检索结果-内蒙古大学图书馆

Integrated Fuzzy System and multi-expression programming Techniques for Supplier Selection

TEHNICKI VJESNIK-TECHNICAL GAZETTE 2019年第1期26卷 122-127页

作者： Paunovic, Marija Ralevic, Nebojsa Milutinovic, Olivera Vojinovic, Zeljko Mladenovic-Vojinovic, Biljana Univ Novi Sad Fac Tech Sci Dept Fundamentals Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Megatrend Univ Fac Law Bulevar Marsala Tolbuhina 8 Novi Beograd 11070 Serbia Univ Novi Sad Fac Econ Subotica Segedinski Put 9-11 Subotica 24000 Serbia

Supplier selection problem is a multi-objective problem in which different criteria should be taken into consideration. This article presents a new approach to supplier pre-qualification, supplier selection and evaluation. In the first stage of the model, multi-expression programming (MEP) techniques are used for a supplier pre-qualification. Techniques implemented in MEP allow construction of experiential models using the knowledge contained in the experimental information. Evaluation of the qualified suppliers is done in the second stage using fuzzy logic and Fuzzy Inference System (FIS). In this way, it is possible to retain expert knowledge of the subject phenomenon in a model with the possibility of selecting different operators which lead to the possibility of the faster selection of parameters and making more reliable decisions. Numerical examples are presented to demonstrate the proposed approach.

关键词： fuzzy system multi-expression programming supplier evaluation

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Gene Permutation: A new Probabilistic Genetic Operator for Improving multi expression programming

Gene Permutation: A new Probabilistic Genetic Operator for I...

引用

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

来源：评论

学校读者我要写书评

暂无评论

MREP: multi-Reference expression programming 12th

MREP: Multi-Reference Expression Programming

引用

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

来源：评论

学校读者我要写书评

暂无评论

Consolidation assessment using multi expression programming

引用

APPLIED SOFT COMPUTING 2020年 86卷

作者： Sharifi, Sohrab Abrishami, Saeed Gandomi, Amir H. Univ Alberta Dept Civil & Environm Engn Edmonton AB T6G 1H9 Canada Ferdowsi Univ Mashhad Dept Civil Engn Fac Engn Mashhad 917751111 Razavi Khorasan Iran Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia

In this study, new approximate solutions for consolidation have been developed in order to hasten the calculations. These solutions include two groups of equations, one can be used to calculate the average degree of consolidation and the other one for computing the time factor (inverse functions). Considering the complicated nature of consolidation, an evolutionary computation technique called multi-expression programming was applied to generate several non-piecewise models which are accurate and straightforward enough for different purposes for calculating the degree of consolidation for each depth and its average as well for the whole soil layer. The parametric study was also performed to investigate the impact of each input parameter on the predicted consolidation degree of developed models for each depth. Moreover, the results of the consolidation test carried out on four different clays attained from the literature showed the proper performance of the proposed models. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Geotechnical engineering Evolutionary computation multi-expression programming Consolidation Prediction

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Robust genetic programming-based detection of atrial fibrillation using RR intervals

引用

EXPERT SYSTEMS 2012年第2期29卷 183-199页

作者： Yaghouby, Farid Ayatollahi, Ahmad Bahramali, Reihaneh Yaghouby, Maryam Iran Univ Sci & Technol Sch Elect Engn Tehran Iran Univ Appl Sci & Technol Unit Dadeh Pardazi Iran DPI Tehran Iran Azad Univ Mashhad Fac Engn Mashhad Iran Iran Univ Sci & Technol Sch Elect & Elect Engn Tehran Iran Univ Kentucky Ctr Biomed Engn Wenner Gren Res Lab Lexington KY 40506 USA

In this study, two variants of genetic programming, namely linear genetic programming (LGP) and multi-expression programming (MEP) are utilized to detect atrial fibrillation (AF) episodes. LGP- and MEP-based models are derived to classify samples of AF and Normal episodes based on the analysis of RR interval signals. A weighted least-squares (WLS) regression analysis is performed using the same features and data sets to benchmark the models. Another important contribution of this paper is identification of the effective time domain features of heart rate variability (HRV) signals upon an improved forward floating selection (IFFS) analysis. The models are developed using MIT-BIH arrhythmia database. The diagnostic performances of the LGP and MEP classifiers are evaluated through receiver operating characteristics (ROC) analysis. The results indicate that the LGP and MEP models are able to diagnose the AF arrhythmia with an acceptable high accuracy. The proposed models have significantly better diagnosis performances than the regression and several models found in the literature.

关键词： atrial fibrillation heart rate variability signal linear genetic programming multi-expression programming forward floating selection arrhythmia detection

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：