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1

Chen, Long Bin, and Pei He. "Multi-Subexpression Programming." Applied Mechanics and Materials 411-414 (September 2013): 2067–73. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2067.

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Gene Expression Programming is a new and adaptive brand evolution algorithm which is developed on the basis of genetic algorithm. In recent years, Multi-Expression Programming which is proposed in the genetic programming is a linear structure coding scheme,its main feature is a chromosome contains multiple expressions. The idea of MEP is introduced into the GEP in this paper, so a single GEP gene contains multiple solutions to solve the problem.The new algorithm analyzes each gene in the GEP to extract relational subexpressions, then fitness evaluate certain subexpressions to choose the best f
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2

Zhang, Qingke, Bo Yang, Lin Wang, and Jianzhang Jiang. "An improved multi-expression programming algorithm applied in function discovery and data prediction." International Journal of Information and Communication Technology 5, no. 3/4 (2013): 218. http://dx.doi.org/10.1504/ijict.2013.054952.

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3

Wang, Yu. "Study on Improved Flexible Neural Tree Optimization Algorithm." Advanced Materials Research 765-767 (September 2013): 1055–59. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.1055.

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The BP neural network is easy to fall into local minimum point, the algorithm convergence speed slow, this paper puts forward an improved algorithm of flexible neural tree, introduced the basic theory knowledge of Flexible neural tree , analyzes the characteristics and advantages of the neural tree. The structure optimization and parameter optimization are adopted some optimization algorithm, Introduced the multi expression programming algorithm for optimization of flexible neural tree structure and by using the improved particle swarm algorithm to optimize the parameters of flexible neural tr
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4

Farajpanah, Hiwa, Morteza Lotfirad, Arash Adib, et al. "Ranking of hybrid wavelet-AI models by TOPSIS method for estimation of daily flow discharge." Water Supply 20, no. 8 (2020): 3156–71. http://dx.doi.org/10.2166/ws.2020.211.

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Abstract This research uses the multi-layer perceptron–artificial neural network (MLP-ANN), radial basis function–ANN (RBF-ANN), least square support vector machine (LSSVM), adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5T), gene expression programming (GEP), genetic programming (GP) and Bayesian network (BN) with five types of mother wavelet functions (MWFs: coif4, db10, dmey, fk6 and sym7) and selects the best model by the TOPSIS method. The case study is the Navrood watershed in the north of Iran and the considered parameters are daily flow discharge, temperature and precip
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5

Giustolisi, O., and D. A. Savic. "Advances in data-driven analyses and modelling using EPR-MOGA." Journal of Hydroinformatics 11, no. 3-4 (2009): 225–36. http://dx.doi.org/10.2166/hydro.2009.017.

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Evolutionary Polynomial Regression (EPR) is a recently developed hybrid regression method that combines the best features of conventional numerical regression techniques with the genetic programming/symbolic regression technique. The original version of EPR works with formulae based on true or pseudo-polynomial expressions using a single-objective genetic algorithm. Therefore, to obtain a set of formulae with a variable number of pseudo-polynomial coefficients, the sequential search is performed in the formulae space. This article presents an improved EPR strategy that uses a multi-objective g
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6

Chandra Sen. "Improved Weighted Sum Multi Objective Programming." January 2021 7, no. 01 (2021): 111–13. http://dx.doi.org/10.46501/ijmtst070126.

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Optimizing the combined objective function is the most common approach of Multi Objective Programming (MOP). The scalaization of objective functions have been recommended for combining the objective functions of various dimensions. The Weighted Sum Multi Objective Programming (WSMOP) has also been used for obtaining desirable solution. Several WSMOP methods are used for solving multi objective problems. Few weaknesses have noticed in WSMOP methods. An improved method of WSMOP is proposed in the present study for obtaining desirable solution of multi objective programming problems.
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7

Sharifi, Sohrab, Saeed Abrishami, and Amir H. Gandomi. "Consolidation assessment using Multi Expression Programming." Applied Soft Computing 86 (January 2020): 105842. http://dx.doi.org/10.1016/j.asoc.2019.105842.

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8

Al-Saati, Najla, and Taghreed Alreffaee. "Software Effort Estimation Using Multi Expression Programming." AL-Rafidain Journal of Computer Sciences and Mathematics 11, no. 2 (2014): 53–71. http://dx.doi.org/10.33899/csmj.2014.163756.

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9

Sheng, Zi Qiang. "Design Multi-Stage Draw by Improved Dynamic Programming." SAE International Journal of Materials and Manufacturing 3, no. 1 (2010): 716–22. http://dx.doi.org/10.4271/2010-01-0987.

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10

Ozlen, Melih, Benjamin A. Burton, and Cameron A. G. MacRae. "Multi-Objective Integer Programming: An Improved Recursive Algorithm." Journal of Optimization Theory and Applications 160, no. 2 (2013): 470–82. http://dx.doi.org/10.1007/s10957-013-0364-y.

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11

Romero López, Oscar Javier. "An evolutionary behavioral model for decision making." Adaptive Behavior 19, no. 6 (2011): 451–75. http://dx.doi.org/10.1177/1059712311419680.

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For autonomous agents the problem of deciding what to do next becomes increasingly complex when acting in unpredictable and dynamic environments while pursuing multiple and possibly conflicting goals. One of the most relevant behavior-based models that tries to deal with this problem is the behavior network model proposed by Maes. This model proposes a set of behaviors as purposive perception–action units that are linked in a nonhierarchical network, and whose behavior selection process is orchestrated by spreading activation dynamics. In spite of being an adaptive model (in the sense of self-
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12

Dioşan, Laura, and Anca Andreica. "Multi-objective breast cancer classification by using multi-expression programming." Applied Intelligence 43, no. 3 (2015): 499–511. http://dx.doi.org/10.1007/s10489-015-0668-8.

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13

Li, Qu, Hongbing Cheng, and Min Yao. "Adaptive Multi-phenotype Based Gene Expression Programming Algorithm." Chinese Journal of Electronics 25, no. 5 (2016): 807–16. http://dx.doi.org/10.1049/cje.2016.08.041.

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14

Feller, Christian, Tor Arne Johansen, and Sorin Olaru. "An improved algorithm for combinatorial multi-parametric quadratic programming." Automatica 49, no. 5 (2013): 1370–76. http://dx.doi.org/10.1016/j.automatica.2013.02.022.

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15

NI, Sheng-qiao, Chang-jie TANG, Ning YANG, and Jie ZUO. "Gene expression programming algorithm based on multi-threading evaluator." Journal of Computer Applications 32, no. 4 (2013): 986–89. http://dx.doi.org/10.3724/sp.j.1087.2012.00986.

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16

Baykasoğlu, Adil, and Lale Özbakir. "MEPAR-miner: Multi-expression programming for classification rule mining." European Journal of Operational Research 183, no. 2 (2007): 767–84. http://dx.doi.org/10.1016/j.ejor.2006.10.015.

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17

Lam, Kim Fung, and Jane W. Moy. "Improved Linear Programming Formulations for the Multi-Group Discriminant Problem." Journal of the Operational Research Society 47, no. 12 (1996): 1526. http://dx.doi.org/10.2307/3010610.

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18

An, Siguang, Shiyou Yang, Yanan Bai, and Xiushan Wu. "An improved physical programming method for multi-objective inverse problems." International Journal of Applied Electromagnetics and Mechanics 52, no. 3-4 (2016): 1151–59. http://dx.doi.org/10.3233/jae-162123.

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19

Fung Lam, Kim, and Jane W. Moy. "Improved Linear Programming Formulations for the Multi-group Discriminant Problem." Journal of the Operational Research Society 47, no. 12 (1996): 1526–29. http://dx.doi.org/10.1057/jors.1996.196.

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20

Lam, Kim Fung, and Jane W. Moy. "Improved Linear Programming Formulations for the Multi-group Discriminant Problem." Journal of the Operational Research Society 47, no. 12 (1996): 1526–29. http://dx.doi.org/10.1057/palgrave.jors.0471210.

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21

Peng, YuZhong, ChangAn Yuan, Xiao Qin, JiangTao Huang, and YaBing Shi. "An improved Gene Expression Programming approach for symbolic regression problems." Neurocomputing 137 (August 2014): 293–301. http://dx.doi.org/10.1016/j.neucom.2013.05.062.

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22

Zhang, Yong Bin, Tai Yong Wang, Yan Yu Ding, Teng Yue Shen, Jia Jia Dong, and Shan Gao. "Parametric Programming of Multi-Type Holes Using the Improved Genetic Algorithm." Key Engineering Materials 764 (February 2018): 333–41. http://dx.doi.org/10.4028/www.scientific.net/kem.764.333.

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For path planning and program generation of multi-type holes, this paper presents a method of the path planning, by using the improved genetic algorithm based on chaos algorithm, and develops an interactive programming system for multi-type holes. Users can choose regular holes, and input the parameters and the necessary information, according to their needs. Then the system will combine holes automatically, and calculate the position coordinates of each hole. After that the path planning is carried out by using the improved genetic algorithm. Finally, the system carries out the simulation exp
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23

Jadidi, O., S. Cavalieri, and S. Zolfaghari. "An improved multi-choice goal programming approach for supplier selection problems." Applied Mathematical Modelling 39, no. 14 (2015): 4213–22. http://dx.doi.org/10.1016/j.apm.2014.12.022.

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24

Ma, Xin, and Zhi-bin Liu. "Predicting the Oil Well Production Based on Multi Expression Programming." Open Petroleum Engineering Journal 9, no. 1 (2016): 21–32. http://dx.doi.org/10.2174/1874834101609010021.

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Predicting the oil well production is very important and also quite a complex mission for the petroleum engineering. Due to its complexity, the previous empirical methods could not perform well for different kind of wells, and intelligent methods are applied to solve this problem. In this paper the multi expression programming (MEP) method has been employed to build the prediction model for oil well production, combined with the phase space reconstruction technique. The MEP has shown a better performance than the back propagation networks, gene expression programming method and the Arps declin
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25

Li, Yan Mei, and Jing Min Wang. "The Short-Term Load Forecasting Model Based on Improved Gene Expression Programming Arithmetic." Advanced Materials Research 219-220 (March 2011): 1395–98. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.1395.

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Applying the improved gene expression programming arithmetic to optimization plan,the convergence rate and precision of the model can be improved,which can be used to load forecasting. Preprocessing the load sample data,and applying the flexible skills of the improved gene expression programming arithmetic ,the paper forecasts the whole point load of future short-term to see the same point load sequence of different work-day as sample.Through a case analysis,the improved gene expression programming arithmetic has been proved to have more efficiency and faster convergence rate than optimization
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26

Zeremdini, Jihen, Mohamed Anouar Ben Messaoud, and Aicha Bouzid. "Multi-pitch estimation based on multi-scale product analysis, improved comb filter and dynamic programming." International Journal of Speech Technology 20, no. 2 (2017): 225–37. http://dx.doi.org/10.1007/s10772-017-9398-0.

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27

Zhang, Yong Qiang, and Jing Xiao. "Population Diversity Strategy in Gene Expression Programming." Advanced Materials Research 204-210 (February 2011): 288–92. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.288.

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Population diversity is one of the most important factors that influence the convergence speed and evolution efficiency of gene expression programming (GEP) algorithm. In this paper, the population diversity strategy of GEP (GEP-PDS) is presented, inheriting the advantage of superior population producing strategy and various population strategy, to increase population average fitness and decrease generations, to make the population maintain diversification throughout the evolutionary process and avoid “premature” to ensure the convergence ability and evolution efficiency. The simulation experi
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28

Yang, Lei, Kangshun Li, Wensheng Zhang, and Yaolang Kong. "Short-term vegetable prices forecast based on improved gene expression programming." International Journal of High Performance Computing and Networking 11, no. 3 (2018): 199. http://dx.doi.org/10.1504/ijhpcn.2018.091891.

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29

Kong, Yaolang, Kangshun Li, Wensheng Zhang, and Lei Yang. "Short-term vegetable prices forecast based on improved gene expression programming." International Journal of High Performance Computing and Networking 11, no. 3 (2018): 199. http://dx.doi.org/10.1504/ijhpcn.2018.10012996.

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30

Al-Rabeeah, Masar, Ali Al-Hasani, Santosh Kumar, and Andrew Eberhard. "Enhancement of the Improved Recursive Method for Multi-objective Integer Programming Problem." Journal of Physics: Conference Series 1490 (March 2020): 012061. http://dx.doi.org/10.1088/1742-6596/1490/1/012061.

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31

Che, Ada, Weidong Lei, Jianguang Feng, and Chengbin Chu. "An Improved Mixed Integer Programming Approach for Multi-Hoist Cyclic Scheduling Problem." IEEE Transactions on Automation Science and Engineering 11, no. 1 (2014): 302–9. http://dx.doi.org/10.1109/tase.2013.2254713.

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32

Kumar, Soumojit, and Ashis Kumar Chatterjee. "An improved mathematical programming formulation and application of multi-attribute choice behaviour." International Journal of Operational Research 25, no. 1 (2016): 100. http://dx.doi.org/10.1504/ijor.2016.073253.

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33

Mehr, Ali Danandeh, and Amir H. Gandomi. "MSGP-LASSO: An improved multi-stage genetic programming model for streamflow prediction." Information Sciences 561 (June 2021): 181–95. http://dx.doi.org/10.1016/j.ins.2021.02.011.

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34

Alavi, Amir Hossein, Amir Hossein Gandomi, Mohammad Ghasem Sahab, and Mostafa Gandomi. "Multi expression programming: a new approach to formulation of soil classification." Engineering with Computers 26, no. 2 (2009): 111–18. http://dx.doi.org/10.1007/s00366-009-0140-7.

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35

Wang, Han-Lin, and Zhen-Yu Yin. "High performance prediction of soil compaction parameters using multi expression programming." Engineering Geology 276 (October 2020): 105758. http://dx.doi.org/10.1016/j.enggeo.2020.105758.

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36

Gandomi, Amir Hossein, Amir Hossein Alavi, and Gun Jin Yun. "Formulation of uplift capacity of suction caissons using multi expression programming." KSCE Journal of Civil Engineering 15, no. 2 (2011): 363–73. http://dx.doi.org/10.1007/s12205-011-1117-9.

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37

Johnson, James E., Praveen Kumar, Caleb Easterly, et al. "Improve your Galaxy text life: The Query Tabular Tool." F1000Research 7 (October 5, 2018): 1604. http://dx.doi.org/10.12688/f1000research.16450.1.

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Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different ‘omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps a
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38

Johnson, James E., Praveen Kumar, Caleb Easterly, et al. "Improve your Galaxy text life: The Query Tabular Tool." F1000Research 7 (January 9, 2019): 1604. http://dx.doi.org/10.12688/f1000research.16450.2.

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Galaxy provides an accessible platform where multi-step data analysis workflows integrating disparate software can be run, even by researchers with limited programming expertise. Applications of such sophisticated workflows are many, including those which integrate software from different ‘omic domains (e.g. genomics, proteomics, metabolomics). In these complex workflows, intermediate outputs are often generated as tabular text files, which must be transformed into customized formats which are compatible with the next software tools in the pipeline. Consequently, many text manipulation steps a
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39

Danandeh Mehr, Ali. "An improved gene expression programming model for streamflow forecasting in intermittent streams." Journal of Hydrology 563 (August 2018): 669–78. http://dx.doi.org/10.1016/j.jhydrol.2018.06.049.

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40

Hao, Zhai Liu, Zu Yuan Liu, and Bai Wei Feng. "Application of Multi-Objective Optimization Algorithm Based on Physical Programming in Ship Conceptual Design." Advanced Materials Research 904 (March 2014): 408–13. http://dx.doi.org/10.4028/www.scientific.net/amr.904.408.

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Ship optimization design is a typical multi-objective problem. The multi-objective optimization algorithm based on physical programming is able to obtain evenly distributed Pareto front. But the number of Pareto solutions and the search positions of pseudo-preference structures still exit some disadvantages that are improved in this paper. Firstly uniform design for mixture experiments is used to arbitrarily set the number of Pareto solutions and evenly distribute the search positions of pseudo-preference structures. Then the objective space is searched by shrinking of search domain and rotati
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41

Akbari, Amir, and Paul I. Barton. "An Improved Multi-parametric Programming Algorithm for Flux Balance Analysis of Metabolic Networks." Journal of Optimization Theory and Applications 178, no. 2 (2018): 502–37. http://dx.doi.org/10.1007/s10957-018-1281-x.

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42

Jia, Liping, Yuping Wang, and Lei Fan. "An improved uniform design-based genetic algorithm for multi-objective bilevel convex programming." International Journal of Computational Science and Engineering 12, no. 1 (2016): 38. http://dx.doi.org/10.1504/ijcse.2016.074562.

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43

Sun, Min, and Hongchun Sun. "Improved proximal ADMM with partially parallel splitting for multi-block separable convex programming." Journal of Applied Mathematics and Computing 58, no. 1-2 (2017): 151–81. http://dx.doi.org/10.1007/s12190-017-1138-8.

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44

Silva, Rodrigo F., Mohammad Masudur Rahman, Carlos Eduardo Dantas, Chanchal Roy, Foutse Khomh, and Marcelo A. Maia. "Improved retrieval of programming solutions with code examples using a multi-featured score." Journal of Systems and Software 181 (November 2021): 111063. http://dx.doi.org/10.1016/j.jss.2021.111063.

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45

Maity, Gurupada, and Sankar Kumar Roy. "Solving fuzzy transportation problem using multi-choice goal programming." Discrete Mathematics, Algorithms and Applications 09, no. 06 (2017): 1750076. http://dx.doi.org/10.1142/s1793830917500768.

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This paper explores the study of fuzzy transportation problem (FTP) using multi-choice goal-programming approach. Generally, the decision variable in transportation problem (TP) is considered as real variable, but here the decision variable in each node is chosen from a set of multi-choice fuzzy numbers. Here, we formulate a mathematical model of FTP considering fuzzy goal to the objective function. Thereafter, the solution procedure of the proposed model is developed through multi-choice goal programming approach. The proposed approach is not only improved the applicability of goal programmin
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46

Wang, Wei Hong, Yan Ye Du, and Qu Li. "Multi-Objective Optimization Algorithm Based on Gene Expression Programming and Virus Evolution." Advanced Materials Research 225-226 (April 2011): 372–76. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.372.

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Evolutionary Multi-objective Optimization (EMO) is a hot research direction nowadays and one of the state-of-the-art evolutionary multi-objective optimization algorithms ——NSGA-II has gain wide attention and application in many fields. Gene Expression Programming (GEP) has a powerful search capability, but falls into local optimum easily. Based on the transformed GEP, NSGA-II and the virus evolution mechanism, a new multi-objective evolutionary algorithm GEP Virus NSGA-II is proposed. With the infection operation of virus population, the diversity of the host population is increased, and it’s
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47

Hossein Alavi, Amir, Ali Mollahasani, Amir Hossein Gandomi, and Jafar Boluori Bazaz. "Formulation of secant and reloading soil deformation moduli using multi expression programming." Engineering Computations 29, no. 2 (2012): 173–97. http://dx.doi.org/10.1108/02644401211206043.

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48

Gandomi, Amir H., Ali Faramarzifar, Peyman Ghanad Rezaee, Abazar Asghari, and Siamak Talatahari. "NEW DESIGN EQUATIONS FOR ELASTIC MODULUS OF CONCRETE USING MULTI EXPRESSION PROGRAMMING." Journal of Civil Engineering and Management 21, no. 6 (2015): 761–74. http://dx.doi.org/10.3846/13923730.2014.893910.

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An innovative multi expression programming (MEP) approach is used to derive new predictive equations for tangent elastic modulus of normal strength concrete (NSC) and high strength concrete (HSC). Similar to several building codes, the modulus of elasticity of NSC and HSC is formulated in terms of concrete compressive strength. Furthermore, a generic model is developed for the estimation of the elastic modulus of both NSC and HSC. Comprehensive databases are gathered from the literature to develop the models. For more verification, a parametric analysis is carried out and discussed. The propos
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49

Chen, Xing, Wenhai Zhang, Yu Hou, and Lin Yang. "Improved stereo matching algorithm based on multi-scale fusion." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 4 (2021): 876–82. http://dx.doi.org/10.1051/jnwpu/20213940876.

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Aiming at the low matching accuracy of local stereo matching algorithm in weak texture or discontinuous disparity areas, a stereo matching algorithm combining multi-scale fusion of convolutional neural network (CNN) and feature pyramid structure (FPN) is proposed. The feature pyramid is applied on the basis of the convolutional neural network to realize the multi-scale feature extraction and fusion of the image, which improves the matching similarity of the image blocks. The guide graph filter is used to quickly and effectively complete the cost aggregation. The disparity selection stage adapt
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50

Li, Kangshun, Yan Chen, Wei Li, Jun He, and Yu Xue. "Improved gene expression programming to solve the inverse problem for ordinary differential equations." Swarm and Evolutionary Computation 38 (February 2018): 231–39. http://dx.doi.org/10.1016/j.swevo.2017.07.005.

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