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gene expression programming With Structured Reusability

INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2025年第1期19卷

作者： Zheng, Yaping He, Pei Chang, Chi-Chang Li, Kangshun Guangzhou Univ Guangzhou Peoples R China Chung Shan Med Univ Taichung Taiwan Dongguan City Univ Dongguan Peoples R China

In the field of evolutionary computation, gene expression programming (GEP) has been favored by the public because of its straightforward encoding method. Due to the coding rules for the head and tail on gene, the ability of gene to be constructed as an expression is limited, thereby limiting the expressiveness of individuals. In this article, a variant that uses the standard GEP individual structure and is based on code reuse strategy is proposed to maintain the concise individual representation advantage of GEP and improve the expressive ability of individual. This method can improve the accuracy of the individual and reduce the expression tree complexity. In addition, experiments conducted to predict the recurrence of cervical cancer and malignancy of breast cancer have demonstrated that the predicted performance of GEP and gene expression programming with structured reusability (SR-GEP) has performed better than that of C5.0, but SR-GEP performs best.

关键词： Evolutionary Computation gene expression programming Reuse Strategy Classification

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Experimental analysis and gene expression programming optimization of sustainable concrete containing mineral fillers

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SCIENTIFIC REPORTS 2024年第1期14卷 1-26页

作者： Rauf, Ayesha Asif, Usama Onyelowe, Kennedy Javed, Muhammad Faisal Alabduljabbar, Hisham Nazarbayev Univ Dept Civil Engn Nazarbayev Kazakhstan COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Michael Okpara Univ Agr Dept Civil Engn Umudike 440109 Nigeria Kampala Int Univ Dept Civil Engn Western Campus Kampala Uganda Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi Pakistan Western Caspian Univ Baku Azerbaijan Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia

Rapid urbanization has led to a high demand for concrete, causing a significant depletion of vital natural resources, notably river sand, which is crucial in the manufacturing process of concrete. As a result, there is a growing need for environmentally sustainable alternatives to fine aggregate in concrete. Quarry dust (QD) has evolved as a viable and ecologically friendly substitute in response to this demand. In the past, limited experimental investigations and only conventional modeling techniques were used to promote sustainable mineral fillers in concrete. This study proposed a robust soft computing technique using gene-expression programming (GEP) to enhance the usability of sustainable alternatives in concrete. Initially, an experimental study was carried out to examine the feasibility and mechanical characteristics of concrete made from materials including quarry dust and superplasticizer as a partial replacement for fine aggregate. Ten mixed proportions with various proportions (0%, 20%, 40%, and 60%) of quarry dust were used to make M15 and M20 grades of concrete. A series of experimental tests, such as workability, compressive strength (CS), and tensile strength (TS), were conducted to examine the fresh and hardened properties of modified concrete. The established database from the experimental investigations was then used to develop machine learning (ML) models using GEP. The outcomes of the GEP models were validated by comparing them with multi-linear regression (MLR) models and using various statistical metrics such as root mean squared error (RMSE), performance index (PI), correlation coefficient (R), and external validation methods. Finally, sensitivity analysis was performed to investigate the influence of ingredients such as mineral fillers, superplasticizers, and others on the mechanical properties of concrete. To enhance the practical usage of the study, a graphical user interface (GUI) was also created. The study revealed that 40% of the replac

关键词： Quarry dust Compressive strength Split Tensile Strength Superplasticizer gene expression programming

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Predicting Standard Penetration Test N-value from Cone Penetration Test Data Using gene expression programming

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2024年第7期42卷 5587-5613页

作者： Alam, Mehtab Chen, Jianfeng Umar, Muhammad Ullah, Faheem Shahkar, Muhammad Tongji Univ Coll Civil Engn Dept Geotech Engn Shanghai 200092 Peoples R China Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Engn Sci Topi 23460 Khyber Pakhtunk Pakistan Univ Tokyo Inst Ind Sci Tokyo Japan Natl Univ Comp & Emerging Sci Dept Civil Engn Lahore 54770 Punjab Pakistan Chinese Acad Sci Inst Mt Hazards & Environm Key Lab Mt Hazards & Earth Surface Proc Chengdu 610041 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

Standard Penetration Test (SPT) and the Cone Penetration Test (CPT) are employed in-situ to evaluate soil parameters. In geotechnical engineering practice, engineers often conduct in-situ tests either SPT or CPT to delineate soil profile and evaluate soil parameters for bearing capacity analysis. Most of the geotechnical parameters are correlated with SPT instead and widely employed. Since numerous soil parameters are correlated with SPT N-values, it is very beneficial to establish a correlation between CPT data and SPT N-values. To predict the SPT-N value from CPT data across various soil types such as silty sand, sandy silt, silty clay, and lean clay, this study has developed an empirical model using gene expression programming (GEP). Also a comprehensive GEP model encompassing all soil types has been proposed. The input parameter used in the GEP models are CPT tip resistance (q(c)), CPT-Sleeve friction (q(f)), and effective overburden pressure (sigma(v)'). The effectiveness of the models is evaluated through the implementation of statistical tests, employing a comprehensive index OBJ, and performing parametric analysis. Moreover, to test the reliability of the proposed GEP models, CPT-SPT data pairs that were not utilized in the model generation were employed. The results of the proposed models testing indicated that the models either under-predicts the targeted value by 3-9% or over-predicts by 3-12%. The OBJ values indicate that silty clay has the highest value of 4.985, making it the weakest model, while the all-soil model achieved the lowest value of 1.656, thus being considered the most effective model. The results indicated that the suggested models are precise and exhibit a strong potential for generalization and prediction.

关键词： Standard penetration test Cone penetration test Machine learning gene expression programming

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Prediction of optical properties of rare-earth doped phosphate glasses using gene expression programming

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SCIENTIFIC REPORTS 2024年第1期14卷 1-19页

作者： Ahmadi, Fahimeh El-Mallawany, Raouf Papanikolaou, Stefanos Asteris, Panagiotis G. Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Menoufia Univ Fac Sci Phys Dept Shibin Al Kawm Egypt Natl Ctr Nucl Res NOMATEN Ctr Excellence Ul A Soltana 7 PL-05400 Otwock Poland MIT Dept Nucl Sci & Engn 77 Massachusetts Ave Cambridge MA 02139 USA Computat Mech Lab Sch Pedag & Technol Educ Athens Greece

The progression of optical materials and their associated applications necessitates a profound comprehension of their optical characteristics, with the Judd-Ofelt (JO) theory commonly employed for this purpose. However, the computation of JO parameters (Omega 2, Omega 4, Omega 6) entails wide experimental and theoretical endeavors, rendering traditional calculations often impractical. To address these challenges, the correlations between JO parameters and the bulk matrix composition within a series of Rare-Earth ions doped sulfophosphate glass systems were explored in this research. In this regard, a novel soft computing technique named genetic expression programming (GEP) was employed to derive formulations for JO parameters and bulk matrix composition. The predictor variables integrated into the formulations consist of JO parameters. This investigation demonstrates the potential of GEP as a practical tool for defining functions and classifying important factors to predict JO parameters. Thus, precise characterization of such materials becomes crucial with minimal or no reliance on experimental work.

关键词： Phosphate glass Rare-earth ions Optical properties Judd-Ofelt parameters gene expression programming

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gene expression programming to High-Cycle and Low-Cycle Fatigue Life Prediction of Polymer-Reinforced Concrete in the Vicinity of Room, Water, and Seawater Considering Different Immersion Times

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JOURNAL OF FAILURE ANALYSIS AND PREVENTION 2025年

作者： Kashyzadeh, Kazem Reza Rezaeian, Naeim RUDN Univ Acad Engn Dept Transport Equipment & Technol 6 Miklukho Maklaya St Moscow 117198 Russia RUDN Univ Dept Appl Artificial Intelligence Miklukho Maklaya St Moscow 117198 Russia

Nowadays, the use of machine learning techniques to solve industrial problems is very popular, although the prediction accuracy of these techniques depends on various factors such as input data, neural network architecture, and its settings. But by combining several destructive phenomena at the same time, the complexity of using machine learning increases. The current research presents a gene expression programming (GEP)-based mathematical equations to predict both high-cycle fatigue (HCF) and low-cycle fatigue (LCF) behaviors of polymer-reinforced concrete (PC) exposed to various environmental degradation. To reach this goal, 54 cylindrical samples of PC were prepared in the same conditions. Also, two degradation environments with different pH values were used representing water and seawater, and the samples were immersed in them for zero, one, and 6 months. Next, axial fatigue test was performed at three different load levels. Furthermore, pH value, immersion time, and maximum cyclic force were considered as input parameters to the GEP. Also, the average number of cycles to failure corresponding to each load level was considered as output. At first, according to the range of one thousand cycles, the separation of LCF and HCF regions was done. Furthermore, a unique formula was presented for each fatigue zone. Finally, the model accuracy was assessed for all cases.

关键词： Axial fatigue test Fatigue life prediction Polymer-reinforced concrete Degradation environment gene expression programming

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Prediction of creep index of soft clays using gene expression programming

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SOFT COMPUTING 2023年第21期27卷 16265-16278页

作者： Xue, Xinhua Deng, Chubing Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Peoples R China

The creep index plays an important role in calculating the long-term settlement of natural soft clays, so it is vital to determine the creep index quickly and accurately. However, the prediction accuracy of the existing creep index models is low. This study presents seven gene expression programming (GEP) models by using different combinations of the liquid limit w(L), plasticity index I-p, void ratio e and clay content CI as input variables for the prediction of creep index. A total of 151 datasets were collected from the available literature for building and testing the GEP models. The proposed GEP models were compared with two machine learning (ML) models (i.e., back propagation neural network and random forest) and five conventional empirical models in terms of three statistical indicators. The research results showed that the prediction performances of the two proposed GEP models (i.e., with combinations CI-w(L)-e and CI-I-p-w(L)-e as input, respectively) surpass those of the five conventional empirical models and two ML-based models, recommended for predicting the creep index of natural soft clays in engineering practice.

关键词： gene expression programming Creep index Plasticity index Liquid limit Void ratio Clay content

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Multiphase Turbulence Modeling Using Sparse Regression and gene expression programming

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NUCLEAR TECHNOLOGY 2023年第12期209卷 1977-1986页

作者： Beetham, S. Capecelatro, J. Oakland Univ Dept Mech Engn 115 Lib Dr Rochester MI 48309 USA Univ Michigan Dept Mech Engn 2350 Hayward St Ann Arbor MI 48109 USA Univ Michigan Dept Aerosp Engn 1320 Beal Ave Ann Arbor MI 48109 USA

Turbulence in two-phase flows drives many important natural and engineering processes, from geophysical flows to nuclear power generation. Strong interphase coupling between the carrier fluid and disperse phase precludes the use of classical turbulence models developed for single-phase flows. In recent years, there has been an explosion of machine learning techniques for turbulence closure modeling, though many rely on augmenting existing models. In this work, we propose an approach that blends sparse regression and gene expression programming (GEP) to generate closed-form algebraic models from simulation data. Sparse regression is used to determine a minimum set of functional groups required to capture the physics, and GEP is used to automate the formulation of the coefficients and dependencies on operating conditions. The framework is demonstrated on homogeneous turbulent gas-particle flows in which two-way coupling generates and sustains carrier-phase turbulence.

关键词： Multiphase flow data-driven modeling turbulence gene expression programming

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Comparison of the performances of the gene expression programming model and the RegCM model in predicting monthly runoff

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JOURNAL OF WATER AND CLIMATE CHANGE 2023年第10期14卷 3810-3829页

作者： Pouyanfar, Sajjad Nozari, Hamed Khodamoradpour, Mehraneh Bu Ali Sina Univ Fac Agr Dept Water Sci Engn Hamadan 65174 Iran

Prediction of rainfall and runoff is one of the most important issues in managing catchment water resources and sustainable use of water resources. In this study, the accuracy and efficiency of the gene expression programming (GEP) model and the Regional Climate Model (RegCM) to predict runoff values from monthly precipitation were investigated. For this purpose, monthly precipitation data of 48 synoptic stations, monthly temperature data of 21 synoptic stations, and also monthly runoff data of 40 hydrometric stations located in the Karkheh basin during 45 years (1972-2017) were used. Out of this statistical period, 40 years was used for calibration, and 5 years (1995-1999) for the validation of the model results. The results showed that the GEP model with an average R-2 value of 0.948, average RMSE value of 19.4 m(3)/s, average NSE value of 0.91, and average SE value of 0.3, had a much more accurate performance than the RegCM model, which had an average R-2 value of 0.04, average RMSE value of 298.2 m(3)/s, average NSE value of -0.64, and average SE value of 4.6 in predicting monthly runoff.

关键词： artificial intelligence gene expression programming regional climate model runoff prediction

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Mathematical-Based gene expression programming (GEP): A Novel Model to Predict Zinc Separation from a Bench-Scale Bioleaching Process

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JOURNAL OF SUSTAINABLE METALLURGY 2023年第4期9卷 1601-1619页

作者： Hosseini, Shahab Javanshir, Sepideh Sabeti, Hamid Tahmasebizadeh, Parastoo Tarbiat Modares Univ Fac Engn Tehran Iran Univ Birjand Fac Engn Birjand Iran Birjand Univ Technol Dept Min Engn Birjand *** Iran

Attempts to predict metal recovery accurately have been hindered by the complexity of the solvent extraction process, nonlinear effects, and the multitude of influencing factors. Conventional computing algorithms must be improved for solving practical problems due to insufficient or noisy data and complex multidimensional scenarios. In this study, a gene expression programming (GEP) model was developed to predict zinc extraction (ZE) from the bioleaching process utilizing the most influential parameters such as stirring speed (0-800 rpm), temperature (25-45 degrees C), pH (1-2.5), Di-(2-ethylhexyl) phosphoric acid (D2EHPA) concentration (5-20%), phase ratio (1:5-10:1), saponification degree (0-40%), and contact time (0-900 s) as the input parameters. Under optimal conditions of 20% D2EHPA, 15% saponification degree, 650 rpm stirring speed, pH2, and an A:O ratio of 1:1, zinc extraction reached 98.4%. The main motivation for constructing the GEP model is to present a rational mathematical model with more accurate results than statistical models. Furthermore, a model should be applicable for future usages to predict the value of metal recovery using independent variables accurately. The developed GEP model outperformed multiple linear regression and multiple nonlinear regression models with the adjusted R2 of 0.9551 and 0.9453, RMSE of 5.5100 and 7.2727, MAE of 0.0758 and 2.8308, MARE of 0.0260 and 0.0334, and VAF of 92.3069 and 87.9199 for the respective training and testing parts. Besides, the sensitivity analysis was performed using the cosine amplitude (CA) technique, and the stirring speed and D2EHPA concentration have the highest (rij = 0.931) and lowest (rij = 0.581) impact on the predicted ZE, respectively. The conducted study proves the appropriateness of the developed GEP model for ZE prediction.

关键词： Solvent extraction of zinc gene expression programming Multiple regression analysis Sensitivity analysis

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Predicting buckling loads of perforated rectangular isotropic panels using gene expression programming and Artificial Neural Network

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2023年第8期52卷 5174-5194页

作者： Al Qablan, Husam Al-Qablan, Tamara Hashemite Univ Fac Engn Civil Engn Dept Zarqa Jordan Al Balqa Appl Univ Al Huson Univ Coll Dept Informat Technol Amman Jordan POB 150459 Zarqa 13135 Jordan

This study aims to develop semi-empirical formulas for foretelling the buckling loads of clamped and simply supported rectangular isotropic panels with central circular perforation exposed to different ratios of biaxial load conditions. The empirical formulas were developed and evaluated using gene expression programming (GEP), Artificial Neural Network (ANN), and Finite Element Method (FEM). A total of 714 data set, generated using the FEM, is used to establish and validate the empirical formulas. This study investigates the effect of perforation sizes, plate aspect ratios, and biaxial load ratios on the buckling strength of perforated panels. The proposed formulas will allow a quick and easy estimation of buckling loads for perforated rectangular panels with acceptable accuracy without the need for sophisticated calculations. The results of the empirical formulas were comparable reasonably well with the results of the finite element analysis (FE) and available literature findings.

关键词： Buckling cutout FE analysis semi-empirical formula gene expression programming Artificial Neural Network

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