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genetic and Evolutionary Computation Conference (GECCO)

作者： Sen Fong, Kei Motani, Mehul Natl Univ Singapore Dept Elect & Comp Engn Singapore Singapore Natl Univ Singapore Dept Elect & Comp Engn Inst Data Sci Inst Hlth 1Inst Digital Med WisDM Singapore Singapore

ISBN: (纸本)9798400701207

Symbolic Regression (SR) is the task of finding closed-form expressions that describe the relationship between variables in a dataset. Current SR methods tend to neglect a large portion of the search space of 'short' expressions in favor of longer expressions which are less explainable. In contrast to current SR methods, we propose to prioritize expression length over prediction performance. We do so by systematically searching through the search space of 'short' expressions, utilizing K-expressions from gene expression programming. However, the search space of 'short' expressions is large, scaling approximately exponentially with the number of variables in a dataset. To reduce the size of the search space, we propose a method, termed DistilSR, which replaces terminal symbols with weighted linear combinations of variables. We show that DistilSR exactly recovers the ground-truth equation of 16 synthetic datasets 100% of the time, outperforming 14 benchmark SR methods in SRBench. DistilSR also shows outperformance on 14 real-world datasets when compared against 14 benchmark SR algorithms and 4 benchmark non-SR algorithms from SRBench. These equations were also consistently shorter. Finally, to further enforce sparsity of weights, we propose a method of actively setting uninfluential weights to 0, achieving even shorter expressions with competitive prediction performance.

关键词： symbolic regression gene expression programming

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Modelling the edge breakout shear capacity of single anchors using gene expression programming

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NEURAL COMPUTING & APPLICATIONS 2022年第12期34卷 9635-9646页

作者： Olalusi, Oladimeji Benedict Durgapershad, Avishkar Awoyera, Paul Oluwaseun Kolawole, John Temitope Univ KwaZulu Natal Struct Engn & Computat Mech Grp SECM Dept Civil Engn Durban South Africa Covenant Univ Dept Civil Engn Ota Nigeria Obafemi Awolowo Univ Dept Bldg Ife Nigeria Loughborough Univ Sch Architecture Bldg & Civil Engn Leicester Leics England

The use of soft computing techniques is becoming more common in providing solutions to complex engineering problems such as the concrete breakout strength of anchor. Available techniques include semi-empirical equations that are known to over or underpredict and some soft computing techniques that is incapable of generating predictive equations. This study proposes a gene expression programming (GEP)-based mathematical model to predict the concrete edge breakout capacity of single anchors loaded in shear. In doing so, an experimental database compiled by the American Concrete Institute (ACI) Committee 355, containing 366 samples, was used for the model training and testing. The independent variables considered in the model development are the edge distance, anchor diameter, embedment depth and concrete strength. Moreover, the predictive performance of the developed model was compared to that of the existing models proposed in ACI 318 and the Eurocode 2 (EC2) design standards. The assessment showed that the proposed GEP-based model provided a much more uniform and accurate prediction of the actual strength than the models in the existing design standards. The proposed mathematical model is simple and robust and is expected to be very useful for evaluating the concrete breakout shear capacity of single anchors in pre-planning and pre-design phases;that is, towards inclusions in design standards.

关键词： Fastening to concrete gene expression programming Concrete edge breakout failure Edge distance Anchorages Artificial intelligence Soft computing

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gene expression programming for Estimating Shear Strength of RC Squat Wall

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BUILDINGS 2022年第7期12卷 918页

作者： Tariq, Moiz Khan, Azam Ullah, Asad Zamin, Bakht Kashyzadeh, Kazem Reza Ahmad, Mahmood Natl Univ Sci & Technol NUST Sch Civil & Environm Engn NUST Inst Civil Engn NICE Sect H-12 Islamabad 44000 Pakistan CECOS Univ IT & Emerging Sci Civil Engn Dept Peshawar 25000 Pakistan Peoples Friendship Univ Russia Acad Engn Dept Transport RUDN Univ 6 Miklukho Maklaya St Moscow 117198 Russia Univ Engn & Technol Peshawar Dept Civil Engn Bannu Campus Bannu 28100 Pakistan

The flanged, barbell, and rectangular squat reinforced concrete (RC) walls are broadly used in low-rise commercial and highway under and overpasses. The shear strength of squat walls is the major design consideration because of their smaller aspect ratio. Most of the current design codes or available published literature provide separate sets of shear capacity equations for flanged, barbell, and rectangular walls. Also, a substantial scatter exists in the predicted shear capacity due to a large discrepancy in the test data. Thus, this study aims to develop a single gene expression programming (GEP) expression that can be used for predicting the shear strength of these three cross-sectional shapes based on a dataset of 646 experiments. A total of thirteen influencing parameters are identified to contrive this efficient empirical compared to several shear capacity equations. Owing to the larger database, the proposed model shows better performance based on the database analysis results and compared with 9 available empirical models.

关键词： flanged wall barbell wall rectangular walls shear strength gene expression programming

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Modeling and monitoring the material removal rate of abrasive belt grinding based on vision measurement and the gene expression programming (GEP) algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第1-2期120卷 385-401页

作者： Ren, Lijuan Wang, Nina Pang, Wanjing Li, Yongchang Zhang, Guangpeng Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China

Accurately predicting the material removal rate (MRR) in belt grinding is challenging because of the randomly distributed multiple cutting edges, flexible contact, and continuous wear of the abrasive grains, undermining the ability to achieve the expected machining requirements for belt grinding using the planned parameters. With the development of sensing technology, big data, and intelligent algorithms, online identification methods for material removal through sensing signals have gained traction. A vision-based material removal monitoring method in the belt grinding process was investigated by adopting the gene expression programming (GEP) algorithm. First, the relationship between the grinding parameters and MRR was investigated through a series of experiments. Second, methods of image shooting distance calibration and automatic image segmentation were established. Furthermore, the definition and quantification method of 11 features related to the color, texture, and energy of spark images are described, based on which the features are extracted. Then, the optimal feature subset was determined by analyzing the fluctuation degree and correlation with MRR by computing the coefficient of variation of the features and Pearson's coefficient of features and MRR, respectively. Finally, a continuous function model including the selected features was obtained using the GEP method. The predicted results and testing time were compared with those of other methods such as LightGBM, convolutional neural network (CNN), support vector regression (SVR), and BP neural network. The results show that the MRR prediction model based on the GEP algorithm can obtain explicit function expressions and is highly effective in predicting accuracy and test time, which is of utmost significance for accurate and efficient acquisition of MRR data online.

关键词： Belt grinding Material removal rate Vision-based online monitoring gene expression programming

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Determination of compressive strength of lightweight aggregate concrete in service using gene expression programming based on semi-direct ultrasonic pulse velocity testing

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STRUCTURAL CONCRETE 2022年第4期23卷 2088-2105页

作者： Jafari, Saeed Hadianfard, Mohammad Ali Mahini, Seyed Saeed Shiraz Univ Technol Dept Civil & Environm Engn Shiraz Iran Univ Sunshine Coast Ctr Timber Durabil & Life Design Sunshine Coast Qld Australia

Structural lightweight aggregate concrete (SLWAC) can increase the ability of buildings to resist earthquakes by significantly reducing the overall weight of structures. Determining a relationship for assessing the concrete compressive strength of these structures using non-destructive methods can save both time and money. This study aims to estimate the compressive strength of SLWAC using a model of gene expression programming (GEP) and semi-direct ultrasonic pulse velocity. A hundred specimens of lightweight concrete were made using clay and natural (mineral) pumice aggregates with the maximum nominal sizes of 12.7 mm (1/2 inch) and 19.2 mm (3/4 inch), respectively. Semi-direct ultrasonic testing of the specimens was then undertaken in the laboratory. The results show a high correlation coefficient for the equations derived from the GEP. Moreover, the equations obtained from GEP for three types of SLWAC, based on semi-direct ultrasonic pulse velocity, are compatible with equations obtained from the direct ultrasonic velocity method. The simplicity of generated equations increases the practicality and convenience in the working environment and actual conditions. The R-2 (goodness of fit) values of proposed equations for LWA01, LWA02, and LWA03 specimens were 0.912, 0.962, and 0.985, respectively. It was concluded that based on the semi-direct ultrasonic pulse velocity and derived formulas, the compressive strength of SLWAC can be confidently predicted using GEP.

关键词： gene expression programming non-destructive testing prediction of compressive strength semi-direct ultrasonic pulse velocity structural lightweight aggregate concrete

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Application of gene expression programming to predict the compressive strength of quaternary-blended concrete

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Asian Journal of Civil Engineering 2023年第5期24卷 1351-1364页

作者： Raheel, Muhammad Iqbal, Mudassir Khan, Rawid Alam, Muhammad Azab, Marc Eldin, Sayed M. Department of Civil Engineering University of Engineering and Technology Mardan Pakistan Department of Civil Engineering University of Engineering and Technology Peshawar Pakistan College of Engineering and Technology American University of the Middle East Egaila 54200 Kuwait Center of Research Faculty of Engineering Future University in Egypt New Cairo Egypt

The potential impact of global warming is influencing life on planet earth. Besides other sources, production of cement is one of the major contributors of greenhouse gas emissions. Considering this, a study was undertaken to understand the influence of potential waste materials, which can replace cement and at the same time, make it economical and environment friendly. For this purpose, the influence of a quaternary blend of binders i.e., cement + 3 different pozzolanic materials on the compressive strength of concrete. Different variables such as the quantity of different binders, fine and coarse aggregate, water, superplasticizer and age of the samples were considered to study their influence on the compressive strength on the quaternary-blended concrete using gene expression programming (GEP). The number of chromosomes, genes and, the head size of GEP model was varied to study their influence on the predicted values of compressive strength. The performance of these different GEP models was also assessed using R², RMSE and comparison of regression slopes. It was observed that the model with the number of chromosomes = 150, head size = 9 and the number of genes = 3, resulted in an optimum GEP model as apparent from its high R² = 0.801 in the TR phase, and 0.800 in the TS phase, respectively. The regression slope analysis revealed that the forecasted values show good agreement with the actual values as evident from their higher R² values (≥ 0.80). Similarly, monotonicity analysis for the best performing model M6 revealed that the addition of pozzolanic materials enhanced the compressive strength of quaternary-blended concrete. It was also observed that the compressive strength of quaternary-blended concrete increased sharply within the first 28 days of casting, thus validating the practical mechanics of concrete strength gaining process. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Compressive strength gene expression programming Machine learning Parametric and sensitivity analyses Quaternary-blended concrete

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gene expression programming Model for Tribological Behavior of Novel SiC-ZrO2-Al Hybrid Composites

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MATERIALS 2022年第23期15卷 8593-8593页

作者： Abbasi, Hossein Zeraati, Malihe Moghaddam, Reza Fallah Chauhan, Narendra Pal Singh Sargazi, Ghasem Di Lorenzo, Ritamaria Islamic Azad Univ Dept Comp Engn Tangestan Branch Ahram 75541 Iran Shahid Bahonar Univ Kerman Fac Engn Dept Met & Mat Sci Kerman 14111 Iran Univ Garmsar Fac Engn Garmsar *** Iran Bhupal Nobles Univ Fac Sci Dept Chem Udaipur 313002 Rajasthan India Bam Univ Med Sci Noncommunicable Dis Res Ctr Bam *** Iran Univ Napoli Federico II Fac Med & Chirurg I-80138 Naples Italy

In order to improve product format quality and material flexibility, variety of application, and cost-effectiveness, SiC, ZrO2, and Al hybrid composites were manufactured in the research utilizing the powder metallurgy (PM) technique. A model was created to predict the tribological behavior of SiC-ZrO2-Al hybrid composites using statistical data analysis and gene expression programming (GEP) based on artificial intelligence. For the purpose of examining the impact of zirconia concentration, sliding distance, and applied stress on the wear behavior of hybrid composites, a comprehensive factor design of experiments was used. The developed GEP model was sufficiently robust to achieve extremely high accuracy in the prediction of the determine coefficient (R-2), the root mean square error (RMSE), and the root relative square error (RRSE). The maximum state of the RMSE was 0.4357 for the GEP-1 (w1) model and the lowest state was 0.7591 for the GEP-4 (w1) model, while the maximum state of the RRSE was 0.4357 for the GEP-1 (w1) model and the minimum state was 0.3115 for the GEP-3 model (w1).

关键词： gene expression programming tribological hybrid composite zirconia

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Prediction of the Height of Fracturing via gene expression programming in Australian Longwall Panels: A Comparative Study

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RUDARSKO-GEOLOSKO-NAFTNI ZBORNIK 2022年第1期37卷 91-104页

作者： Rasouli, Hadi Shahriar, Kourosh Madani, Sayyed Hasan Amirkabir Univ Technol Dept Min & Met Engn Tehran Iran

The caving and subsidence developments above a longwall panel usually result in fractures of the overburden, which decrease the strength of the rock mass and its function. The height of fracturing (HoF) includes the caved and continuous fractured zones affected by a high degree of bending. Among the various empirical models, Ditton's geometry and geology models are widely used in Australian coalfields. The application of genetic programming (GP) and gene expression programming (GEP) in longwall mining is entirely new and original. This work uses a GEP method in order to predict HoF. The model variables, including the panel width (W), cover depth (H), mining height (T), unit thickness (t), and its distance from the extracted seam (y), are selected via the dimensional analysis and Buckingham's P-theorem. A dataset involving 31 longwall panels is used to present a new nonlinear regression function. The statistical estimators, including the coefficient of determination (R-2), the average error (AE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE), are used to compare the performance of the discussed models. The R-2 value for the GEP model (99%) is considerably higher than the corresponding values of Ditton's geometry (61%) and geology (81%) models. Moreover, the maximum values of the statistical error estimators (AE, MAPE, and RMSE) for the GEP model are 12%, 14%, and 16%, respectively, of the corresponding values of Ditton's models.

关键词： Longwall mining height of fracturing gene expression programming empirical model

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gene expression programming based mathematical modeling for leak detection of water distribution networks

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MEASUREMENT 2022年 188卷 110611-110611页

作者： Tijani, I. A. Zayed, Tarek Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Kowloon Hong Kong Peoples R China

Globally, the loss of potable water through buried and service water distribution networks (WDNs) is one of the persistent challenges confronted by water utilities. Most of the WDNs inspection methods are ad-hoc and typically provide the current status of the pipes. Despite the significant research efforts, a reliable prediction method of the water leak of buried and service water distribution pipes is rare. To overcome this challenge, gene expression programming (GEP) models - that uses extracted features of acoustic signals collected from noise loggers attached to pipeline valves at the chamber - are developed and validated in the current study. To develop these models, extensive fieldwork hitherto unavailable was undertaken in this study to record the flow-induced acoustic signal of WDNs. The GEP models are developed using a step function - that returns a binary output - which is leveraged to exhibit the proposed detection and prediction methods using the acoustic signal collected from the buried WDNs. The models only consider the highly correlated features with the leakage. The models are found to be able to predict leakages on metal and nonmetal pipes with about 95% accuracy. These models were developed to reduce the time and cost of deployment of equipment for leakage detection of pipes. The proposed method presented in this study highlights the prospect and advantage of using the GEP in infrastructural management given the amazing capability of the machine intelligence technique to recognize multi-dimensional circumstances with ease, high prediction accuracy, and potential for unceasing improvement.

关键词： Acoustic signals gene expression programming Prediction model Water distribution networks Water leak detection

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A Hybrid Logistic Regression: gene expression programming Model and Its Application to Mineral Prospectivity Mapping

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NATURAL RESOURCES RESEARCH 2022年第4期31卷 2041-2064页

作者： Xiao, Fan Chen, Weilin Wang, Jun Erten, Oktay Sun Yat Sen Univ Sch Earth Sci & Engn Zhuhai 519000 Peoples R China Guangdong Key Lab Geol Proc & Mineral Resources Zhuhai 519000 Peoples R China Guangdong Prov Key Lab Geodynam & Geohazards Zhuhai 519000 Peoples R China Southern Lab Ocean Sci & Engn Zhuhai 519000 Peoples R China Univ Alberta Ctr Computat Geostat Edmonton AB Canada

Mineral prospectivity mapping (MPM) is a fundamental task in mineral exploration. The logistic regression (LR) method has been widely used as a data-driven tool for MPM because it is compact and straightforward, and it presents explicit explanations. However, there exists the inherent deficiency of linear assumption in the ordinary LR method. In order to overcome this disadvantage, a hybrid model (i.e., hybrid GEP-LR) that combines LR with gene expression programming (GEP) has been developed. It uses the evolutionary algorithm of GEP to discover automatically the symbolic function defined in the logit transformation of the ordinary LR method without any assumption. To investigate the performance of the hybrid GEP-LR hybrid method, a synthetic data modeling and a case study of prospectivity mapping for porphyry Cu-Mo polymetallic deposits (PCMPDs) in the Eastern Tianshan region, northwestern China are presented here. In the synthetic data modeling, two different datasets, namely linearly separable dataset and nonlinearly separable dataset, were generated by Monte Carlo simulation. By defining the area under the receiver operating characteristic curve (AUC) as a fitness function, these two datasets were used to validate the efficiency of the hybrid GEP-LR method in classification of both linearly and nonlinearly separable datasets. In the case study, 15 hybrid GEP-LR models were designed using 15 different evolutionary strategies of the GEP algorithm including the number of genes, the head length of every gene, and the set of functions. Binary maps of prospectivity for PCMPDs derived using these 15 hybrid GEP-LR models with the fitness function of AUC were compared and contrasted with the one derived using the ordinary LR method. The results demonstrate that the proposed hybrid GEP-LR method is a robust data-driven tool that can deal with both linear and nonlinear problems in data mining. It outperforms the ordinary LR method in predicting target areas for mineral pro

关键词： Logistic regression gene expression programming Mineral prospectivity mapping Porphyry deposits Eastern Tianshan

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