gene expression programming (GEP) is a genotype/phenotype system that evolves computer programs of different sizes and shapes encoded in linear chromosomes of fixed length. However, the performance of basic GEP is hig...
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ISBN:
(纸本)078039335X
gene expression programming (GEP) is a genotype/phenotype system that evolves computer programs of different sizes and shapes encoded in linear chromosomes of fixed length. However, the performance of basic GEP is highly dependent on the genetic operators' rate. In this work, we present a new algorithm called GEPSA that combines GEP and Simulated Annealing (SA), and GEPSA decreases the dependence on genetic operators' rate without impairing the performance of GEP. Three function finding problems, including a benchmark problem of prediction sunspots, are tested on GEPSA, results shows that importing Simulated Annealing can improve the performance of GEP.
In this paper we shown the applying of gene expression programming algorithm to correction modelling of non-linear dynamic objects. The correction modelling is the non-linear modelling method based on equivalent linea...
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ISBN:
(纸本)9783319285559;9783319285535
In this paper we shown the applying of gene expression programming algorithm to correction modelling of non-linear dynamic objects. The correction modelling is the non-linear modelling method based on equivalent linearization technique that allows to incorporate in modelling process the known linear model of the same or similar object or phenomenon. The usefulness of the proposed method will be shown on a practical example of the continuous stirred tank reactor modelling.
To improve model accuracy,tabu search is introduced to gene expression programming (GEP) and impoves GEP's local search ability, gene expression programming Based on Parallel Tabu Search (PTS-GEP) is proposed. In ...
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ISBN:
(纸本)9783037858646
To improve model accuracy,tabu search is introduced to gene expression programming (GEP) and impoves GEP's local search ability, gene expression programming Based on Parallel Tabu Search (PTS-GEP) is proposed. In PTS-GEP, the research conducts experiment over the data from previously reported research and compares the result to two other algorithms namely simple GEP, UC-GEP. The results demonstrate the optimal performance of PTS-GEP in model accuracy.
Aiming at the problem that the current estimation methods of rock shear strength parameters cannot reflect and quantify their uncertainty, an estimation method based on gene expression programming (GEP) and Bayesian i...
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Aiming at the problem that the current estimation methods of rock shear strength parameters cannot reflect and quantify their uncertainty, an estimation method based on gene expression programming (GEP) and Bayesian inference (BI) is proposed to realize uncertainty prediction in the sense of probability. Based on the rock strength parameter dataset, GEP was used to establish the mapping relationship between rock shear strength parameters and uniaxial compressive strength (UCS), and tensile strength (UTS). Then, according to the function expression, prior information, and the likelihood function, the joint posterior probability distribution of the rock shear strength parameters was obtained by BI. The results showed that the GEP-BI method can effectively predict the shear strength parameters under the given UCS and UTS of rock experimental data, and can also give the probability distribution of the predicted results, which has strong interpretability and uncertainty analysis ability. According to the uncertainty degree and prediction effectiveness of the results, it was suggested to adopt the lognormal distribution as the likelihood function, which can effectively avoid the meaningless negative value. Compared with machine learning methods, the GEP-BI model has a better prediction effect, which proves the feasibility and effectiveness of the GEP-BI method.
As an important part of the Internet of Energy, a complex access environment, flexible access modes and a massive number of access terminals, dynamic, and distributed mass data in an active distribution network will b...
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As an important part of the Internet of Energy, a complex access environment, flexible access modes and a massive number of access terminals, dynamic, and distributed mass data in an active distribution network will bring new challenges to the security of data transmission. To address the emerging challenge of this active distribution network, first we propose a content filtering function mining algorithm based on simulated annealing and gene expression programming (CFFM-SAGEP). In CFFM-SAGEP, genetic operation based on simulated annealing and dynamic population generation based on an adaptive coefficient are applied to improve the convergence speed and precision, the recall and the F beta measure value of the content filtering. Finally, based on CFFM-SAGEP, we present a distributed mining for content filtering function based on simulated annealing and gene expression programming (DMCF-SAGEP) to improve efficiency of content filtering. In DMCF-SAGEP, a local function merging strategy based on the minimum residual sum of squares is designed to obtain a global content filtering model. The results using three data sets demonstrate that compared with traditional algorithms, the algorithms proposed demonstrate strong content filtering performance.
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 a...
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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.
Many approaches to AI in robotics use a multi-layered approach to determine levels of behaviour from basic operations to goal-directed behaviour, the most well-known of which is the subsumption architecture. In this p...
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Many approaches to AI in robotics use a multi-layered approach to determine levels of behaviour from basic operations to goal-directed behaviour, the most well-known of which is the subsumption architecture. In this paper, the performances of the unigenic gene expression programming (ugGEP) and multigenic GEP (mgGEP) in evolving robot controllers for a wall following robot are analysed. Additionally, the paper introduces Regulatory Multigenic gene expression programming, a new evolutionary technique that can be utilised to automatically evolve modularity in robot behaviour. The proposed technique extends the mgGEP algorithm, by incorporating a regulatory gene as part of the GEP chromosome. The regulatory gene, just as in systems biology, determines which of the genes in the chromosome to express and therefore how the controller solves the problem. In the initial experiments, the proposed algorithm is implemented for a robot wall following problem and the results compared to that of ugGEP and mgGEP. In addition to the wall following behaviour, a robot foraging behaviour is implemented with the aim of investigating whether the position of a specific module (sub-expression tree) in the overall expression tree is of importance when coding for a problem.
Accurate gas viscosity determination is an important issue in the oil and gas *** approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT)....
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Accurate gas viscosity determination is an important issue in the oil and gas *** approaches for gas viscosity measurement are timeconsuming,expensive and hardly possible at high pressures and high temperatures(HPHT).In this study,a number of correlations were developed to estimate gas viscosity by the use of group method of data handling(GMDH)type neural network and gene expression programming(GEP)techniques using a large data set containing more than 3000 experimental data points for methane,nitrogen,and hydrocarbon gas *** is worth mentioning that unlike many of viscosity correlations,the proposed ones in this study could compute gas viscosity at pressures ranging between 34 and 172 MPa and temperatures between 310 and 1300 ***,a comparison was performed between the results of these established models and the results of ten wellknown models reported in the *** absolute relative errors of GMDH models were obtained 4.23%,0.64%,and 0.61%for hydrocarbon gas mixtures,methane,and nitrogen,*** addition,graphical analyses indicate that the GMDH can predict gas viscosity with higher accuracy than GEP at HPHT ***,using leverage technique,valid,suspected and outlier data points were ***,trends of gas viscosity models at different conditions were evaluated.
Data clustering is a necessary process in many scientific disciplines, and fuzzy c-means (FCM) is one of the most popular clustering algorithms. Recently, distributing weight values and avoiding local minimization are...
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Data clustering is a necessary process in many scientific disciplines, and fuzzy c-means (FCM) is one of the most popular clustering algorithms. Recently, distributing weight values and avoiding local minimization are the possible ways to improve the results of FCM. In this paper, fuzzy C-means clustering based on weights and gene expression programming (WGFCM) is proposed to improve the performance of FCM. A new weight vectors calculation based on entropy is introduced to measure distance accurately. Moreover, gene expression programming (GEP) is employed to determine the appropriate cluster centers. Experiments are conducted with ten UCI data sets to compare the proposed method with FCM. In addition, WGFCM is compared with other FCM based methods and different clustering approaches published for a fair assessment. The results show that the proposed method is far superior to FCM-based methods in terms of purity, Rand Index, accuracy rate, objective function value and iterative cost. Moreover, it has an advantage over other clustering approaches in terms of the accuracy. (C) 2017 Elsevier B.V. All rights reserved.
Due to the effects of anthropogenic activities and natural climate change, streamflows of rivers have gradually decreased. In order to maintain reliable water supplies, reservoir operation and water resource managemen...
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Due to the effects of anthropogenic activities and natural climate change, streamflows of rivers have gradually decreased. In order to maintain reliable water supplies, reservoir operation and water resource management, accurate streamflow forecasts are very important. Based on monthly flow data from five hydrological stations in the middle and lower parts of the Hanjiang River Basin, between 1989 and 2009, we consider an efficient approach of adopting the gene expression programming model based on wavelet decomposition and de-noising (WDDGEP) to forecast river flow. Original flow time series data are initially decomposed into one sub-signal approximation and seven sub-signal details using the dmey wavelet. A wavelet threshold de-noising method is also applied in this study. Data that have been de-noised after decomposition are then adopted as inputs for WDDGEP models. Finally, the forecasted sub-signal results are summed to formulate an ensemble forecast for the original monthly flow series. A comparison of the prediction accuracy between the two models is based on three performance evaluation measures. Results show that the new WDDGEP models can effectively enhance accuracy in forecasting streamflow, and the proposed wavelet-based de-noising of the observed non-stationary time series is an effective measure to improve simulation accuracy.
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