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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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An Improved gene expression programming Algorithm Based On Hybrid Strategy 8

An Improved Gene Expression Programming Algorithm Based On H...

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8th International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Wang, Chao-xue Zhang, Jing-jing Wu, Shu-ling Ma, Chun-sen Xian Univ Architecture & Technol Sch Informat & Control Engn Xian Peoples R China Chinese Acad Agr Sci Inst Plant Protect Beijing Peoples R China

ISBN: (纸本)9781509000227

gene expression programming (GEP) is a new evolutionary algorithm, which has the very good applications in the field of function finding. In view of the insufficiency of traditional GEP, this paper puts forward an improved gene expression programming algorithm based on hybrid strategy (HSI-GEP). This paper has two improvements: (1) using mirror and reset mechanism to replace the inferior individuals of population, to improve the quality and the diversity of population;(2) introducing the clonal selection before tournament selection in order to improve the mining ability of algorithm about the superior individuals. The experiments compared with the improved GEP from authoritative literatures about function finding problems have been carried on, and the results show that HSI-GEP is of high quality, has fast convergence rate and obvious competitiveness.

关键词： gene expression programming mirror replace reset mechanism clonal selection

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M5 Model Tree and gene expression programming for the Prediction of Metrological Parameters

M5 Model Tree and Gene Expression Programming for the Predic...

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International Conference on Computers, Communications and Systems (ICCCS)

作者： Raza, Khalid Jamia Millia Islamia Dept Comp Sci New Delhi 110025 India

ISBN: (纸本)9781467397544

Metrological parameter prediction is an important task carried out by metrological researchers and water resource analyst for the estimation of water demand, water resource planning, management and operation. Accurate predictions of these parameters are still an open problem. M5 model tree and gene expression programming are two popular machine learning techniques which are considered as white-box model. In this paper, we have applied two machine learning techniques, such as M5 model tree and gene expression programming, for the prediction of various metrological parameters at a weather station. Among these two techniques, one is better over other for the prediction of a set of parameters.

关键词： Metrological variable predictions gene expression programming Model tree Evolutionary algorithm

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Edit Distance Based Crossover Operator in gene expression programming 8

Edit Distance Based Crossover Operator in Gene Expression Pr...

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8th International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Li Qu Cheng Hongbing Lin, H. X. Zhejiang Univ Coll Comp Sci Hangzhou 310037 Zhejiang Peoples R China Delft Univ Technol Fac Elect Engn Math & Comp Sci Delft Netherlands

ISBN: (纸本)9781509000227

The population diversity greatly affects the evolutionary efficiency and solution quality of gene expression programming algorithm. Population diversity should be preserved by keeping certain distance between individuals in the population. Edit distance can describe the similarity of individuals well. Crossover is a way to create and maintain the distance of the individuals. In this paper, we propose two edit distance based crossover operators. Experimental results show that the proposed farthest edit distance based crossover operator is able to preserve the diversity of population and solve the optimization problem more efficiently.

关键词： gene expression programming population diversity Edit distance crossover operator

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Modeling Systems of Ordinary Differential Equations Using Immune Based gene expression programming 7

Modeling Systems of Ordinary Differential Equations Using Im...

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7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Xue, Siqing Wu, Jie China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

ISBN: (纸本)9781479986460

This paper aims to model system of ordinary differential equations by using a new hybrid gene expression programming algorithm. gene expression programming is a recently developed evolutionary computation method for model learning and knowledge discovery. The hybrid algorithm combined immune clonal selection algorithm and memetic algorithm with gene expression programming to find not only the structure of system of differential equations but also optimize its constant parameters. The idea of immune clone principle is incorporated into the evolution process to enhance the diversity of population and the memetic algorithm is introduced to improve the ability of local search. Experiments on benchmark problems have shown that the hybrid approach is able to provide highly competitive results compared with that of conventional genetic programming applied to this problem.

关键词： gene expression programming Ordinary Differential Equations Immune Clonal Selection Algorithm Memetic Algorithm

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gene expression programming as a basis for new generation of electricity demand prediction models

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期74卷 120-128页

作者： Mousavi, Seyyed Mohammad Mostafavi, Elham Sadat Hosseinpour, Fariba Islamic Azad Univ Sci & Res Branch Dept Geog & Urban Planning Tehran Iran Isfahan Univ Technol Dept Ind Engn Esfahan Iran Islamic Azad Univ Fac Econ & Accounting Cent Tehran Branch IAUCTB Tehran Iran

This study proposes a new gene expression programming (GEP) approach for the prediction of electricity demand. The annual population, gross domestic product, stock index, and total revenue from exporting industrial products were used to predict the electricity demand of the same year in Thailand. Several statistical criteria were used to verify the validity of the model. Further, the contributions of the influencing variables to the prediction of the electricity demand were analyzed. Correlation coefficient, root mean squared error and mean absolute percent error were used to evaluate the performance of the model. In addition to its high accuracy, the derived model outperforms regression and other soft computing-based models. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Electricity demand gene expression programming Prediction

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Forecasting Construction and Demolition Waste Using gene expression programming

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2015年第5期29卷 04014059-04014059页

作者： Wu, Zezhou Fan, Hongqin Liu, Guiwen Hong Kong Polytech Univ Dept Bldg & Real Estate Kowloon Hong Kong Peoples R China Chongqing Univ Sch Construct Management & Real Estate Chongqing 400045 Peoples R China

Accurate forecasting of construction and demolition waste (CDW) generation could provide valuable information for the planning, design, and management of CDW at municipal levels. However, the lack of reliable forecasting approaches and historical records makes it difficult to predict the amount of CDW for a long-or short-term plan. To effectively tackle the CDW forecasting problem, a novel computer-based prediction model, gene expression programming (GEP), is introduced and tested. With the CDW and other data on predictor variables from the last two decades, the amount of CDW is forecasted in this study. Results and findings obtained from this research show that GEP is an effective model for predicting waste generation, with lower average forecasting error than the multiple linear model and the artificial neural network. Research issues related to model selection, training, and validation are also discussed in the paper. (C) 2014 American Society of Civil Engineers.

关键词： Construction and demolition waste gene expression programming Time series analysis Forecasting

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DYNAMIC PERFORMANCE EVALUATION OF COMPUTING SYSTEM BASED ON gene expression programming THEORY

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2015年第5期11卷 1725-1738页

作者： You, Jing Qin, Wei Feng, Hui Huang, Rong Shangguan, Jinglun Changzhou Univ Fac Informat Sci & Engn Gehu Rd Wujin Dist 213164 Changzhou Peoples R China

Software aging, with the growing software complexity, which can cause the degradation of performance and even software failure, has never been able to be avoided. In order to monitor the software aging of server, some researchers proposed different modeling methods, such as linear regression and auto-regressive and moving average (ARMA) model, to analyze and predict the performance. However, these models cannot express the combined effects of a variety of resources on system performance. In this paper, a dynamic modeling algorithm based on gene expression programming (GEP) theory is put forward. The GEP model can monitor, evaluate and predict the system performance by building the direct relationship between some performance index and multiple computing resources including the utilization of CPU and memory, I/O performance, network transmission, etc. In addition, the new GEP model can be adjusted to trace the performance trend and the running state of system by moving window strategy. Finally, three experiments are designed and the results demonstrate that the GEP model can express the overall trend with essential details, which will have higher precision and practicability than those of traditional models when the trend of data has complex changes, and the multi-stage GEP model can be established to analyze and predict the healthy state of system during the process of aging.

关键词： Performance evaluation gene expression programming Dynamic monitoring Software aging Performance model

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Ground motion prediction equations (GMPEs) for elastic response spectra in the Iranian plateau using gene expression programming (GEP)

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2014年第6期26卷 2825-2839页

作者： Amiri, Gholamreza Ghodrati Amiri, Mohamad Shamekhi Tabrizian, Zahra Iran Univ Sci & Technol Sch Civil Engn Ctr Excellence Fundamental Studies Struct Eng Tehran Iran Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Babol Noshirvani Univ Technol Coll Civil Engn Babol Sar Iran

This paper proposes ground-motion prediction equations(GMPEs) for the horizontal component of earthquake in Iranian plateau. These equations present the velocity and acceleration response spectra at 5% damping ratio as continuous period functions, within range of 0.1 to 4 seconds. So far many equations have been presented and the recent suggested proportions are functions of several parameters. In this research, due to easy usage and lack of information in Iran, only the magnitude of earthquake, the distance between earthquake source and the location and the ground type are used as important factors. Iranian plateau is divided into two zones: Alborz-Central Iran and Zagros, each of which is divided into rock and soil region according to the ground type. Regarding the fact that the occurred and reported earthquakes in Iran are shallow, surface wave magnitude (Ms) is used in this study. Moreover, hypocentral distance is considered as distance between the earthquake source and the location. To obtain the velocity and acceleration response spectra, a gene expression programming(GEP) algorithm is used which utilizes no constant regression model and the model is acquired smartly as a continuous period function. The consequences show a consistency with high proportionality coefficient among the observed and anticipated results.

关键词： Ground-motion prediction equation gene expression programming Iran rock soil

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Research on the Target Feature Recognition and Simulation Based on gene expression programming

Research on the Target Feature Recognition and Simulation Ba...

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作者： Xuejun Zhao Chuangming Tong Xiujiu Yuan Jianwei Zhang The Air Force Engineering University The Second Artillery Equipment Academe

In this paper,We studied the gene expression programming, on the analysis of the structure of gene expression trees, finally, the target feature recognition based on gene expression programming experiment and simulati... 详细信息

关键词： gene expression programming Target feature gene expression trees Nonlinear systems

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