RMSE) and the concordance coefficient (CC). Besides, Taylor diagrams are used to test the resemblance between measured and calculated values. The outcomes disclose that NF4, as the precise NF model, performs better than the best GEP model (GEP1) and regression equations. As a conclusion, the obtained results proved the suitable accuracy and applicability of the NF method in Fr-p estimation.

关键词： gene expression programming neuro-fuzzy rigid boundary channel sediment transport self-cleansing urban drainage

来源：评论

学校读者我要写书评

暂无评论

Distributed electricity load forecasting model mining based on hybrid gene expression programming and cloud computing

引用

PATTERN RECOGNITION LETTERS 2018年第Jul.15期109卷 72-80页

作者： Deng, Song Yuan, Changan Yang, Lechan Zhang, Liping Nanjing Univ Posts & Telecommun 9 Wenyuan Rd Nanjing 210023 Jiangsu Peoples R China Guangxi Teachers Educ Univ 175 Mingxiu East Rd Nanning 530023 Peoples R China Nanjing Univ 163 Xianlin Rd Nanjing 210093 Jiangsu Peoples R China

Load forecasting is an important part of power grid management. Accurate and timely load forecasting is of great significance to formulate economical and reasonable power allocation plan, improve safety and economy of power grid operation and improve power quality. In this paper, in order to find electricity load forecasting model, we propose an electricity load forecasting function mining algorithm based on artificial fish swarm and gene expression programming (ELFFM-AFSGEP). On the basis, distributed load forecast model mining based on hybrid gene expression programming and cloud computing (DLFMM-HGEPCloud) is proposed to solve the problem of massive electricity load forecasting. In order to better solve global electricity load forecasting model, error minimization crossover is introduced into DLFMM-HGEPCloud. The performance of the proposed algorithm in this paper is evaluated with a real-world dataset, and compared with GEP and some published algorithms by using the same dataset. Experimental results show that our proposed algorithm has an advantage in average time-consumption, average number of convergence, forecasted accuracy and excellent parallel performance in speedup and scaleup. (C) 2017 Published by Elsevier B.V.

关键词： Load forecasting Artificial fish swarm gene expression programming Cloud computing

来源：评论

学校读者我要写书评

暂无评论

Hydropower Plant Site Assessment by Integrated Hydrological Modeling, gene expression programming and Visual Basic programming

引用

WATER RESOURCES MANAGEMENT 2016年第7期30卷 2517-2530页

作者： Al-Juboori, Anas Mahmood Guven, Aytac Gaziantep Univ Dept Civil Engn TR-27310 Gaziantep Turkey Univ Mosul Dams & Water Resources Res Ctr Mosul Iraq

The integrated hydrological modeling techniques were used to create a conceptual rainfall-runoff model for Hurman River watershed. The HEC-HMS and water modeling system (WMS) were used to generate the required runoff data at any location within the watershed based on the flow duration curve (FDC) analysis. The modeling results showed a good agreement between the observed and the simulated runoff data. The model accuracy was confirmed through four well statistical indicators, Nash-Sutcliffe efficiency (NSE), coefficient of determination (R-2), mean error (ME) and root mean square error (RMSE). The watershed was divided into 130 sub-basins by adding outlet at each 1 km distance along the stream network and runoff was simulated by using daily rainfall data. The 130 flow duration curves were estimated. gene expression programming was used to develop a mathematical expression based on the results of rainfall-runoff model to generate flow duration curve at any location along the stream network. A Visual Basic Computer Program was developed with visual interface in Microsoft Excel software to run-of-the river type hydropower plant site assessment. A searching algorithm to select optimum site to install the hydropower project was developed according to the power or head criteria. The results showed that 1 MW as input power criteria revealed optimum hydroelectricity generation compared to other alternative design criteria.

关键词： Rainfall-runoff modeling HEC-HMS gene expression programming Visual Basic Flow duration curve Hydropower plant

来源：评论

学校读者我要写书评

暂无评论

A robust MILP and gene expression programming based on heuristic rules for mixed-model multi-manned assembly line balancing

引用

APPLIED SOFT COMPUTING 2021年 109卷 107513-107513页

作者： Zhang, Zikai Tang, Qiuhua Chica, Manuel Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan Hubei Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan Hubei Peoples R China Univ Granada Andalusian Res Inst Data Sci & Computat Intellige Dept Comp Sci & Artificial Intelligence DaSCI Granada 18071 Spain Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia

Current dynamic markets require manufacturing industries to organize a robust plan to cope with uncertain demand planning. This work addresses the mixed-model multi-manned assembly line balancing under uncertain demand and aims to optimize the assembly line configuration by a robust mixed-integer linear programming (MILP) model and a robust solution generation mechanism embedded with dispatching rules. The proposed model relaxes the cycle time constraint and designs robust sequencing constraints and objective functions to ensure the line configuration can meet all the demand plans. Furthermore, two solution generation mechanisms, including a task-operator-sequence and an operator-task-sequence, are designed. To quickly find a suitable line configuration, a gene expression programming (GEP) approach with multi-attribute representation is proposed to obtain efficient dispatching rules which are ultimately embedded into the solution generation mechanisms. Experimental results show that solving the proposed MILP model mathematically is effective when tackling small and medium-scale instances. However, for large instances, the dispatching rules generated by the GEP have significant superiority over traditional heuristic rules and those rules mined by a genetic programming algorithm. (C) 2021 Published by Elsevier B.V.

关键词： Uncertain demand Robust optimization Mixed-model multi-manned assembly line gene expression programming

来源：评论

学校读者我要写书评

暂无评论

Modelling the edge breakout shear capacity of single anchors using gene expression programming

引用

NEURAL COMPUTING & APPLICATIONS 2022年第12期34卷 9635-9646页

作者： Olalusi, Oladimeji Benedict Durgapershad, Avishkar Awoyera, Paul Oluwaseun Kolawole, John Temitope Univ KwaZulu Natal Struct Engn & Computat Mech Grp SECM Dept Civil Engn Durban South Africa Covenant Univ Dept Civil Engn Ota Nigeria Obafemi Awolowo Univ Dept Bldg Ife Nigeria Loughborough Univ Sch Architecture Bldg & Civil Engn Leicester Leics England

The use of soft computing techniques is becoming more common in providing solutions to complex engineering problems such as the concrete breakout strength of anchor. Available techniques include semi-empirical equations that are known to over or underpredict and some soft computing techniques that is incapable of generating predictive equations. This study proposes a gene expression programming (GEP)-based mathematical model to predict the concrete edge breakout capacity of single anchors loaded in shear. In doing so, an experimental database compiled by the American Concrete Institute (ACI) Committee 355, containing 366 samples, was used for the model training and testing. The independent variables considered in the model development are the edge distance, anchor diameter, embedment depth and concrete strength. Moreover, the predictive performance of the developed model was compared to that of the existing models proposed in ACI 318 and the Eurocode 2 (EC2) design standards. The assessment showed that the proposed GEP-based model provided a much more uniform and accurate prediction of the actual strength than the models in the existing design standards. The proposed mathematical model is simple and robust and is expected to be very useful for evaluating the concrete breakout shear capacity of single anchors in pre-planning and pre-design phases;that is, towards inclusions in design standards.

关键词： Fastening to concrete gene expression programming Concrete edge breakout failure Edge distance Anchorages Artificial intelligence Soft computing

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：