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Journal articles on the topic 'Genetic Expression Programming'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

FERREIRA, CÂNDIDA. "GENETIC REPRESENTATION AND GENETIC NEUTRALITY IN GENE EXPRESSION PROGRAMMING." Advances in Complex Systems 05, no. 04 (December 2002): 389–408. http://dx.doi.org/10.1142/s0219525902000626.

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The neutral theory of molecular evolution states that the accumulation of neutral mutations in the genome is fundamental for evolution to occur. The genetic representation of gene expression programming, an artificial genotype/phenotype system, not only allows the existence of non-coding regions in the genome where neutral mutations can accumulate but also allows the controlled manipulation of both the number and the extent of these non-coding regions. Therefore, gene expression programming is an ideal artificial system where the neutral theory of evolution can be tested in order to gain some insights into the workings of artificial evolutionary systems. The results presented in this work show beyond any doubt that the existence of neutral regions in the genome is fundamental for evolution to occur efficiently.

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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 fitness as individuals fitness, and carry on related genetic manipulation. Finally, the improved algorithm experiment with GEP and MEP, compare their mining the same functions ability,record average fitness value and success rate. The experiment results show that the improved algorithm has better evolutionary efficiency.

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Hosseini, Maryamsadat, Rouzbeh Shad, and Samsung Lim. "Landslide Susceptibility Mapping using Genetic Expression Programming." IOP Conference Series: Earth and Environmental Science 767, no. 1 (May 1, 2021): 012042. http://dx.doi.org/10.1088/1755-1315/767/1/012042.

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ASLAN, Behzat, and Fevzi Önen. "APPLICATION OF GENETIC EXPRESSION PROGRAMMING IN URBAN DRINKING WATER." Middle East Journal of Technic 2, no. 2 (December 30, 2017): 143–55. http://dx.doi.org/10.23884/mejt.2017.2.2.01.

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OLTEAN, MIHAI, CRINA GROŞAN, LAURA DIOŞAN, and CRISTINA MIHĂILĂ. "GENETIC PROGRAMMING WITH LINEAR REPRESENTATION: A SURVEY." International Journal on Artificial Intelligence Tools 18, no. 02 (April 2009): 197–238. http://dx.doi.org/10.1142/s0218213009000111.

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Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given.

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Abraham, Ajith, and Crina Grosan. "Decision Support Systems Using Ensemble Genetic Programming." Journal of Information & Knowledge Management 05, no. 04 (December 2006): 303–13. http://dx.doi.org/10.1142/s0219649206001566.

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This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi-Expression Programming (MEP) and Gene Expression Programming (GEP). The clustered data are used as the inputs to the genetic programming algorithms. Some simulation results demonstrating the difference of these techniques are also performed. Test results reveal that the proposed ensemble method performed better than the individual GP approaches and that the method is efficient.

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CAZENAVE, TRISTAN. "MONTE-CARLO EXPRESSION DISCOVERY." International Journal on Artificial Intelligence Tools 22, no. 01 (February 2013): 1250035. http://dx.doi.org/10.1142/s0218213012500352.

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Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Programming evaluates and combines trees to discover expressions that maximize a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from expression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize.

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Faradonbeh, Roohollah Shirani, Danial Jahed Armaghani, Masoud Monjezi, and Edy Tonnizam Mohamad. "Genetic programming and gene expression programming for flyrock assessment due to mine blasting." International Journal of Rock Mechanics and Mining Sciences 88 (October 2016): 254–64. http://dx.doi.org/10.1016/j.ijrmms.2016.07.028.

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9

Gao, Xin Wen, Ben Bo Guan, and Xing Jian Guan. "Study on the Optimize Strategies of Gene Expression Programming." Applied Mechanics and Materials 432 (September 2013): 565–70. http://dx.doi.org/10.4028/www.scientific.net/amm.432.565.

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The purpose of this paper is to improve the efficiency of the Gene Expression Programming (GEP) algorithm. The GEP algorithm is an evolutionary computation. It inherits the characteristics of Genetic Algorithm and Genetic Programming. Through its own characteristics, the GEP algorithm can get the optimal solution of the complicated problem. So, the GEP algorithm has achieved good results in many areas. However, there are also some inevitable drawbacks about the GEP algorithm itself. This paper proposes 5 deficiencies aspects of the GEP algorithm (expression meaning, fitness calculation, local convergence, variable selection, genetic operations, selection of genetic operation rates), and gives the corresponding solutions.

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Sharifi, S., M. Sterling, and D. W. Knight. "Prediction of end-depth ratio in open channels using genetic programming." Journal of Hydroinformatics 13, no. 1 (March 18, 2010): 36–48. http://dx.doi.org/10.2166/hydro.2010.087.

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In this paper, genetic programming (GP) is used as an effective model induction tool to solve a classic problem in open channel flow: the free overfall. By applying GP to experimental data of circular channels with a flat bed and employing a model selection procedure, a reliable expression in the form of is found for calculating the critical depth (hc) and end-depth ratio (EDR). Further effort is made to verify the applicability and superiority of this expression for channels with other cross sections. This global expression not only outperforms other expressions in estimating the critical depth, it is also dimensionally correct (unlike some other applications of GP) and can be used for channels with any cross-section and any flow regime.

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Hatakeyama, Hiroyuki, Shingo Mabu, Kotaro Hirasawa, and Jinglu Hu. "Genetic Network Programming with Actor-Critic." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 1 (January 20, 2007): 79–86. http://dx.doi.org/10.20965/jaciii.2007.p0079.

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A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP” has been already proposed. GNP represents its solutions as graph structures, which can improve the expression ability and performance. In addition, GNP with Reinforcement Learning (GNP-RL) was proposed a few years ago. Since GNP-RL can do reinforcement learning during task execution in addition to evolution after task execution, it can search for solutions efficiently. In this paper, GNP with Actor-Critic (GNP-AC) which is a new type of GNP-RL is proposed. Originally, GNP deals with discrete information, but GNP-AC aims to deal with continuous information. The proposed method is applied to the controller of the Khepera simulator and its performance is evaluated.

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Janeiro, Fernando M., and Pedro M. Ramos. "Gene expression programming and genetic algorithms in impedance circuit identification." ACTA IMEKO 1, no. 1 (June 6, 2012): 19. http://dx.doi.org/10.21014/acta_imeko.v1i1.16.

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Impedance circuit identification through spectroscopy is often used to characterize sensors. When the circuit topology is known, it has been shown that the component values can be obtained by genetic algorithms. Also, gene expression programming can be used to search for an adequate circuit topology. In this paper, an improved version of the impedance circuit identification based on gene expression programming and hybrid genetic algorithm is presented to both identify the circuit and estimate its parameters. Simulation results are used to validate the proposed algorithm in different situations. Further validation is presented from measurements on a circuit that models a humidity sensor and also from measurements on a viscosity sensor.

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Ozbek, Ahmet, Mehmet Unsal, and Aydin Dikec. "Estimating uniaxial compressive strength of rocks using genetic expression programming." Journal of Rock Mechanics and Geotechnical Engineering 5, no. 4 (August 2013): 325–29. http://dx.doi.org/10.1016/j.jrmge.2013.05.006.

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14

Kayadelen, C. "Soil liquefaction modeling by Genetic Expression Programming and Neuro-Fuzzy." Expert Systems with Applications 38, no. 4 (April 2011): 4080–87. http://dx.doi.org/10.1016/j.eswa.2010.09.071.

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Taylan, Fatih. "Prediction of Layer Thickness in Molten Borax Bath with Genetic Evolutionary Programming." Zeitschrift für Naturforschung A 66, no. 3-4 (April 1, 2011): 193–98. http://dx.doi.org/10.1515/zna-2011-3-408.

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In this study, the vanadium carbide coating in molten borax bath process is modeled by evolutionary genetic programming (GEP) with bath composition (borax percentage, ferro vanadium (Fe-V) percentage, boric acid percentage), bath temperature, immersion time, and layer thickness data. Five inputs and one output data exist in the model. The percentage of borax, Fe-V, and boric acid, temperature, and immersion time parameters are used as input data and the layer thickness value is used as output data. For selected bath components, immersion time, and temperature variables, the layer thicknesses are derived from the mathematical expression. The results of the mathematical expressions are compared to that of experimental data; it is determined that the derived mathematical expression has an accuracy of 89%.

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Dongmei Zhang, Yang Li, Chengjun Li, and Jianquan Bao. "A Novel Spatial Interpolation Parallel Algorithm based on Genetic Expression Programming." International Journal of Advancements in Computing Technology 3, no. 9 (October 31, 2011): 72–81. http://dx.doi.org/10.4156/ijact.vol3.issue9.10.

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17

Bagatur, T., and F. Onen. "Development of predictive model for flood routing using genetic expression programming." Journal of Flood Risk Management 11 (February 12, 2016): S444—S454. http://dx.doi.org/10.1111/jfr3.12232.

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18

Gillies, Christopher, Nilesh Patel, Jan Akervall, and George Wilson. "Gene expression classification using binary rule majority voting genetic programming classifier." International Journal of Advanced Intelligence Paradigms 4, no. 3/4 (2012): 241. http://dx.doi.org/10.1504/ijaip.2012.052068.

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19

Kök, M., E. Kanca, and Ö. Eyercioğlu. "Tool life model for Al2O3particle reinforced MMCs using genetic expression programming." Materials Science and Technology 27, no. 12 (December 2011): 1819–27. http://dx.doi.org/10.1179/1743284710y.0000000037.

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20

Atici, Umit. "Modelling of the Elasticity Modulus for Rock Using Genetic Expression Programming." Advances in Materials Science and Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/2063987.

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In rock engineering projects, statically determined parameters are more reflective of actual load conditions than dynamic parameters. This study reports a new and efficient approach to the formulation of the static modulus of elasticityEsapplying gene expression programming (GEP) with nondestructive testing (NDT) methods. The results obtained using GEP are compared with the results of multivariable linear regression analysis (MRA), univariate nonlinear regression analysis (URA), and the dynamic elasticity modulus (Ed). The GEP model was found to produce the most accurate calculation ofEs. The proposed approach is a simple, nondestructive, and practical way to determineEsfor anisotropic and heterogeneous rocks.

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Aytek, Ali, Ozgur Kisi, and Aytac Guven. "A genetic programming technique for lake level modeling." Hydrology Research 45, no. 4-5 (September 20, 2013): 529–39. http://dx.doi.org/10.2166/nh.2013.069.

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The potential of gene expression programming (GEP) approach for modeling monthly lake levels is investigated. The application of the methodology is presented for the monthly water level data of Van Lake, which is the biggest lake in Turkey. The root mean square errors, mean absolute relative errors, determination coefficient, and modified coefficient of efficiency (EM) are used for evaluating the accuracy of the genetic programming-based models. The results of the proposed models are compared with those of the neuro-fuzzy models. The comparison results indicate that the suggested GEP-based models perform better than the neuro-fuzzy models in forecasting monthly lake levels.

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Marref, Amine, Saleh Basalamah, and Rami Al-Ghamdi. "Evolutionary Computation Techniques for Predicting Atmospheric Corrosion." International Journal of Corrosion 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/805167.

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Corrosion occurs in many engineering structures such as bridges, pipelines, and refineries and leads to the destruction of materials in a gradual manner and thus shortening their lifespan. It is therefore crucial to assess the structural integrity of engineering structures which are approaching or exceeding their designed lifespan in order to ensure their correct functioning, for example, carrying ability and safety. An understanding of corrosion and an ability to predict corrosion rate of a material in a particular environment plays a vital role in evaluating the residual life of the material. In this paper we investigate the use of genetic programming and genetic algorithms in the derivation of corrosion-rate expressions for steel and zinc. Genetic programming is used to automatically evolve corrosion-rate expressions while a genetic algorithm is used to evolve the parameters of an already engineered corrosion-rate expression. We show that both evolutionary techniques yield corrosion-rate expressions that have good accuracy.

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23

Anđelić, Nikola, Sandi Baressi Šegota, Ivan Lorencin, Zdravko Jurilj, Tijana Šušteršič, Anđela Blagojević, Alen Protić, Tomislav Ćabov, Nenad Filipović, and Zlatan Car. "Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm." International Journal of Environmental Research and Public Health 18, no. 3 (January 22, 2021): 959. http://dx.doi.org/10.3390/ijerph18030959.

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Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

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Taylan, Fatih, and Cengiz Kayacan. "Genetic Evolutionary Approach for Cutting Forces Prediction in Hard Milling." Zeitschrift für Naturforschung A 66, no. 10-11 (November 1, 2011): 675–80. http://dx.doi.org/10.5560/zna.2011-0026.

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Hard milling is a very common used machining procedure in the last years. Therefore the prediction of cutting forces is important. The paper deals with this prediction using genetic evolutionary programming (GEP) approach to set mathematical expression for out cutting forces. In this study, face milling was performed using DIN1.2842 (90MnCrV8) cold work tool steel, with a hardness of 61 HRC. Experimental parameters were selected using stability measurements and simulations. In the hard milling experiments, cutting force data in a total of three axes were collected. Feed direction (Fx) and tangential direction (Fy) cutting forces generated using genetic evolutionary programming were modelled. Cutting speed and feed rate values were treated as inputs in the models, and average cutting force values as output. Mathematical expressions were created to predict average Fxand Fy forces that can be generated in hard material milling.

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Razaq, Salaudeen Abdul, Shamsuddin Shahid, Tarmizi Ismail, Eun-Sung Chung, Morteza Mohsenipour, and Xiao-jun Wang. "Prediction of Flow Duration Curve in Ungauged Catchments Using Genetic Expression Programming." Procedia Engineering 154 (2016): 1431–38. http://dx.doi.org/10.1016/j.proeng.2016.07.516.

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Tashakori Abkenar, Alireza, Ali Nazari, Shantha D. Gamini Jayasinghe, Ajay Kapoor, and Michael Negnevitsky. "Fuel Cell Power Management Using Genetic Expression Programming in All-Electric Ships." IEEE Transactions on Energy Conversion 32, no. 2 (June 2017): 779–87. http://dx.doi.org/10.1109/tec.2017.2693275.

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Lee, Jhong-Yue, I.-Shyan Hwang, Andrew Tanny Liem, K. Robert Lai, and AliAkbar Nikoukar. "Genetic expression programming: a new approach for QoS traffic prediction in EPONs." Photonic Network Communications 25, no. 3 (April 24, 2013): 156–65. http://dx.doi.org/10.1007/s11107-013-0399-x.

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Kanca, E., F. Çavdar, and M. M. Erşen. "Prediction of Mechanical Properties of Cold Rolled Steel Using Genetic Expression Programming." Acta Physica Polonica A 130, no. 1 (July 2016): 365–69. http://dx.doi.org/10.12693/aphyspola.130.365.

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MILLER, NICHOLAS C., and PHILIP K. CHAN. "SEMANTIC SEARCH TECHNIQUES FOR LEARNING SMALLER BOOLEAN EXPRESSION TREES IN GENETIC PROGRAMMING." International Journal of Computational Intelligence and Applications 13, no. 03 (September 2014): 1450018. http://dx.doi.org/10.1142/s1469026814500187.

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One sub-field of Genetic Programming (GP) which has gained recent interest is semantic GP, in which programs are evolved by manipulating program semantics instead of program syntax. This paper introduces a new semantic GP algorithm, called SGP+, which is an extension of an existing algorithm called SGP. New crossover and mutation operators are introduced which address two of the major limitations of SGP: large program trees and reduced accuracy on high-arity problems. Experimental results on "deceptive" Boolean problems show that programs created by the SGP+ are 3.8 times smaller while still maintaining accuracy as good as, or better than, SGP. Additionally, a statistically significant improvement in program accuracy is observed for several high-arity Boolean problems.

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Reza Abdi, Mahmood, and Hadi Rashed. "Shear Strength Enhancement Prediction of Sand–Fiber Mixtures Using Genetic Expression Programming." Journal of Materials in Civil Engineering 33, no. 11 (November 2021): 04021323. http://dx.doi.org/10.1061/(asce)mt.1943-5533.0003954.

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Ghazouani, Haythem. "A genetic programming-based feature selection and fusion for facial expression recognition." Applied Soft Computing 103 (May 2021): 107173. http://dx.doi.org/10.1016/j.asoc.2021.107173.

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Mabu, Shingo, Fengming Ye, and Kotaro Hirasawa. "An Explicit Memory Scheme of Genetic Network Programming." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 7 (November 20, 2012): 851–63. http://dx.doi.org/10.20965/jaciii.2012.p0851.

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Many classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Strategies (ES), etc. have made significant contribution to the study of evolutionary computation. And recently, a new approach named Genetic Network Programming (GNP) has been proposed especially for solving complex problems in dynamic environments. It is based on the algorithms of classical evolutionary computation techniques and uses data structures of directed graphs which are the unique feature of GNP. Focusing on GNP’s distinguished expression ability of the graph structure, this paper proposes an enhanced architecture for standard GNP in order to improve the performance of GNP by adopting an explicit memory scheme which records and utilizes the exploited information flexibly and extensively during the evolution process of GNP. In the enhanced architecture, the important gene information of the elite individuals is extracted and accumulated in the memory during evolution. Among the accumulated information, some of them are selected and used to guide the agents. In this paper, the proposed architecture is applied to the tileworld which is an excellent benchmark for evaluating the architecture demonstrating its superiority.

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Guven, Aytac, and H. Md Azamathulla. "Gene-expression programming for flip-bucket spillway scour." Water Science and Technology 65, no. 11 (June 1, 2012): 1982–87. http://dx.doi.org/10.2166/wst.2012.100.

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During the last two decades, researchers have noticed that the use of soft computing techniques as an alternative to conventional statistical methods based on controlled laboratory or field data, gave significantly better results. Gene-expression programming (GEP), which is an extension to genetic programming (GP), has nowadays attracted the attention of researchers in prediction of hydraulic data. This study presents GEP as an alternative tool in the prediction of scour downstream of a flip-bucket spillway. Actual field measurements were used to develop GEP models. The proposed GEP models are compared with the earlier conventional GP results of others (Azamathulla et al. 2008b; RMSE = 2.347, δ = 0.377, R = 0.842) and those of commonly used regression-based formulae. The predictions of GEP models were observed to be in strictly good agreement with measured ones, and quite a bit better than conventional GP and the regression-based formulae. The results are tabulated in terms of statistical error measures (GEP1; RMSE = 1.596, δ = 0.109, R = 0.917) and illustrated via scatter plots.

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GOV, ESRA, and KAZIM YALCIN ARGA. "GENETIC MUTATIONS ARE CHARACTERIZED BY INCREASE IN ENTROPY AT THE TRANSCRIPTIONAL LEVEL." Journal of Biological Systems 22, no. 03 (August 28, 2014): 377–91. http://dx.doi.org/10.1142/s0218339014500132.

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Predicting the genomic and phenotypic re-programming in organisms undergoing genetic perturbations is a challenging task in modern biology. It is hypothesized that genomic alterations perturb the dynamics of biological information flow. In the present study, a statistical data analysis framework was designed and the network entropy concept was employed to quantify the level of disorder at the transcriptional level as a result of the genomic re-programming of S. cerevisiae cells under genetic perturbations. The customized re-programming in transcription levels to different genetic modifications was observed and genetic mutations were characterized by enhanced network entropies, which revealed higher degree of randomness in mRNA expression levels. To our knowledge, this study constitutes the first numerical demonstration on the conservative energetic state of the microorganisms against genetic perturbations.

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Jȩdrzejowicz, Joanna, Piotr Jȩdrzejowicz, and Izabela Wierzbowska. "Implementing Gene Expression Programming in the Parallel Environment for Big Datasets’ Classification." Vietnam Journal of Computer Science 06, no. 02 (May 2019): 163–75. http://dx.doi.org/10.1142/s2196888819500118.

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The paper investigates a Gene Expression Programming (GEP)-based ensemble classifier constructed using the stacked generalization concept. The classifier has been implemented with a view to enable parallel processing with the use of Spark and SWIM — an open source genetic programming library. The classifier has been validated in computational experiments carried out on benchmark datasets. Also, it has been inbvestigated how the results are influenced by some settings. The paper is an extension of a previous paper of the authors.

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Tenpe, Ashwini R., and Anjan Patel. "Application of genetic expression programming and artificial neural network for prediction of CBR." Road Materials and Pavement Design 21, no. 5 (November 26, 2018): 1183–200. http://dx.doi.org/10.1080/14680629.2018.1544924.

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Wu, Chih-Hung, I.-Sheng Lin, Ming-Liang Wei, and Tain-Yu Cheng. "Target Position Estimation by Genetic Expression Programming for Mobile Robots With Vision Sensors." IEEE Transactions on Instrumentation and Measurement 62, no. 12 (December 2013): 3218–30. http://dx.doi.org/10.1109/tim.2013.2272173.

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Baylar, Ahmet, Mehmet Unsal, Fahri Ozkan, and Cafer Kayadelen. "Estimation of air entrainment and aeration efficiencies of weirs using genetic expression programming." KSCE Journal of Civil Engineering 18, no. 6 (May 20, 2014): 1632–40. http://dx.doi.org/10.1007/s12205-014-1058-1.

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Tan, Mei Sze, Jing Wei Tan, Siow-Wee Chang, Hwa Jen Yap, Sameem Abdul Kareem, and Rosnah Binti Zain. "A genetic programming approach to oral cancer prognosis." PeerJ 4 (September 21, 2016): e2482. http://dx.doi.org/10.7717/peerj.2482.

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BackgroundThe potential of genetic programming (GP) on various fields has been attained in recent years. In bio-medical field, many researches in GP are focused on the recognition of cancerous cells and also on gene expression profiling data. In this research, the aim is to study the performance of GP on the survival prediction of a small sample size of oral cancer prognosis dataset, which is the first study in the field of oral cancer prognosis.MethodGP is applied on an oral cancer dataset that contains 31 cases collected from the Malaysia Oral Cancer Database and Tissue Bank System (MOCDTBS). The feature subsets that is automatically selected through GP were noted and the influences of this subset on the results of GP were recorded. In addition, a comparison between the GP performance and that of the Support Vector Machine (SVM) and logistic regression (LR) are also done in order to verify the predictive capabilities of the GP.ResultThe result shows that GP performed the best (average accuracy of 83.87% and average AUROC of 0.8341) when the features selected are smoking, drinking, chewing, histological differentiation of SCC, and oncogene p63. In addition, based on the comparison results, we found that the GP outperformed the SVM and LR in oral cancer prognosis.DiscussionSome of the features in the dataset are found to be statistically co-related. This is because the accuracy of the GP prediction drops when one of the feature in the best feature subset is excluded. Thus, GP provides an automatic feature selection function, which chooses features that are highly correlated to the prognosis of oral cancer. This makes GP an ideal prediction model for cancer clinical and genomic data that can be used to aid physicians in their decision making stage of diagnosis or prognosis.

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Guo, Zhaolu, Zhijian Wu, Xiaojian Dong, Kejun Zhang, Shenwen Wang, and Yuanxiang Li. "Component Thermodynamical Selection Based Gene Expression Programming for Function Finding." Mathematical Problems in Engineering 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/915058.

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Gene expression programming (GEP), improved genetic programming (GP), has become a popular tool for data mining. However, like other evolutionary algorithms, it tends to suffer from premature convergence and slow convergence rate when solving complex problems. In this paper, we propose an enhanced GEP algorithm, called CTSGEP, which is inspired by the principle of minimal free energy in thermodynamics. In CTSGEP, it employs a component thermodynamical selection (CTS) operator to quantitatively keep a balance between the selective pressure and the population diversity during the evolution process. Experiments are conducted on several benchmark datasets from the UCI machine learning repository. The results show that the performance of CTSGEP is better than the conventional GEP and some GEP variations.

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Azamathulla, H. Md. "Gene-expression programming to predict scour at a bridge abutment." Journal of Hydroinformatics 14, no. 2 (June 18, 2011): 324–31. http://dx.doi.org/10.2166/hydro.2011.135.

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The process involved in the local scour at an abutment is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This study presents the use of gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to estimate the scour depth. The datasets of laboratory measurements were collected from the published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The proposed GEP approach gives satisfactory results compared with existing predictors and artificial neural network (ANN) modeling in predicting the scour depth at an abutment.

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Giustolisi, Orazio. "Using genetic programming to determine Chèzy resistance coefficient in corrugated channels." Journal of Hydroinformatics 6, no. 3 (July 1, 2004): 157–73. http://dx.doi.org/10.2166/hydro.2004.0013.

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Genetic Programming has been used to determine Chèzy resistance coefficient for full circular corrugated channels. Three corrugated plastic pipes have been experimentally studied in order to generate data. The tests aim at measuring hydraulic parameters of the open-channel flow for some slopes, from 3.49–17.37% (2–10°), in order to discover the dependence of the channel resistance coefficient when wake-interference flow occurs. The monomial formula for the Chèzy resistance coefficient performs well on experimental data, both from measurement errors and from a technical point of view. In this paper, we present some very parsimonious formulae that have been created by Genetic Programming with few constants and which fit the data better than the monomial formula. Moreover, two of the Genetic Programming formulae, after ‘physical post-refinement’, seem to better explain the role of the roughness in the Chèzy resistance coefficient for corrugated channels with respect to its traditional expression for rough channels. This fact suggests that at least the structure of those formulae can be extrapolated to other types of corrugated channels. Finally, the work stresses the fact that the Genetic Programming hypothesis can be easily manipulated by means of ‘human’ physical insight. Therefore, Genetic Programming should be considered more than a simple data-driven technique, especially when it is used to perform scientific discovery.

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Fajfar, Iztok, and Tadej Tuma. "Creation of Numerical Constants in Robust Gene Expression Programming." Entropy 20, no. 10 (October 1, 2018): 756. http://dx.doi.org/10.3390/e20100756.

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The problem of the creation of numerical constants has haunted the Genetic Programming (GP) community for a long time and is still considered one of the principal open research issues. Many problems tackled by GP include finding mathematical formulas, which often contain numerical constants. It is, however, a great challenge for GP to create highly accurate constants as their values are normally continuous, while GP is intrinsically suited for combinatorial optimization. The prevailing attempts to resolve this issue either employ separate real-valued local optimizers or special numeric mutations. While the former yield better accuracy than the latter, they add to implementation complexity and significantly increase computational cost. In this paper, we propose a special numeric crossover operator for use with Robust Gene Expression Programming (RGEP). RGEP is a type of genotype/phenotype evolutionary algorithm closely related to GP, but employing linear chromosomes. Using normalized least squares error as a fitness measure, we show that the proposed operator is significantly better in finding highly accurate solutions than the existing numeric mutation operators on several symbolic regression problems. Another two important advantages of the proposed operator are that it is extremely simple to implement, and it comes at no additional computational cost. The latter is true because the operator is integrated into an existing crossover operator and does not call for an additional cost function evaluation.

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Alitavoli, M., E. Khaleghi, H. Babaei, T. Mirzababaie Mostofi, and N. Namazi. "Modeling and prediction of metallic powder behavior in explosive compaction process by using genetic programming method based on dimensionless numbers." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 233, no. 2 (February 28, 2018): 195–201. http://dx.doi.org/10.1177/0954408918761223.

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Explosive compaction process of metallic powders has been studied using a semi-empirical method. This method utilizes dimensional analysis along with genetic programming approach to obtain an expression relating the final density of compacts to the effective parameters during compaction process such as shock compaction energy, properties of metallic powder, and geometry of the problem and explosive charge. Dimensionless numbers have been constructed based on the effective parameters using a complete set of input–output experimental data. The obtained dimensionless numbers then have been applied as input–output data pairs for genetic programming optimization process considering modeling error as the objective. The obtained results show that the proposed model using dimensional analysis method along with genetic programming can predict the final density of compacts with 99.8% accuracy. Also, the outputs of the proposed model have been compared with those obtained by group method of data handling type neural network in the literature. Consequently, genetic programming method has much less root mean square error than group method of data handling model and can be successfully used for modeling and prediction of the complex process behavior.

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Paul, T. K., and H. Iba. "Prediction of Cancer Class with Majority Voting Genetic Programming Classifier Using Gene Expression Data." IEEE/ACM Transactions on Computational Biology and Bioinformatics 6, no. 2 (April 2009): 353–67. http://dx.doi.org/10.1109/tcbb.2007.70245.

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Bhadouria, Vivek Singh, and Dibyendu Ghoshal. "A study on genetic expression programming-based approach for impulse noise reduction in images." Signal, Image and Video Processing 10, no. 3 (May 27, 2015): 575–84. http://dx.doi.org/10.1007/s11760-015-0780-6.

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Hassani, Meisam, Mohammad Safi, Reza Rasti Ardakani, and Amir Saedi Daryan. "Predicting fire resistance of SRC columns through gene expression programming." Journal of Structural Fire Engineering 12, no. 2 (November 9, 2020): 125–40. http://dx.doi.org/10.1108/jsfe-04-2020-0013.

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Purpose This paper aims to predict the fire resistance of steel-reinforced concrete columns by application of the genetic algorithm. Design/methodology/approach In total, 11 effective parameters are considered including mechanical and geometrical properties of columns and loading values as input parameters and the duration of concrete resistance at elevated temperatures as the output parameter. Then, experimental data of several studies – with extensive ranges – are collected and divided into two categories. Findings Using the first set of the data along with the gene expression programming (GEP), the fire resistance predictive model of steel-reinforced concrete (SRC) composite columns is presented. By application of the second category, evaluation and validation of the proposed model are investigated as well, and the correspondent time-temperature diagrams are derived. Originality/value The relative error of 10% and the R coefficient of 0.9 for the predicted model are among the highlighted results of this validation. Based on the statistical errors, a fair agreement exists between the experimental data and predicted values, indicating the appropriate performance of the proposed GEP model for fire resistance prediction of SRC columns.

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Nakajima, Natsu, and Tatsuya Akutsu. "Network Completion for Static Gene Expression Data." Advances in Bioinformatics 2014 (March 26, 2014): 1–9. http://dx.doi.org/10.1155/2014/382452.

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We tackle the problem of completing and inferring genetic networks under stationary conditions from static data, where network completion is to make the minimum amount of modifications to an initial network so that the completed network is most consistent with the expression data in which addition of edges and deletion of edges are basic modification operations. For this problem, we present a new method for network completion using dynamic programming and least-squares fitting. This method can find an optimal solution in polynomial time if the maximum indegree of the network is bounded by a constant. We evaluate the effectiveness of our method through computational experiments using synthetic data. Furthermore, we demonstrate that our proposed method can distinguish the differences between two types of genetic networks under stationary conditions from lung cancer and normal gene expression data.

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DIOŞAN, LAURA, ALEXANDRINA ROGOZAN, and JEAN-PIERRE PECUCHET. "LEARNING SVM WITH COMPLEX MULTIPLE KERNELS EVOLVED BY GENETIC PROGRAMMING." International Journal on Artificial Intelligence Tools 19, no. 05 (October 2010): 647–77. http://dx.doi.org/10.1142/s0218213010000352.

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Classic kernel-based classifiers use only a single kernel, but the real-world applications have emphasized the need to consider a combination of kernels — also known as a multiple kernel (MK) — in order to boost the classification accuracy by adapting better to the characteristics of the data. Our purpose is to automatically design a complex multiple kernel by evolutionary means. In order to achieve this purpose we propose a hybrid model that combines a Genetic Programming (GP) algorithm and a kernel-based Support Vector Machine (SVM) classifier. In our model, each GP chromosome is a tree that encodes the mathematical expression of a multiple kernel. The evolutionary search process of the optimal MK is guided by the fitness function (or efficiency) of each possible MK. The complex multiple kernels which are evolved in this manner (eCMKs) are compared to several classic simple kernels (SKs), to a convex linear multiple kernel (cLMK) and to an evolutionary linear multiple kernel (eLMK) on several real-world data sets from UCI repository. The numerical experiments show that the SVM involving the evolutionary complex multiple kernels perform better than the classic simple kernels. Moreover, on the considered data sets, the new multiple kernels outperform both the cLMK and eLMK — linear multiple kernels. These results emphasize the fact that the SVM algorithm requires a combination of kernels more complex than a linear one in order to boost its performance.

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Mattick, John S. "The central role of RNA in the genetic programming of complex organisms." Anais da Academia Brasileira de Ciências 82, no. 4 (December 2010): 933–39. http://dx.doi.org/10.1590/s0001-37652010000400016.

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Notwithstanding lineage-specific variations, the number and type of protein-coding genes remain relatively static across the animal kingdom. By contrast there has been a massive expansion in the extent of genomic non-proteincoding sequences with increasing developmental complexity. These non-coding sequences are, in fact, transcribed in a regulated manner to produce large numbers of large and small non-protein-coding RNAs that control gene expression at many levels including chromatin architecture, post-transcriptional processing and translation. Moreover, many RNAs are edited, especially in the nervous system, which may be the basis of epigenome-environment interactions and the function of the brain.

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