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Surveying the mechanical response of non-persistent jointed slabs subjected to compressive axial loading utilising GEP approach

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INTERNATIONAL JOURNAL OF GEOTECHNICAL ENGINEERING 2021年第10期15卷 1312-1324页

作者： Asadizadeh, Mostafa Rezaei, Mohammad Hamedan Univ Technol Dept Min Engn Mardom St Hamadan 65155579 Hamadan Iran Univ Kurdistan Dept Min Engn Fac Engn Sanandaj Iran

Strength and deformational behaviour of non-persistent jointed rock mass are the most important issues in the field of rock mechanics. In this paper, many slab shaped rock-like blocks containing two pre-existing parallel rough non-persistent joints were subjected to the uniaxial compressive load in order to investigate the effect of joint roughness coefficient, length of bridge, angle of bridge and inclination of joint on its mechanical responses through physical tests. Based on the test results, two models i.e. gene expression programming and linear regression analysis have been developed, and some statistical indexes were adopted to evaluate their performance. The results revealed that the gene expression programming is far superior to linear regression method, and its results are in agreement with the lab tests outcomes;moreover, parametric study of the suggested models indicated that joint inclination and joint roughness coefficient have the highest and lowest effective on the tests outcomes respectively.

关键词： Rock mass non-persistent joint mechanical behaviour gene expression programming linear regression analysis

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Hybrid Methods of GMDH-Neural Networks Synthesis and Training for Solving Problems of Time Series Forecasting 15th

Hybrid Methods of GMDH-Neural Networks Synthesis and Trainin...

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15th International Scientific Conference on Intellectual Systems of Decision Making and Problems of Computational Intelligence (ISDMCI)

作者： Lytvynenko, Volodymyr Wojcik, Waldemar Fefelov, Andrey Lurie, Iryna Savina, Nataliia Voronenko, Mariia Boskin, Oleg Smailova, Saule Kherson Natl Tech Univ Kherson Ukraine Lublin Univ Technol Lublin Poland Natl Univ Water & Environm Engn Rivne Ukraine D Serikbayev East Kazakhstan State Tech Univ Ust Kamenogorsk Kazakhstan

ISBN: (纸本)9783030264741

In this paper, for solving the problem of forecasting non-stationary time series, hybrid learning methods for GMDH-neural networks are proposed. Training methods combine artificial immune systems with members of the evolutionary algorithm family, in particular, gene expression programming systems. The following hybrid computational methods for the synthesis and training of GMDH-neural networks have been developed: a method in which candidate models are represented as gene expression, and training is performed by clonal selection;the method of two-phase structural-parametric synthesis, in which the structural component is formed by programming the expression of genes, and the parameterization is performed by clonal selection;a method based on cooperative-competitive processes of interaction of the elements of the immune system, in which the structure and parameters of the GMDH-neural network are represented by the entire population element-wise. Comparative experimental studies of the quality of the proposed computational methods for solving forecasting problems were carried out.

关键词： Group method of data handling Clonal selection method gene expression programming

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Trading Decision of Taiwan Stocks with the Help of United States Stock Market 22

Trading Decision of Taiwan Stocks with the Help of United St...

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22nd International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES)

作者： Huang, Shih-Chan Yang, Chang-Biau Chen, Hung-Hsin Natl Sun Yat Sen Univ Dept Comp Sci & Engn 70 Lienhai Rd Kaohsiung 80424 Taiwan

The paper studies how to improve the trading decision of Taiwan stocks with the information of US stock market. Our method first aligns the trading days between Taiwan and US stock markets. Next, the similarity between the portfolio index (PI, constructed from 100 Taiwan stocks) and one of the US stock indices, the Dow Jones Industrial Average (DJIA), NASDAQ composite index (NASDAQ), or Standard & Poor's 500 (S&P 500), is computed, respectively. The trading signals of PI or each US stock index are generated by the method of Lee et al. Finally, the consensus signals of PI are determined by the majority vote scheme with the weighted functions, calculated from the similarity. The testing period of PI starts from 2000/1/4 to 2017/12/29, totally 4480 days. As the experimental results show, the index combination (PI, DJIA, NASDAQ) with the weighted function W(4) is considered to be the best combination for trading PI. Its average annualized return (cumulative return) achieves 15.03% (1170.42%), which is better than the method of Lee et al. 13.88% (947.65%), and the buy-and-hold strategy 9.85% (442.90%). (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： stock investment Taiwan stock US stock indices trading signal gene expression programming

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Two Ensemble Classifiers Constructed from GEP-Induced expression Trees

Two Ensemble Classifiers Constructed from GEP-Induced Expres...

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4th KES International Symposium on Agent and Multi-Agent Systems

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Informat Wita Stwosza 57 PL-80952 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

ISBN: (纸本)9783642135408

In this paper we propose two ensemble classifiers using expression trees as weak classifiers. The first ensemble uses the AdaBoost approach and the second makes use of Dempster'aAZs rule of combination and applies triplet mass functions to combine classifiers. The performance of both ensemble classifiers is evaluated experimentally. The experiment involved 9 well known datasets from the UCI Irvine Machine Learning Repository. Experiment results show that using GEP-induced expression trees allows to construct high quality ensemble classifiers.

关键词： gene expression programming AdaBoost evidentional reasoning

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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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Cellular GEP-Induced Classifiers

Cellular GEP-Induced Classifiers

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2nd International Conference on Computational Collective Intelligence: Technologies and Applications

作者： Jedrzejowicz, Joanna Jedrzejowicz, Piotr Univ Gdansk Inst Informat Wita Stwosza 57 PL-80952 Gdansk Poland Gdynia Maritime Univ Dept Informat Syst Morska Poland

ISBN: (纸本)9783642166921

In this paper we propose integrating two collective computational intelligence techniques gene expression programming and cellular evolutionary algorithms with a view to induce expression trees, which, subsequently, serve as weak classifiers. From these classifiers stronger ensemble classifiers are constructed using majority-voting and boosting techniques. The paper includes the discussion of the validating experiment result confirming high quality of the proposed ensemble classifiers.

关键词： gene expression programming cellular evolutionary algorithm ensemble classifiers

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Estimating Cement Compressive Strength from Microstructural Images using GEP with Probabilistic Polarized Similarity Weight Tournament Selection 18

Estimating Cement Compressive Strength from Microstructural ...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Zhang, Liangliang Yue, Xinya Wang, Lin Yang, Bo Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Linyi Univ Sch Informat Linyi 276000 Shandong Peoples R China

ISBN: (纸本)9781450356183

The safety of building facilities is directly affected by the physical properties of cement, among which cement compressive strength plays the most important role in evaluating them. Therefore, the investigation of cement compressive strength is helpful in improving the cement properties. Traditionally, chemical composition, curing condition, and water-cement ratio are used to estimate the strength of cement paste. However, this approach is limited by the extreme complexity of physical changes and chemical reactions during cement hydration. Considering the cement microstructure contains information related to strength microscopically, microtomography, which can image three-dimensional microstructure, provides scientists with another way to study cement compressive strength nondestructively. This study estimates cement compressive strength using microstructure features extracted from microtomography images and gene expression programming. A probabilistic polarized similarity weight tournament selection operator is also proposed to balance the exploration and exploitation. Experimental results corroborate that the obtained relationship possesses higher estimation accuracy, good interpretability and the evolutionary capability performs well.

关键词： Cement compressive strength gene expression programming microtomography images

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A Model of a GEP-Based Text Clustering on Counter Propagation Networks

A Model of a GEP-Based Text Clustering on Counter Propagatio...

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3rd International Conference on Artificial Intelligence and Computational Intelligence (AICI 2011)

作者： Luo, Jin'guang Yuan, Chang'an Luo, Jinkun Guangxi Teachers Educ Univ Sch Comp & Informat Engn Nanning 530023 Peoples R China

ISBN: (纸本)9783642242816

In this paper, we present a model of a GEP-based test clustering research on counter propagation networks. The idea of the model is that it optimize the link weight vector by using the advantage of the GEP(gene expression programming, GEP) in combinatorial optimization. We investigate how the value of weight in the network affect the performance of the text clustering by comparing it to a based on genetic algorithm and SOM network and the method of the traditional CPN(Counter Propagation Networks, CPN). Furthermore, we improve and optimize the weight in the CPN network by the method of GEP, thus raise the quality of the text clustering in the network. Finally, in this paper, we demonstrated the validity and superiority of the presented model.

关键词： Text Clustering Weight Optimization Neural Network gene expression programming Counter Propagation Networks

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Dictionary Method for Multiple Soft and Catastrophic Fault Diagnosis Based on Evolutionary Computation

Dictionary Method for Multiple Soft and Catastrophic Fault D...

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International Conference on Signals and Electronic Systems (ICSES 2008)

作者： Korzybski, Marek Tech Univ Lodz Fac Elect Elect Comp & Control Engn PL-90924 Lodz Poland

ISBN: (纸本)9788388309472

In this paper a new method for detection, location and identification of multiple soft and catastrophic faults in linear and nonlinear analog circuits is described. It is based on the node approach and two evolutionary methods: gene expression programming (GEP) and genetic algorithm (GA). On the before test stage the algorithm needs repeated analyses of circuit-under-test (CUT) with different values of parameters which can be faulty. The results form the training set for constructing a dictionary. It consists of all sets created with groups of possible faulty parameters, associated with them formulas for computing these parameters, the lower and the upper limits of node voltages of the circuit with faulty parameters. These formulas are determined using GEP, one of the new evolutionary methods. On the after test stage the method obtains the set of possible faulty elements and calculates the values of possible faulty elements. The accuracy of the fault identification of this results is usually unsatisfactory. The requested precision of fault detection can be with the already known method for soft fault diagnosis accomplished. The time consumed for obtaining formulas enabling us to calculate faulty parameters increases with the number of possible faulty elements and the number of simultaneously faulty elements. The results of the method depend on parameters of GEP process.

关键词： electric circuit diagnosis parametric faults catastrophic faults multiple faults evolutionary methods gene expression programming genetic algorithms

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The Classifier for Prediction of Peri-operative Complications in Cervical Cancer Treatment

The Classifier for Prediction of Peri-operative Complication...

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13th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Kluska, Jacek Kusy, Maciej Obrzut, Bogdan Rzeszow Univ Technol Fac Elect & Comp Engn Powstancow Warszawy 12 PL-35959 Rzeszow Poland Rzeszow Univ Technol Fac Med Warsaw Poland

ISBN: (纸本)9783319071756;9783319071763

This paper addresses the problem of creating a new classifier as highly interpretable fuzzy rule-based system, based on the analytical theory of fuzzy modeling and gene expression programming. This approach is applied to solve the prediction problem of peri-operative complications of radical hysterectomy in patients with cervical cancer. The developed classifier has the form of the set of fuzzy metarules, which are readable for the medical community, and additionally, is accurate enough. The consequents of the metarules describe the presence or absence of peri-operative complications. For the construction of the classifier we can use the fuzzified, binarized or both types of the attributes. We also compare the efficiency of our model with the decision trees and C5 algorithm.

关键词： Takagi-Sugeno system gene expression programming cervical cancer complications prediction

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