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Empirical models for longitudinal dispersion coefficient in natural streams

JOURNAL OF HYDROLOGY 2019年 575卷 1359-1361页

作者： Gharabaghi, Bahram Sattar, Ahmed M. A. Univ Guelph Sch Engn Guelph ON NIG 2 W1 Canada Cairo Univ Fac Engn Dept Irrigat & Hydraul Giza Egypt

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Joint shear strength models for exterior RC beam-column connections exposed to biaxial and uniaxial cyclic loading

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JOURNAL OF BUILDING ENGINEERING 2020年 30卷

作者： Murad, Yasmin Univ Jordan Civil Engn Dept Amman 11942 Jordan

Joint shear strength models available in the literature can predict the shear strength for reinforced concrete (RC) beam-column connections exposed to uniaxial cyclic loading, while an accurate biaxial joint shear strength model is still lacking. gene expression programming (GEP) is used in this research to develop uniaxial and biaxial joint shear strength models for exterior RC beam-to-column connections exposed to uniaxial and biaxial cyclic loading respectively. The GEP models are developed based on an experimental database available in the literature, where the models are randomly trained and tested. Uniaxial joint shear strength is also predicted using the ACI 352 and ASCE 41 formulations for connections exposed to uniaxial cyclic loading. The performance of the GEP models is statistically evaluated using the coefficient of determination R-squared. The R-squared values are 79%, 79%, 95% and 93% for the ACI, ASCE, uniaxial GEP and biaxial GEP models, respectively. The R-squared values of the GEP models are high, which confirms their accuracy and indicates that they are more fitting to the experimental results than the ACI and ASCE formulations.

关键词： Joint shear strength Biaxial cyclic loading Uniaxial cyclic loading Exterior RC beam-column connections ACI 352 ASCE 41 gene expression programming

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gene expression programming with multiple chromosomes

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2011年第4期14卷 235-241页

作者： Wang, Bo Yao, Min Zhu, Rong Zhejiang Univ Sch Comp Sci Hangzhou Zhejiang Peoples R China Zhejiang Chinese Med Univ Sch Informat Technol Hangzhou Zhejiang Peoples R China Zhejiang Univ Sch Comp Sci & Technol Hangzhou Zhejiang Peoples R China Jiaxing Univ Sch Informat Engn Jiaxing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

gene expression programming (GEP) has been widely used in the areas of pattern recognition and knowledge discovery, however, when dealing with complicated problems, it is very time-consuming and the number of generations is large. In order to overcome these drawbacks, this paper proposes a multi-chromosomes GEP algorithm (MC-GEP). Firstly, the individual is composed of multiple chromosomes, each chromosome consists of one or more genes. Secondly, the expression of each chromosome or combinations of several chromosomes may be chosen to indicate the individual. Finally, chromosome recombination is changed and performed orderly like meiosis. Experimental results show that MC-GEP can reduce the running time and the number of generations with respect to the GEP.

关键词： gene expression programming GEP MC-GEP multi-chromosomes

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Model genetic Rules Based Systems for Evaluation of Projects

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Procedia Computer Science 2020年 170卷 390-395页

作者： Jesus Silva John Freddy Escobar Gomez Ernesto Steffens Sanabria Hugo hernandez Palma Midori Ikeda Jorge Linares Nohora Mercado Corporación Universitaria Minuto de Dios –Uniminuto Barranquilla Colombia Universidad de la Costa Barranquilla Colombia

The process of project evaluation is of vital importance for decision-making in organizations. In the particular case of IT projects, the historical average of successful projects is 30.7%, while renegotiated projects are 47.3% and cancelled projects are 22% [1]. These figures mean that huge budgets are affected every year by errors in planning or control and monitoring of projects, with an economic and social impact. The objective of this research is to evaluate the MCGEP evolutionary algorithm in different versions databases with information on the evaluation of IT projects. The aim is to determine the possibility of applying an evolutionary algorithm that uses programming of genetic expressions as opposed to others of greater use.

关键词： genetic Algorithms gene expression programming MCGEP Algorithm Project Evaluation Rules learning

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GEP-based classifier with drift detection for mining imbalanced data streams

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Procedia Computer Science 2020年 176卷 41-49页

作者： Joanna Jedrzejowicz Piotr Jedrzejowicz Institute of Informatics Faculty of Mathematics Physics and Informatics University of Gdansk 80-308 Gdansk Poland Department of Information Systems Gdynia Maritime University Morska 83 81-225 Gdynia Poland

Mining data streams require to cope with time, data size and possible concept drift constraints. Even more challenging is the case where, apart from the above, one has to deal with imbalanced data. Mining non stationary and imbalanced data streams is a relatively new area of research. In this paper, we propose the gene expression programming (GEP) classifier with drift detection and data reuse for mining imbalanced data streams. GEP is used to evolve a complex expression tree returning predictions. Drift detector role is to signal the occurrence of drift which triggers inducing a new learner. Data reuse mechanism allows for improving the balance between minority and majority instances in a subset of data used for evolving the learner. The proposed approach is validated experimentally. The experiment results confirm that our classifier produces high-quality predictions.

关键词： Imbalanced non stationary data streams gene expression programming data reuse

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Biometric Identification from Human Aesthetic Preferences

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SENSORS 2020年第4期20卷 1133-1133页

作者： Sieu, Brandon Gavrilova, Marina Univ Calgary Dept Comp Sci 2500 Univ Dr NW Calgary AB T2N 1N4 Canada

In recent years, human-machine interactions encompass many avenues of life, ranging from personal communications to professional activities. This trend has allowed for person identification based on behavior rather than physical traits to emerge as a growing research domain, which spans areas such as online education, e-commerce, e-communication, and biometric security. The expression of opinions is an example of online behavior that is commonly shared through the liking of online images. Visual aesthetic is a behavioral biometric that involves using a person's sense of fondness for images. The identification of individuals using their visual aesthetic values as discriminatory features is an emerging domain of research. This paper introduces a novel method for aesthetic feature dimensionality reduction using gene expression programming. The proposed system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40,000 images demonstrate a 95% accuracy of identity recognition based solely on users' aesthetic preferences.

关键词： pattern recognition behavioral biometrics biometric security gene expression programming visual aesthetics human-machine interactions

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Modified Particle Swarm Optimization-Artificial Neural Network and gene expression Programing for Predicting High Temperature Oxidation Behavior of Ni-Cr-W-Mo Alloys

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INTERNATIONAL JOURNAL OF ENGINEERING 2020年第11期33卷 2327-2338页

作者： Hasibi, H. Mahmoudian, A. Khayati, G. R. Shahid Bahonar Univ Kerman Dept Mat Sci & Engn Kerman Iran Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Met Kerman Iran

This paper is an attempt to model the oxidation behavior of Ni-base alloys by considering the alloying elements, i.e., Cr, W, Mo, as variables. Modified particle swarm optimization-artificial neural network (MPSO-ANN) and gene expression programming (GEP) techniques were employed for modeling. Data set for construction of (MPSO-ANN) and GEP models selected from 66 cyclic oxidation performed in the temperature range of 400-1150 degrees C for 27 different Ni-based alloy samples at various amounts of Cr, W, and Mo. The weight percent of alloying elements selected as input variables and the changes of weight during the oxidation cycle considered as output. To analyze the performance of proposed models, various statistical indices, viz. root mean squared error (RMSE) and the correlation coefficient between two data sets (R-2) were utilized. The collected data of GEP randomly divided into 21 training sets and 6 testing sets. The results confirmed that the possibility of oxidation behavior modeling using GEP by 12 2. = 0.981, RMSE =0.0822. By consideration of oxidation resistance as criteria, Cr, Mo, and W enhanced the oxidation resistance of Ni-based alloys. The results showed that in the presence of Cr as alloying element, especially at Cr contents higher than 22 wt.%, the effect of W and Mo were negligible. However, the same trend was reversed at the sample with Cr content lower than 20 wt.%. In these cases, the effect of W and Mo on oxidation resistance were significantly enhanced.

关键词： Artificial Neural Network gene expression programming High-temperature Alloys Modified Particle Swarm Optimization Oxidation Behavior

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Hyper heuristic method based on improved GEP for scheduling problems in shop floor

Hyper heuristic method based on improved GEP for scheduling ...

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International Conference on Industrial Engineering and Systems Management (IESM)

作者： Nie, Li Bai, Yuewei Fang, Xiaohong Wang, Xiaogang Shanghai Polytech Univ Coll Engn Shanghai Peoples R China

ISBN: (纸本)9781728115665

Considering the complex and inexplicit relationship between the working conditions and scheduling performance in shop floor, the paper proposed a hyper heuristic method based on gene expression programming (GEP) to customize efficient job dispatch rules and machine assignment rules for scheduling problems in shop floor. Since the traditional GEP has the shortcomings such as slow speed, inadequate precision and many parameters, which are not suitable for the actual environment, the paper made improvements on traditional GEP in two aspects: individual structure and the population evolution adaptation strategy. Through the simulation experiments the efficiency of the improved GEP machine learning approach is validated.

关键词： production scheduling problem scheduling rule machine learning hyper heuristic method gene expression programming

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Proposing a rigorous empirical model for estimating the bubble point pressure in heterogeneous carbonate reservoirs

ADVANCES IN GEO-ENERGY RESEARCH

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ADVANCES IN GEO-ENERGY RESEARCH 2020年第2期4卷 126-134页

作者： Rostami, Alireza Daneshi, Alireza Miri, Rohaldin Petr Univ Technol Put Dept Petr Engn Ahwaz Iran Shahid Chamran Univ Oil & Gas Fields & Equipment Res & Dev Ctr Ahwaz Iran Iran Univ Sci & Technol Lust Dept Petr Engn Tehran Iran

Bubble point pressure is of great significance in reservoir engineering calculations affecting the success of reservoir simulation. For determining this valuable parameter, experimental tests are the most reliable techniques;however, these measurements are costly and time-consuming. So, it is crucial to propose an empirical model for estimating bubble point pressure. The existing correlations mainly have large errors and develop based on restricted database from a specific geographical location. As a result, development of an all-inclusive correlation is essential. In current article, gene expression programming (GEP) was used to create a generalized model for bubble point pressure estimation. To do this, an all-inclusive source of data was utilized for training and testing the model from the petroleum industry. Several statistical approaches including both illustration tools and diverse error functions were utilized to show the supremacy of the developed GEP model. Consequently, the recommended model is the most accurate as compared to the similar correlations in literature with the average absolute relative error (AARE = 11.41%) and determination coefficient (R-2 = 0.96). Furthermore, the solution gas-oil ratio shows to be the most influencing variable on determining bubble point pressure according to sensitivity analysis. The results of contour map analysis demonstrate that most portions of the experimental region are predicted via the GEP equation with fewer errors as compared to two well-known literature correlations. Finally, the proposed GEP model can be of high prominence for accurate bubble point pressure estimation.

关键词： Bubble point pressure gene expression programming correlation sensitivity analysis comprehensive error analysis

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Employing a gene expression-based Technique to Improve the Accuracy of Estimating the Total generated Power by Neighboring Photovoltaic Systems 9

Employing a Gene Expression-based Technique to Improve the A...

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9th International Conference on Power and Energy Systems (ICPES)

作者： Al-Hilfi, Hasanain A. H. Shahnia, Farhad Abu-Siada, A. Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA Australia Univ Basrah Comp Ctr Basrah Iraq Murdoch Univ Discipline Engn & Energy Perth WA Australia

ISBN: (纸本)9781728126586

The penetration of photovoltaic systems (PVs) is increasing and they are considered attractive options for electricity generation in distribution networks. This study focuses on estimating the total power generated by a group of neighboring PVs, spread in a locality while using a single pyranometer for measuring the solar irradiance. A new model has been proposed which employs the gene expression programming (GEP) technique for developing a correlation between the distribution of the PVs and the irradiance measured by the pyranometer in estimating the total power generated. The proposed technique considers the geographic variability reduction and employs a Wavelet Transform technique for the calculations. The effective performance of the proposed model is validated using the real data collected by the Solar Project at the University of Queensland, Brisbane, Australia. The studies reveal that the proposed technique yields more accurate results against the other existing approaches.

关键词： gene expression programming Geographic smoothing Photovoltaic system Variability reduction index

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