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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Anjum, Aftab Islam, Mazharul Wang, Lin Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China

ISBN: (纸本)9781728124858

multi-expression programming (MEP) encodes multiple genes through linear representation and is a widely useful technique for tangible applications like classification, symbolic regression and digital circuit designing. MEP uses only two genetic operators (mutation, crossover) to explore the search space and exploit genetic materials. However, after going through multiple generations and due to its naturally inspired fitness-based selection procedure, MEP significantly reduces genetic diversity in the population and ultimately produces homogeneous individuals;hence, leading to poor convergence and an ultimate fall into the local minimum. Gene-permutation, the newly proposed Probabilistic Genetic Operator, breakouts the homogeneity by rearranging and inducing new genetic materials in the individuals which in turn maintains the healthy genetic diversity in the population. Moreover, it also assists other genetic operators to produce more effective chromosomes and fully explore the search space. The experiments point out that Gene-permutation improves training efficiency as well as reduces test errors on several well-known symbolic regression problems.

关键词： multi-expression programming genetic operator symbolic regression genetic programming evolutionary algorithm

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A hybrid model for supplier selection: integration of AHP and multi expression programming (MEP)

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NEURAL COMPUTING & APPLICATIONS 2017年第3期28卷 499-504页

作者： Fallahpour, Alireza Olugu, Ezutah Udoncy Musa, Siti Nurmaya Univ Malaya Fac Engn Dept Mech Engn Kuala Lumpur Malaysia

Supplier evaluation and selection is a complicated process which deals with conflicting attributes such as quality, cost. To mitigate the computational complexity, intelligent-based techniques have gained much popularity. But the main shortcoming of the existing models in this regard is to be a black box system. In this paper, we aim to combine analytical hierarchy process with multi-expression programming to both introduce a new evolutionary approach in the field of supplier evaluation and selection and cope with the earlier problem. To show the validity of the model, statistical test was carried out. The finding showed that the proposed model is accurate and acceptable for using in the evaluation process.

关键词： Supplier selection Black box multi-expression programming AHP Supply chain management

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multi-objective breast cancer classification by using multi-expression programming

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APPLIED INTELLIGENCE 2015年第3期43卷 499-511页

作者： Diosan, Laura Andreica, Anca Univ Babes Bolyai Dept Comp Sci R-3400 Cluj Napoca Romania

Despite many years of research, breast cancer detection is still a difficult, but very important problem to be solved. An automatic diagnosis system could establish whether a mammography presents tumours or belongs to a healthy patient and could offer, in this way, a second opinion to a radiologist that tries to establish a diagnosis. We therefore propose a system that could contribute to lowering both the costs and the work of an imaging diagnosis centre of breast cancer and in addition to increase the trust level in that diagnosis. We present a multi-objective evolutionary approach based on multi-expression programming-a linear Genetic programming method-that could classify a mammogram starting from a raw image of the breast. The processed images are represented through Histogram of Oriented Gradients and Kernel Descriptors since these image features have been reported as being very efficient in the image recognition scientific community and they have not been applied to mammograms before. Numerical experiments are performed on freely available datasets consisting of normal and abnormal film-based and digital mammograms and show the efficiency of the proposed decision support system.

关键词： multi-objective optimization Genetic programming multi-expression programming Breast cancer

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MREP: multi-Reference expression programming 12th

MREP: Multi-Reference Expression Programming

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12th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Qingke Meng, Xiangxu Yang, Bo Liu, Weiguo Shandong Univ Minist Educ Engn Res Ctr Digital Media Technol Sch Comp Sci & Technol Jinan 250101 Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China

ISBN: (纸本)9783319422947;9783319422930

MEP is a variant of genetic program applied to solve the symbol regression and classification problems. It can encode multiple solutions of a problem in a single chromosome. However, when the ratio of genes reuse is low, it may not get a high accuracy result within limited iterations and may fall into the trap of local optimum. Therefore, we proposed a novel genetic evolutionary algorithm named MREP (multi-reference expression programming). The MREP chromosome is encoded in a two-dimensional structure and each gene in one chromosome can refer other sub-layer's gene randomly. The main contribution can be described as follows: Firstly, a novel chromosome encoding scheme is proposed based on a two-dimensional structure. Secondly, two different cross-layer reference strategies are designed to enhance the code reuse of genes located at different layers in one chromosome. Two groups experiments were conducted on eight symbol regression functions. The statistical results reveal that the MREP performs better than the compared algorithms and can solve the symbol regression functions problem efficiently.

关键词： multi-expression programming Cross-layer-reference Two dimensional operators Genetic programming Symbol regression

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Data-driven evolutionary programming for evaluating the mechanical properties of concrete containing plastic waste

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CASE STUDIES IN CONSTRUCTION MATERIALS 2024年 21卷

作者： Asif, Usama Javed, Muhammad Faisal Alsekait, Deema Mohammed Aslam, Fahid Elminaam, Diaa Salama Abd Nazarbayev Univ Dept Civil Engn Astana Kazakhstan COMSATS Univ Islamabad Dept Civil Engn Abbottabad Campus Abbottabad 22060 Pakistan Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi Pakistan Western Caspian Univ Baku Azerbaijan Princess Nourah Bint Abdulrahman Univ Appl Coll Dept Comp Sci & Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Engn Al Kharj Dept Civil Engn Al Kharj 11942 Saudi Arabia Middle East Univ MEU Res Unit Amman 11831 Jordan Jadara Univ Jadara Res Ctr Irbid 21110 Jordan

Plastic waste (PW) has emerged as a global environmental concern due to its detrimental impact on ecosystems and human health. Traditional concrete heavily relies on natural aggregates like sand, gravel, and crushed stone, whose extraction leads to environmental degradation, including habitat destruction and resource depletion. Recently, the use of PW in concrete has gained attention as a sustainable alternative to these conventional aggregates. By incorporating PW as a partial replacement for natural aggregates, the construction industry can reduce its reliance on finite resources while also addressing the issue of PW. However, despite its potential environmental benefits, the incorporation of PW into concrete has primarily been explored through experimental studies, which are often time-consuming and resource-intensive. Therefore, this study aims to optimize the utilization of waste plastic in concrete through machine learning (ML) techniques, specifically multi-expression programming (MEP) and Gene expression programming (GEP). A comprehensive literature review was conducted to compile a database for evaluating the compressive strength (CS) and tensile strength (TS) of PW concrete. The most influential parameters, such as plastic (P), gravel (G), water (W), cement (C), sand (S), and age (A), were considered as inputs in the models' development. The models developed were thoroughly evaluated using multiple statistical measures. Additionally, sensitivity analysis was conducted to discern and highlight influential factors that have a significant impact on the predicted outcomes. The findings indicate that both MEP (CS_R-2 = 0.88, and TS_R-2 = 0.89) and GEP (CS_R-2 = 0.87, and TS_R-2 = 0.88) models performed well, with MEP demonstrating slightly superior performance. Sensitivity analysis highlights the significant influence of cement (25.63 % and 24.53 %) and plastic (22.4 % and 23.44 %) on concrete strength properties. Furthermore, the equations provided by GEP and

关键词： Plastic waste concrete Compressive strength Gene expression programming multi-expression programming Tensile strength Sustainable development

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multi-expression programming based model for prediction of formation enthalpies of nitro-energetic materials

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EXPERT SYSTEMS 2013年第1期30卷 66-78页

作者： Bagheri, Mehdi Gandomi, Amir Hossein Bagheri, Mehrdad Shahbaznezhad, Mohcen Islamic Azad Univ Sci & Res Branch Tehran Iran Islamic Azad Univ Cent Tehran Branch Tehran Iran NIOC ICOFC Dept Engn Tehran Iran

There has been considerable interest in predicting the properties of nitro-energetic materials to improve their performance. Not to mention insightful physical knowledge, computational-aided molecular studies can expedite the synthesis of novel energetic materials through cost reduction labours and risky experimental tests. In this paper, quantitative structureproperty relationship based on multi-expression programming employed to correlate the formation enthalpies of frequently used nitro-energetic materials with their molecular properties. The simple yet accurate obtained model is able to correlate the formation enthalpies of nitro-energetic materials to their molecular structure with the accuracy comparable to experimental precision.

关键词： nitro-energetic materials multi-expression programming formation enthalpy QSPR

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Development of predictive models for sustainable concrete via genetic programming-based algorithms

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JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T 2023年 24卷 6391-6410页

作者： Chen, Lingling Wang, Zhiyuan Khan, Aftab Ahmad Khan, Majid Javed, Muhammad Faisal Alaskar, Abdulaziz Eldin, Sayed M. Bengbu Univ Sch Civil & Hydraul Engn Bengbu 233030 Peoples R China Anhui Univ Finance & Econ Sch Management Sci & Engn Bengbu 233030 Peoples R China Hanyang Univ Dept Civil & Environm Engn Seoul South Korea Creat Engn Consultants Peshawar Pakistan COMSATS Univ Islamabad Abbottabad Campus Abbottabad Pakistan King Saud Univ Coll Engn Dept Civil Engn Riyadh 11451 Saudi Arabia Future Univ Egypt Fac Engn Ctr Res New Cairo 11835 Egypt

Waste foundry sand (WFS), a by-product of the casting industry, is a potential material that may be employed as a substitute for fine aggregate in concrete. In the present study, gene expression programming (GEP) and multi-expression programming (MEP) are used to generate predictive models for the split tensile strength (STS) and elastic modulus (E) of waste foundry sand concrete (WFSC). Therefore, a comprehensive database was collected that contains 146 and 242 values of E and STS, respectively. Seven different variables were chosen as input for the development of the ML-based models. The reliability and accuracy of the proposed model were evaluated by using various statistical indicators. Given the performance assessment, both GEP and MEP accurately predict the E with a correlation of 0.994 and 0.996, respectively. However, GEP performance was much superior in predicting STS (R 1/4 0.987) as compared to the MEP model (R 1/4 0.892). The integrated statistical performance (r, OF) of both models approaches zero, indicating the excellent performance and generalization potential of the developed models. For the interpretation of machine learning (ML) models, Shapley additive explanation (SHAP) was used to know about the input variables' importance and influence on the output parameter. The SHAP analysis revealed that a higher ratio of FA/TA results in the enhancement of the elastic modulus, whereas CA/C higher ratio is favorably influencing the split tensile strength up to some extent, however, this trend changes when the ratio is further increased. These soft computing prediction techniques can incentivize the use of WFS in sustainable concrete, reducing waste disposal and promoting environment-friendly construction. Furthermore, it is recommended that the findings of this study be validated with more extensive data sets and that other ML techniques be investigated. & COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY li

关键词： Waste foundry sand Gene expression programming multi-expression programming Solid waste Sustainable construction

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Analytical and innovative modeling investigations on the performance of nanoparticle-modified self-compacting mortars

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EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2025年第5期29卷 881-900页

作者： Faraj, Rabar H. Ahmed, Hemn Unis Rafiq, Serwan Khwrshid Sor, Nadhim Hamah Univ Halabja Civil Engn Dept Halabja Kurdistan Regio Iraq Komar Univ Sci & Technol Civil Engn Dept Sulaimani Kurdistan Regio Iraq Univ Sulaimani Coll Engn Civil Engn Dept Sulaimani Kurdistan Regio Iraq Univ Garmian Civil Engn Dept Kalar Kurdistan Regio Iraq

The transition of a material from macro- to Nano-scale brings about significant changes in electron conductivity, optical absorption, mechanical properties, chemical reactivity, and surface morphology. These changes present opportunities for creating innovative composite mixtures. As there is a growing need for improved infrastructure, it becomes crucial to develop new, high-performance materials. To enhance the performance of concrete mixtures, various methods have been explored, including the utilization of nanoparticles (NPs). Incorporating NPs aims to improve the fresh and mechanical properties of self-compacting concrete (SCC) while also enhancing the permeability and absorption capacity of the composite by introducing extremely fine particles to fill micro-pores and voids. Numerous initiatives have been implemented to explore the mechanical characteristics of SCC. Typically, compressive strength (CS) serves as a crucial mechanical parameter for assessing concrete quality. Conventional methods for determining SCC's CS are costly, time-intensive, and restrictive due to the intricate interplay of various mixing proportions and curing processes. Thus, this investigation employs machine learning techniques, including artificial neural network (ANN), multi-expression programming (MEP), full quadratic (FQ), and linear regression (LR), to predict self-compacting mortar's CS. Approximately 292 CS values from the literature were extracted and analyzed to facilitate model development. Six influential variables were used as input parameters and one as an output during the modeling process. Four statistical metrics gauged model performance, and sensitivity analysis was conducted. Results indicate that the ANN model outperformed other models in predicting self-compacting mortar's CS. Meanwhile, the water-to-binder ratio, nanoparticle dosage, and concrete age significantly influence self-compacting mortar's CS.

关键词： Self-compacting mortar nanoparticles modeling artificial neural network multi-expression programming

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Estimation of Soil Water Characteristic Curve Using Machine-Learning Algorithms and Its Application in Embankment Response

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JOURNAL OF COMPUTING IN CIVIL ENGINEERING 2025年第3期39卷

作者： Showkat, Rakshanda Jalal, Fazal E. Babu, G. L. Sivakumar Indian Inst Sci Dept Civil Engn Bengaluru 560012 Karnataka India King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Construct & Bldg Mat Dhahran 31261 Saudi Arabia

The parameters of the soil water characteristic curve (SWCC) play a pivotal role in the examination of unsaturated soil behavior. This study employs three machine learning models-random forest (RF), extreme gradient boosting (XGBoost), and multiexpression programming (MEP)-to predict the SWCC using key soil properties. Among them, the RF model demonstrated the most robust performance in SWCC prediction. The Shapley Additive Explanation (SHAP) analysis further reveals that suction is the most influential factor affecting SWCC predictions, with other input parameters also contributing significantly. Additionally, the MEP model offers a straightforward expression for SWCC estimation and, thus, proved practical for predicting embankment responses and exhibited superior accuracy over traditional methods, such as the Arya and Paris model (ACAP). For a precise assessment of the hydromechanical response of the embankment subjected to infiltration, an increase in pore pressure is observed when employing the MEP model compared to the ACAP model for fine-grained soils. The findings emphasize the potential of RF and MEP in enhancing SWCC prediction and their practical implications for soil engineering applications.

关键词： Soil water characteristic curve Partially saturated soils multi-expression programming Extreme gradient boosting Random forest

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Evaluating the impact of data preprocessing to develop a robust MEP-based forecasting model for building integrated with PCM

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ENERGY 2025年 324卷

作者： Nazir, Kashif Memon, Shazim Ali Nazarbayev Univ Sch Engn & Digital Sci Dept Civil & Environm Engn Nur Sultan Kazakhstan

Data quality is a crucial aspect to accurately predict the energy use of buildings utilizing machine learning methods. Data preprocessing can ensure data quality when a database does not match the criteria for evolving a robust prediction model. Regarding phase change material (PCM)-incorporated buildings, there was no study before this research evaluating the impact of data preprocessing for establishing a robust machine learning based model to forecast their energy consumption (EC). Therefore, for the first time, this research presents an application of the data preprocessing process to compare the results of the formulated multi-expression programming (MEP)-based prediction model's accuracy for predicting the EC of PCM-integrated buildings using processed with actual databases. Data cleaning, outlier detection and removal, and data smoothing were performed on the actual EC database during the data preprocessing process. Results of model evaluation and validation processes for the articulated prediction models showed that the data preprocessing improved the MEP-based prediction model by 33 % to predict the EC precisely. Conclusively, model interpretability (sensitivity, parametric, and energy saving analysis) demonstrated that the developed more reliable prediction model provides energy savings of approximately 20 % by integrating optimum PCM.

关键词： Phase change material Data preprocessing multi-expression programming Energy consumption Model interpretability

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