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Feature selection approach for evolving reactive scheduling policies for dynamic job shop scheduling problem using gene expression programming

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第15期61卷 5029-5052页

作者： Shady, Salama Kaihara, Toshiya Fujii, Nobutada Kokuryo, Daisuke Kobe Univ Grad Sch Syst Informat Kobe Hyogo Japan

Dispatching rules are one of the most widely applied methods for solving Dynamic Job Shop Scheduling problems (DJSSP) in real-world manufacturing systems. Hence, the automated design of effective rules has been an important subject in the scheduling literature for the past several years. High computational requirements and difficulty in interpreting generated rules are limitations of literature methods. Also, feature selection approaches in the field of automated design of scheduling policies have been developed for the tree-based GP approach only. Therefore, the aim of this study is to propose a feature selection approach for the gene expression programming (GEP) algorithm to evolve high-quality rules in simple structures with an affordable computational budget. This integration speeds up the search process by restricting the GP search space using the linear representation of the GEP algorithm and creates concise rules with only meaningful features using the feature selection approach. The proposed algorithm is compared with five algorithms and 30 rules from the literature under different processing conditions. Three performance measures are considered including total weighted tardiness, mean tardiness, and mean flow time. The results show that the proposed algorithm can generate smaller rules with high interpretability in a much shorter training time.

关键词： Feature selection gene expression programming dispatching rules genetic programming dynamic job shop scheduling discrete event simulation

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gene expression programming with dual strategies and neighborhood search for symbolic regression problems

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APPLIED SOFT COMPUTING 2023年 145卷

作者： Peng, Hu Li, Lin Mei, Changrong Deng, Changshou Yue, Xuezhi Wu, Zhijian Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Jiujiang Univ Sch Elect Informat Engn Jiujiang 332005 Peoples R China Jiangxi Univ Sci & Technol Sch Sci Ganzhou 341000 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430000 Peoples R China

As an evolutionary algorithm for automatic generation of computer programs, gene expression programming (GEP) has received a lot of attention because it can generate concise and highly accurate solutions. However, GEP also has some unsatisfactory aspects, such as too many control parameters and a lack of balance between exploration and exploitation. Therefore, a new GEP with dual strategies and neighborhood search (DNSGEP) is proposed in this paper for symbolic regression problems. As the core part, a new multi-parent crossover operator with neighborhood search is proposed to replace the original transposition and recombination operators in GEP, which greatly reduces the excessive control parameters in GEP. To assist the multi-parent crossover operator in selecting parents, selection weight is used to assess the quality of individuals in the population. The multi-parent crossover operator combines selection weight to learn more from the better parents. In order to satisfy the different needs of global search and local search of the algorithm, two different crossover strategies are extended on the basis of the multi-parent crossover operator. Furthermore, a population distribution entropy strategy to GEP is proposed for determining when the algorithm should choose which crossover strategy to use. DNSGEP is compared with GEP and three GEP variants on 14 symbolic regression problems and 6 even parity problems. Experimental results show that DNSGEP has the success rate and accuracy that are fully superior to GEP, GEPADF and OGEP, not inferior to SLGEP.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： gene expression programming Multi-parent crossover operator Neighborhood search Dual strategies mechanism Population distribution entropy

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Prediction of UCS of fine-grained soil based on machine learning part 1: multivariable regression analysis, gaussian process regression, and gene expression programming

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MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2023年第2期6卷 199-222页

作者： Khatti, Jitendra Grover, Kamaldeep Singh Rajasthan Tech Univ Dept Civil Engn Kota Rajasthan India

The present research introduces the best architecture approach and model for predicting the unconfined compressive strength (UCS) of cohesive virgin soil by comparing the multivariable regression analysis (MRA), gaussian process regression (GPR), and gene expression programming (GEP) approaches. The present research reveals the effect of the quality & quantity of the training database and the impact of the multicollinearity on the performance and overfitting of the MRA, GPR, and GEP models. The performance of the soft computing models has been measured by root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), weighted mean absolute percentage error (WMAPE), coefficient of determination (R-2), correlation coefficient (r), variance accounted for (VAF), Nash-Sutcliffe efficiency (NS), normalized mean bias error (NMBE), Legate McCabe's Index (LMI), root mean square error to observations' standard deviation ratio (RSR), a20-index, index of agreement (IOA), and index of scatter (IOS) statistical tools. The performance comparison of MRA, GPR, and GEP shows that GPR model MD11 has predicted UCS of soil with high performance (R = 0.9959, VAF = 99.18, NS = 0.9909, LMI = 0.1026, RSR = 0.0952, a20-index = 100.00, IOA = 0.9487 & IOS = 0.0531) and the least prediction error (RMSE = 2.4482 N/cm(2), MAE = 1.8840 N/cm(2), MAPE = 5.0849 N/cm(2), WMAPE = 0.0408 N/cm(2), NMBE = 0.1299 N/cm(2)). In the validation, model MD11 has achieved RMSE = 3.4849 N/cm(2), MAE = 3.1845 N/cm(2), R = 0.9040, R-2 = 0.8172, confidence interval of +/- 5.0% by predicting UCS of lab-tested twelve soil samples, which is acceptable. This study shows that the GPR approach predicts better UCS in the presence of multicollinearity and using a small database. The sensitivity analysis illustrates that the UCS prediction of cohesive virgin soil is very highly influenced by fine content, dry unit weight, porosity, void ratio, degree of saturation, and specific gravity of soi

关键词： Machine learning Regression analysis Gaussian process regression gene expression programming Unconfined compressive strength

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Prediction of Pile Setup for Driven Pipe Piles in Fine-Grained Soils Using gene expression programming

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2023年第6期41卷 3605-3624页

作者： Alzahrani, Saeed Bilgin, Omer Imam Abdulrahman Bin Faisal Univ Coll Engn Dept Civil & Construct Engn Dammam 31451 Saudi Arabia Univ Dayton Dept Civil Engn 300 Coll Pk Dayton OH 45469 USA

This paper presents the development of a novel model for predicting setup of closed-ended steel pipe (CEP) piles driven in predominantly fine-grained soils using gene expression programming (GEP). The proposed GEP model incorporates both pile and soil properties and is based on dynamic pile load test data obtained from 109 piles installed at 59 different sites. Multiple variable regression analyses conducted on the compiled dataset showed that the most influential parameters in predicting the time-dependent resistance of driven piles were the resistance mobilized at the end of pile installation, time elapsed after installation, pile shaft surface area, and average silt content along the pile length. The data were divided into a training set for model calibration and a validation set for model verification. Sensitivity analysis was performed to assess the model's robustness. A comparison of the new GEP model with existing pile setup models in the literature was carried out using the collected data, which demonstrated that the proposed GEP model significantly outperformed the tested models. Additionally, data from 22 additional CEP piles were compiled from the literature for model verification purposes. The results showed that the proposed GEP model can predict the total pile resistance with good accuracy.

关键词： Driven piles Pile setup Pile resistance Dynamic load test CAPWAP Fine-grained soils gene expression programming Soil properties Pile properties

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A New Algorithm for Detection of Animal and Plant Ion Concentration Based on gene expression programming

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2023年第1期17卷 1-11页

作者： Li, Kangshun Lin, Leqing Li, Jiaming Chen, Siwei Jalil, Hassan Dongguan City Coll Dongguan Peoples R China South China Agr Univ Coll Math & Informat Sci Guangzhou Peoples R China South China Agr Univ Guangzhou Peoples R China

In order to accurately predict the concentration detection data of ion sensors for animal and plant, this paper proposes a gene expression programming (GEP) based concentration detection method. The method includes collecting ion concentration data as well as voltage timing data;preprocessing all the collected data to obtain an initial sample set;constructing a prediction model of ion concentration, which is an explicit functional relationship between voltage and the concentration of a specific ion. The gene expression programming is used to train and evaluate the prediction model, and obtain a trained model. By comparing gene expression programming with other two modeling methods, it is found that the accuracy of the model established by gene expression programming has greater advantages than that established by polynomial fitting and neural network in processing animal and plant ion concentration data.

关键词： gene expression programming ion concentration prediction model

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MGEPM: Specific composition style melody generation based on music gene expression programming 10

MGEPM: Specific composition style melody generation based on...

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10th International Conference on Big Data and Information Analytics

作者： Zhou, Xuanfei Luo, Weicheng Yuan, Yutong Tang, Boya Qiao, Linbo Wang, Chen Chen, Xingxuan Changsha Normal Univ Presch Coll Changsha Peoples R China Shantou Univ Coll Engn Shantou Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China Ludong Univ Sch Arts Yantai Peoples R China Natl Univ Def Technol Sch Comp Changsha Peoples R China

ISBN: (纸本)9798350354638;9798350354621

This study proposes a music gene expression programming method to achieve music melody feature learning of a specific singer's compositional style. The user specifies the song format, and the program automatically generates and evaluates songs. A mapping mechanism between 16-bit octal numbers and musical scores is proposed to realize the evolution of musical scores in units of measures. In the melody generation stage, the modification operations that are generally performed by the composer in the process of composing music are integrated with the evolutionary operations of gene expression programming to create a music gene expression programming operation. In the music melody evaluation stage, a long short-term memory neural network evaluation model is established and trained using the main verse and chorus of the music as well as the spectrum of the entire song. The neural network evaluates the resulting song fitness value, providing feedback for music evolution. In contrast to melody generated via other methods, comparative experiments show that the melody generated by this research method has an obvious compositional style of a specific composer.

关键词： automatic melody generation music-style learning gene expression programming long short-term memory evolutionary algorithm deep learning

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Prediction of moment redistribution capacity in reinforced concrete beams using gene expression programming

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STRUCTURES 2023年 47卷 2209-2219页

作者： Aydogan, Mehmet Safa Alacali, Sema Arslan, Guray Yildiz Tech Univ Dept Civil Engn Istanbul Turkey

Moment redistribution can play an important role in making the design of reinforced concrete (RC) structures more realistic and economical. In this paper, a new comprehensive formula has been proposed that considers four input parameters that are thought to influence moment redistribution the most in statically indeterminate RC beams using gene expression programming (GEP). For this reason, an experimental database of 108 data points was collected from experimental studies in the literature to predict the moment redistribution of the RC beams using genetic programming. All of these collected data points belong to two-span RC continuous beams. The results of the GEP formulation were statistically compared with the experimental results obtained from the literature and the results from the equations provided by the current design code provisions. The results of the comparison revealed that the proposed GEP-based formulation has the best performance and accuracy among the proposed models. Moreover, the sensitivity analysis and parametric study were also carried out to evaluate the most critical parameters affecting on moment redistribution of RC continuous beams.

关键词： Moment redistribution RC continuous beams Prediction Design codes gene expression programming

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Development of prediction models for strength properties of concrete using gene expression programming

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2025年第3期10卷 1-29页

作者： Khan, Asad Ullah Javed, Muhammad Faisal Khan, Majid Univ Engn & Technol Dept Civil Engn Peshawar 25120 Pakistan GIK Inst Engn Sci & Technol Topi 23640 Khyber Pakhtunk Pakistan Western Caspian Univ Coll Engn Al Kharj Dept Civil Engn Baku Azerbaijan Southern Illinois Univ Edwardsville Dept Civil Engn Edwardsville IL 62026 USA

Accurately forecasting the strength properties of concrete is critical, especially for design code compliance. While statistical and empirical models are commonly used;however, these methods demand intensive experimentation and may yield inaccuracies in intricate relationships between concrete characteristics, mix components, and curing conditions. To address these limitations, the current study utilized the gene expression programming (GEP) method to forecast the mechanical characteristics of concrete, such as modulus of elasticity (E), modulus of rupture (MOR), and dynamic modulus (DM). For this purpose, 440 values for E and MOR and 279 values for DM were compiled to develop robust and reliable predictive models. Multiple statistical metrics were utilized to validate the predictions of the model. The GEP model exhibited excellent prediction accuracy with a correlation coefficient (R) of 0.95, 0.96, and 0.94 for MOR, E, and DM, respectively. The comparison between GEP and linear regression models revealed revealed that machine learning-based models (GEP) exhibit superior accuracy than traditional regression models. Moreover, SHapley Additive exPlanations (SHAP) revealed that age significantly influenced all three considered strength characteristics of concrete. In addition, a graphical user interface (GUI) has been created to simplify the utilization of ML-based models for predicting the strength characteristics of concrete. The outcome of this study has the potential to be utilized for smartly predicting concrete's strength properties, eliminating the need for expensive and time-consuming testing procedures.

关键词： Concrete Strength properties Machine learning gene expression programming SHAP Graphical user interface

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Correlating Binder Types and Chloride Diffusion Coefficients of Concrete Mixes Using gene expression programming

Correlating Binder Types and Chloride Diffusion Coefficients...

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Annual Conference of the Canadian-Society-for-Civil-Engineering (CSCE)

作者： Shovon, S. M. Abid Anam Islam, Md Mohaiminul Manzur, Tanvir Bangladesh Univ Engn & Technol Dept Civil Engn Dhaka Bangladesh

ISBN: (纸本)9783031615139;9783031615115;9783031615108

There is a high risk of chloride-induced corrosion of reinforced concrete (RC) in chloride-prone environment. Such corrosion of embedded rebars within concrete can drastically reduce the service life of the affected structure. Hence, evaluation of corrosion vulnerability of a RC element under vulnerable environment is necessary to ensure desired durability. The intrusion of chlorine ions in concrete occurs through the interconnected pores within concrete matrix. A high chloride diffusion coefficient of a concrete mix usually indicates a high intrusion susceptibility of chloride in the resultant RC element. One of the effective ways to reduce diffusion coefficient of concrete is through pore refinement that can be achieved by secondary hydration. The pore refinement breaks the connectivity of the pore network and eventually, hinders the intrusion of chloride ions. The secondary hydration is ensured by using supplementary cementitious materials (SCM) with active silica content. The Rapid Migration Test (RMT) is used to measure chloride diffusion coefficient of a concrete mix that requires specific test setup and preparation of test samples. The RMT is also a destructive test and time-consuming. As a result, conducting RMT experiments and finding chloride diffusion coefficients of a concrete mix is not always feasible. In this study, an attempt has been made to develop a correlation between relevant mix design parameters and chloride diffusion coefficients of the resultant concrete using gene expression programming (GEP). GEP is an adaptive evolutionary artificial intelligence algorithm that uses evolutionary approaches on provided data and fits the data to generate an equation. Iterating the GEP program over a small set of data many times gives a good approximation. A total of 60 experimental RMT data has been utilized with mixes having wide ranges of SCM contents (fly ash and slag up to 40% level of replacement) and water to binder ratios. The developed correlation a

关键词： Reinforced concrete Corrosion Chloride diffusion coefficient gene expression programming Service life

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Comparative Evaluation of Monthly Rainfall Estimates Using gene expression programming and Multiple Linear Regression 23rd

Comparative Evaluation of Monthly Rainfall Estimates Using G...

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2022 International Association for Hydraulic Environmental Engineering and Research Asia Pacific Division

作者： Pathan, Azazkhan, I Said, Saif Sardar Vallabhbhai Natl Inst Technol Dept Civil Engn Surat Gujarat India Aligarh Muslim Univ AMU Dept Civil Engn Aligarh Uttar Pradesh India

ISBN: (纸本)9789819760084;9789819760091

Predicting rainfall, which is a major component of the hydrological cycle, is critical in water resources engineering, planning, and management, as well as in irrigation scheduling. Being an agricultural area Aligarh district in Uttar Pradesh, India, has a greater interest in developing an efficient model to predict the monthly rainfall. Therefore, this paper presents a comparative analysis of two different techniques, viz., gene expression programming (GEP) and multiple linear regression (MLR), to predict the average monthly rainfall in Aligarh district. The non-spatial average monthly weather data, including rainfall, temperature, relative humidity, wind speed, and cloud cover from 2009 to 2019, were selected for calibration and validation of the models. Results revealed that the optimized GEP model produced average monthly rainfall estimates with considerably high accuracies, yielding R-2 similar to 0.91 and 0.86, RMSE of 18.63 and 19.27, and MAE of 10.6 and 11.57 for calibration and validation data, respectively. The MLR model produced rainfall estimates with moderate R-2 similar to 0.77 and 0.73, RMSE of 22.25 and 32.27, and MAE of 15.65 and 21.76 for calibration and validation data, respectively. Analysis of the results revealed that the GEP model performs substantially well with high forecast accuracies, thereby indicating significant rationality in the optimized GEP model. Further, sensitivity analysis performed on the weather parameters showed that cloud cover exhibited the highest significance of 96.7% on rainfall estimation. The developed GEP-based model can be used by town planners and agricultural professionals.

关键词： gene expression programming Multiple linear regression Rainfall forecasting Mean absolute error Root-mean-squared error

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