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APPLICATION OF gene expression programming FOR ESTIMATING TOTAL EMISSIVITY OF H2O-CO2 MIXTURES IN AIR-FUEL COMBUSTION WITHOUT SOOT FORMATION

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COMPUTATIONAL THERMAL SCIENCES 2015年第3期7卷 191-215页

作者： Maghbooli, Babak Najafi, Hamidreza Sobati, Mohammad Amin Farayand Sabz Engn Co Tehran Iran Iran Univ Sci & Technol Sch Chem Engn Tehran 16765163 Iran

In the present study, a gene expression programming algorithm has been applied to propose new and accurate correlations for estimating total emissivity of CO2-H2O homogeneous mixtures in air-fuel combustion environment without soot formation at atmospheric condition. The main parameters of the correlations include temperature (T: 300-2500 K), partial pressure of water vapor (p(w): 2.0265-20.265 kPa), partial pressure of carbon dioxide (p(c): 4.053-20.265 kPa), and mean beam length (L: 0.01-25 m). The RADCAL statistical narrow-band model was used in order to generate 78,000 values of total emissivity to be used as the benchmark data for the correlations. 34,620 total emissivity data points were selected for developing the correlations and 43,380 data points were selected for the optimization and testing the capability of the correlations. All the benchmark data were split into two sub-data sets based on temperature (i.e., the first data set: 300 K <= T <1200 K, the second data set: 1200 K <= T <= 2500 K). For each sub-data set, different correlations have been developed. The average absolute relative deviations of the estimated results from the benchmark data are 3.6% of the low temperature, and 3.9% of the high temperature data sets.

关键词： gene expression programming total gas emissivity combustion statistical narrow-band model water vapor carbon dioxide homogenous media RADCAL

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Prediction model for compressive arch action capacity of RC frame structures under column removal scenario using gene expression programming

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STRUCTURES 2020年 25卷 212-228页

作者： Azim, Iftikhar Yang, Jian Javed, Muhammad Faisal Iqbal, Muhammad Farjad Mahmood, Zafar Wang, Feiliang Liu, Qing-feng Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn State Key Lab Ocean Engn Shanghai 200240 Peoples R China State Key Lab Green Bldg Mat Beijing Peoples R China COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad Pakistan GIK Inst Engn Sci & Technol Dept Civil Engn Topi 23460 Swabi Pakistan Univ Agr Dept Maths Stats & Comp Sci Peshawar Pakistan

Compressive arch action (CAA) is one of the important resistance mechanisms against progressive collapse in reinforced concrete (RC) frame buildings at small deformations. In this study, the distinguishing features of gene expression programming (GEP) are utilized in order to establish a prediction model for the CAA capacity of RC beam-column substructures. The proposed equation correlates CAA capacity with six input parameters, i.e., compressive strength of concrete (f'(c)), double-beam span-to-depth ratio (L/d), width of beam (b), top and bottom longitudinal reinforcement ratio at the joints (rho(t) & rho(b), respectively), and the ratio of axial restraints stiffness to the axial stiffness of beam (alpha). The data for the above-mentioned variables is collected from different studies to develop the proposed model and subsequently implemented for the verification purpose. Different statistical and external validations criteria recommended in literature are carried out to assess the performance of the developed model. Furthermore, sensitivity and parametric analyses are also performed using criteria available in literature. The performance of the genetically developed GEP model was checked and compared with models developed by using regression techniques. The GEP model demonstrated superior performance to linear and nonlinear regression methods. The results show that the developed model is successfully capable of evaluating the CAA capacity of RC beam-column substructures and is incredibly imperative for prediction rationales.

关键词： Compressive arch action RC beam-column substructure Progressive collapse gene expression programming Prediction model

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A Hybrid Logistic Regression: gene expression programming Model and Its Application to Mineral Prospectivity Mapping

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NATURAL RESOURCES RESEARCH 2022年第4期31卷 2041-2064页

作者： Xiao, Fan Chen, Weilin Wang, Jun Erten, Oktay Sun Yat Sen Univ Sch Earth Sci & Engn Zhuhai 519000 Peoples R China Guangdong Key Lab Geol Proc & Mineral Resources Zhuhai 519000 Peoples R China Guangdong Prov Key Lab Geodynam & Geohazards Zhuhai 519000 Peoples R China Southern Lab Ocean Sci & Engn Zhuhai 519000 Peoples R China Univ Alberta Ctr Computat Geostat Edmonton AB Canada

Mineral prospectivity mapping (MPM) is a fundamental task in mineral exploration. The logistic regression (LR) method has been widely used as a data-driven tool for MPM because it is compact and straightforward, and it presents explicit explanations. However, there exists the inherent deficiency of linear assumption in the ordinary LR method. In order to overcome this disadvantage, a hybrid model (i.e., hybrid GEP-LR) that combines LR with gene expression programming (GEP) has been developed. It uses the evolutionary algorithm of GEP to discover automatically the symbolic function defined in the logit transformation of the ordinary LR method without any assumption. To investigate the performance of the hybrid GEP-LR hybrid method, a synthetic data modeling and a case study of prospectivity mapping for porphyry Cu-Mo polymetallic deposits (PCMPDs) in the Eastern Tianshan region, northwestern China are presented here. In the synthetic data modeling, two different datasets, namely linearly separable dataset and nonlinearly separable dataset, were generated by Monte Carlo simulation. By defining the area under the receiver operating characteristic curve (AUC) as a fitness function, these two datasets were used to validate the efficiency of the hybrid GEP-LR method in classification of both linearly and nonlinearly separable datasets. In the case study, 15 hybrid GEP-LR models were designed using 15 different evolutionary strategies of the GEP algorithm including the number of genes, the head length of every gene, and the set of functions. Binary maps of prospectivity for PCMPDs derived using these 15 hybrid GEP-LR models with the fitness function of AUC were compared and contrasted with the one derived using the ordinary LR method. The results demonstrate that the proposed hybrid GEP-LR method is a robust data-driven tool that can deal with both linear and nonlinear problems in data mining. It outperforms the ordinary LR method in predicting target areas for mineral pro

关键词： Logistic regression gene expression programming Mineral prospectivity mapping Porphyry deposits Eastern Tianshan

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Forecasting urban water consumption using bayesian networks and gene expression programming

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EARTH SCIENCE INFORMATICS 2022年第1期15卷 623-633页

作者： Mousavi-Mirkalaei, Pezhman Roozbahani, Abbas Banihabib, Mohammad Ebrahim Randhir, Timothy O. Univ Tehran Coll Aburaihan Dept Water Engn Tehran Iran Univ Massachusetts Dept Environm Conservat Amherst MA 01003 USA

Forecasting Urban Water Consumption (UWC) has a significant impress in efficient urban water management in rapidly growing cities in arid regions. Enhancing forecasting accuracy of UWC using novel models is a crucial requirement in order to the management of smart cities. In this study, Bayesian Networks (BN) is developed as a probabilistic model and compared to gene expression programming (GEP) model as an evolutionary algorithm for forecasting UWC. The amount of current water consumption predicts future water consumption. The scenario with sunshine hours was added to the parameter set as the best scenario in both BN and GEP models based on comparison of Root Mean Square Error (0.11, 0.16), Mean Absolute Relative Error (0.02, 0.05), Max Root Error (0.26, 0.26), and Coefficient of determination (0.8, 0.7), respectively. The outcomes indicate that the BN model provided a more desirable efficiency compared to the GEP model. Furthermore, it can be concluded that the sunshine hour has a considerable influence on UWC, and the ability of the BN model is greatly enhanced by adding this predictor to forecast UWC in a city in an arid region with rapid population growth.

关键词： Bayesian Network Daily Water Consumption Urban Water gene expression programming Sunshine Hour

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Short-Term Load Forecasting Based on Improved gene expression programming

Short-Term Load Forecasting Based on Improved Gene Expressio...

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7th World Congress on Intelligent Control and Automation

作者： Yin, Jinliang Huo, Limin Guo, Lirui Hu, Jie Agr Univ Hebei Dept Mech & Elect Engn Baoding Hebei Province Peoples R China

ISBN: (纸本)9781424421138

gene expression programming (GEP) was improved (IGEP) to overcome the shortcomings that the initial population is generated randomly, there are no standards to measure the gene, and evolution results got before can't be utilized. Quasi-law of nature that excessive multiplication, environmental factor selecting, using pheromones to measure gene, passing on from generation to generation and self-adaptive mutation rate method are proposed and that make the Improved gene expression programming (IGEP) is capable of memory and self-adaptive ability. The Improved gene expression programming (IGEP) was applied to short-term load forecasting. Firstly, the load series of the same time but different days are chosen as the training samples. Secondly, the load samples are filtered and processed generally. And finally, the short-term load is forecasted classified by weekday and weekend after eliminating the pseudo-data. Compared with the results forecasted by means of GP and GEP, it proves that the method of IGEP in short-term load forecasting is better.

关键词： gene expression programming improved gene expression programming power system short-term load forecasting

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gene expression programming Algorithms for Optimization of Water Distribution Networks

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Procedia Engineering 2012年 37卷 359-364页

作者： Huanhuan Wang Shuming Liu Fanlin Meng Mingming Li School of Enviroment Tsinghua UniversityBeijing100084China

Water distribution system (WDS), as the important and expensive infrastructure, plays a crucial role in supplying water for the citizens living in the urban areas. The huge cost of the system drives researchers to seek the optimal cost design. During the last several decades, evolutionary methods such as genetic algorithms (GA), ant colony optimization algorithms (ACO) and simulated annealing (SA) have been found to explore the optimal combination of pipe diameters for design of WDS. However, in the case of complex distribution network, those methods suffer with low computational efficiency. In this paper, the gene expression programming (GEP) algorithms, a method based on the genetic algorithms, is adopted to solve the optimal design of WDS. This method is applied to the Hanoi benchmark network, the result of 6.081 million costs and 15,000 evaluations is combined with that obtained with GA and SA. From the results, it is observed that the proposed algorithm is an advanced alternative for the design of WDS, taking the computational efficiency and the ability of finding global or near-global optimal solutions into consideration.

关键词： Water distribution network water supply gene expression programming Optimal Design

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RETRACTED: Modeling Ductile-to-Brittle Transition Temperature of Functionally Graded Steels by gene expression programming (Retracted article. See vol. 29, pg. 202, 2020)

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2012年第4期21卷 465-492页

作者： Milani, Amir Ali Nazari, Ali Islamic Azad Univ Dept Mat Sci & Engn Saveh Branch Saveh Iran

In this article, a model based on gene expression programming for predicting ductile-to-brittle transition temperature of functionally graded steels in both crack divider and crack arrester configurations has been presented. Functionally, graded steels containing graded ferritic and austenitic regions together with bainite and martensite intermediate layers were produced by electroslag remelting. For the purpose of building the model, training and testing using experimental results from 140 specimens produced from two basic composites were conducted. The data used as input in gene expression programming models are arranged in a format of six parameters that cover the FGS type, the crack tip configuration, the thickness of graded ferritic region, the thickness of graded austenitic region, the distance of the notch from bainite or martensite intermediate layer, and temperature. According to these input parameters, in the gene expression programming, the ductile-to-brittle transition temperature of each FGS specimen was predicted. The training and testing results in the gene expression programming model have shown strong potential for predicting the ductile-to-brittle transition temperature of each FGS specimens.

关键词： crack arrester crack divider DBTT ESR functionally graded steels gene expression programming

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RETRACTED: Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer (Retracted Article)

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NEURAL COMPUTING & APPLICATIONS 2019年第2期31卷 767-776页

作者： Nazari, Ali Islamic Azad Univ Saveh Branch Dept Mat Sci & Engn Saveh Iran

In the present study, compressive strength of lightweight aluminosilicate geopolymers produced by fine fly ash and rice husk bark ash together with palm oil clinker (POC) aggregates has been modeled by gene expression programming. To build the model, training and testing by using experimental results from 144 specimens were conducted. The used data in the models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, in the gene expression programming models, the compressive strength of each specimen was predicted. The best value of R-2 and the minimum values of root mean square error (RMSE) and absolute percentage error (MAPE) are 0.9669, 2.583, and 1.984, respectively, all in training phase. The minimum value of R-2 and the maximum values of RMSE and MAPE are 0.9456, 3.067, and 2.356, respectively, all in testing phase. The training and testing results in the models have shown a strong potential for predicting the compressive strength of the lightweight geopolymer specimens in the considered range and one may predict them with a tiny error.

关键词： Aluminosilicate geopolymer Compressive strength gene expression programming

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RETRACTED: Prediction of Compressive Strength of Geopolymers with Seeded Fly Ash and Rice Husk-Bark Ash by gene expression programming (Retracted article. See vol. 29, pg. 202, 2020)

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INTERNATIONAL JOURNAL OF DAMAGE MECHANICS 2012年第8期21卷 1202-1226页

作者： Nazari, Ali Riahi, Shadi Khalaj, Gholamreza Bohlooli, Hamid Kaykha, Mohammad Mehdi Islamic Azad Univ Dept Mech Engn Birjand Branch Birjand Iran Univ Zabol Dept Mech Engn Zabol Iran

In the present work, compressive strength of inorganic polymers (geopolymers) made from seeded fly ash and rice husk-bark ash has been predicted by gene expression programming. Different specimens were subjected to compressive strength tests at 7 and 28 days of curing. One set of the specimens were cured at room temperature until reaching to 7 and 28 days and the other sets were oven cured for 36 h at the range of 40-90 degrees C and then room cured until 7 and 28 days. A model based on gene expression programming for predicting the compressive strength of the specimens was presented. To build the model, training and testing using experimental results from 120 specimens were conducted. According to the input parameters, in the gene expression programming models, the compressive strength of each specimen was predicted. The training and testing results in the gene expression programming models have shown a strong potential for predicting the compressive strength of the geopolymer specimens.

关键词： compressive strength fly ash FTIR gene expression programming geopolymer particle size rice husk-bark ash seeded mixture

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Optimizing hadoop parameter settings with gene expression programming guided PSO

Optimizing hadoop parameter settings with gene expression pr...

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IEEE International Conference on Internet of People (IoP)

作者： Khan, Mukhtaj Huang, Zhengwen Li, Maozhen Taylor, Gareth A. Khan, Mushtaq Brunel Univ London Dept Elect & Comp Engn Uxbridge UB8 3PH Middx England Abdul Wali Khan Univ Mardan Dept Comp Sci Mardan Pakistan Tongji Univ Key Lab Embedded Syst & Serv Comp Shanghai Peoples R China Pakistan Ordnance Factory Dept Informat Technol Lahore Punjab Pakistan

Hadoop MapReduce has become a major computing technology in support of big data analytics. The Hadoop framework has over 190 configuration parameters, and some of them can have a significant effect on the performance of a Hadoop job. Manually tuning the optimum or near optimum values of these parameters is a challenging task and also a time consuming process. This paper optimizes the performance of Hadoop by automatically tuning its configuration parameter settings. The proposed work first employs gene expression programming technique to build an objective function based on historical job running records, which represents a correlation among the Hadoop configuration parameters. It then employs particle swarm optimization technique, which makes use of the objective function to search for optimal or near optimal parameter settings. Experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings. Moreover, it outperforms both rule-of-thumb settings and the Starfish model in Hadoop performance optimization. (C) 2016 The Authors. Concurrency and Computation: Practice and Experience Published by John Wiley & Sons Ltd.

关键词： hadoop mapreduce big data analytics gene expression programming particle swarm optimization

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