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Web music emotion recognition based on higher effective gene expression programming

NEUROCOMPUTING 2013年 105卷 100-106页

作者： Zhang, Kejun Sun, Shouqian Zhejiang Univ Coll Comp Sci Hangzhou 310027 Peoples R China

In the study, we present a higher effective algorithm, called revised gene expression programming (RGEP), to construct the model for music emotion recognition. Our main contributions are as follows: firstly, we describe the basic mechanisms of music emotion recognition and introduce gene expression programming (GEP) to deal with the model construction for music emotion recognition. Secondly, we present RGEP based on backward-chaining evolutionary algorithm and use GEP, RGEP, and SVM to construct the models for music emotion recognition separately, the results show that the models obtained by SVM, GEP, and RGEP are satisfactory and well confirm the experimental values. Finally, we report the comparison of these models, and we find that the model obtained by RGEP outperforms classification accuracy of the model by GEP and takes almost 15% less processing time of GEP and even half processing time of SVM, which offers a new efficient way for solving music emotion recognition problems;moreover, because processing time is essential for the problem of large scale music information retrieval, therefore, RGEP might prompt the development of the music information retrieval technology. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Music emotion recognition Evolutionary algorithm gene expression programming Support vector machine Music information retrieval

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Correlation of Pile Axial Capacity and CPT Data Using gene expression programming

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GEOTECHNICAL AND GEOLOGICAL ENGINEERING 2011年第5期29卷 725-748页

作者： Alkroosh, I. Nikraz, H. Curtin Univ Technol Dept Civil Engn Bentley Campus Perth WA 6845 Australia

Numerous methods have been proposed to assess the axial capacity of pile foundations. Most of the methods have limitations and therefore cannot provide consistent and accurate evaluation of pile capacity. However, in many situations, the methods that correlate cone penetration test (CPT) data and pile capacity have shown to provide better results, because the CPT results provide more reliable soil properties. In an attempt to obtain more accurate correlation of CPT data with axial pile capacity, gene expression programming (GEP) technique is used in this study. The GEP is a relatively new artificial intelligent computational technique that has been recently used with success in the field of engineering. Three GEP models have been developed, one for bored piles and two other models for driven piles (a model for each of concrete and steel piles). The data used for developing the GEP models are collected from the literature and comprise a total of 50 bored pile load tests and 58 driven pile load tests (28 concrete pile load tests and 30 steel pile load tests) as well as CPT data. For each GEP model, the data are divided into a training set for model calibration and an independent validation set for model verification. The performances of the GEP models are evaluated by comparing their results with experimental data and the robustness of each model is investigated via sensitivity analyses. The performances of the GEP models are evaluated further by comparing their results with the results of number of currently used CPT-based methods. Statistical analyses are used for the comparison. The results indicate that the GEP models are robust and perform well.

关键词： Pile Axial capacity Correlation Cone penetration test gene expression programming Training and validation

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Prediction of essential proteins based on gene expression programming

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BMC GENOMICS 2013年第Sup4期14卷 1-8页

作者： Zhong, Jiancheng Wang, Jianxin Peng, Wei Zhang, Zhen Pan, Yi Cent S Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China Hunan Normal Univ Dept Elect & Informat Engn Changsha 410083 Hunan Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

Background: Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships. Results: In this work, we propose a GEP-based method to predict essential protein by combing some biological features and topological features. We carry out experiments on S. cerevisiae data. The experimental results show that the our method achieves better prediction performance than those methods using individual features. Moreover, our method outperforms some machine learning methods and performs as well as a method which is obtained by combining the outputs of eight machine learning methods. Conclusions: The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.

关键词： Positive Predictive Value Negative Predictive Value Betweenness Centrality Machine Learning Method gene expression programming

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Spatial prediction of rainfall-induced shallow landslides using gene expression programming integrated with GIS: a case study in Vietnam

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NATURAL HAZARDS 2018年第3期92卷 1871-1887页

作者： Nhat-Duc Hoang Dieu Tien Bui Duy Tan Univ Fac Civil Engn Inst Res & Dev R 8093 Quang Trung Da Nang 550000 Vietnam Univ Coll Southeast Norway Dept Business & IT Geog Informat Syst Grp Gulbringvegen 36 N-3800 Bo I Telemark Norway

Shallow landslide represents one of the most devastating morphodynamic processes that bring about great destructions to human life and infrastructure. Landslide spatial prediction can significantly help government agencies in land use and mitigation measure planning. Nevertheless, landslide spatial modeling remains a very challenging problem due to its inherent complexity. This study proposes an integration of geographical information system (GIS) and gene expression programming (GEP) for predicting rainfall-induced shallow landslide occurrences in Son La Province, Vietnam. A landslide inventory map has been constructed based on historical landslide locations. Furthermore, a dataset which features 12 influencing factors is collected using GIS technology. Based on the GEP algorithm and the collected dataset, an empirical model for spatial prediction of the shallow landslide has been established by means of natural selection. The predictive capability of the model has been verified by the area under the curve calculation. Experimental results point out that the newly proposed approach is a promising tool for shallow landslide prediction.

关键词： Shallow landslide Rainfall-induced gene expression programming Geographical information system Artificial intelligence

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Comparison Between gene expression programming and Traditional Models for Estimating Evapotranspiration under Hyper Arid Conditions

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WATER RESOURCES 2016年第2期43卷 412-427页

作者： Yassin, Mohamed A. Alazba, Abdulrahman A. Mattar, Mohamed A. King Saud Univ Alamoudi Water Chair POB 2460 Riyadh 11451 Saudi Arabia King Saud Univ Coll Food & Agr Sci Dept Agr Engn POB 2460 Riyadh 11451 Saudi Arabia Agr Res Ctr AEnRI POB 256 Giza Egypt

gene expression programming (GEP) was used to develop new mathematical equations for estimating daily reference evapotranspiration (ETref) for the Kingdom of Saudi Arabia. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The GEP models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman-Monteith model was used as a reference target for evapotranspiration (ET) values, with h(c) varies from 5 to 105 cm with increment of a centimetre. Eight GEP models have been compared with four locally calibrated traditional models (Hargreaves-Samani, Irmak, Jensen-Haise and Kimberly-Penman). The results showed that the statistical performance criteria values such as determination coefficients (R-2) ranged from as low as 64.4% for GEP-MOD1, where the only parameters included (maximum, minimum, and mean temperature and crop height), to as high as 95.5% for GEP-MOD8 with which all climatic parameters included (maximum, minimum and mean temperature;maximum, minimum and mean humidity;solar radiation;wind speed;and crop height). Moreover, an interesting founded result is that the solar radiation has almost no effect on ETref under the hyper arid conditions. In contrast, the wind speed and plant height have a great positive impact in increasing the accuracy of calculating ETref. Furthermore, eight GEP models have obtained better results than the locally calibrated traditional ETref equations.

关键词： arid conditions reference evapotranspiration gene expression programming traditional models

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Investigating the effects of feeding properties on rock breakage by jaw crusher using response surface method and gene expression programming

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ADVANCED POWDER TECHNOLOGY 2021年第5期32卷 1521-1531页

作者： Koken, Ekin Lawal, Abiodun Ismail Abdullah Gul Univ Nanotechnol Engn Dept TR-38170 Kayseri Turkey Fed Univ Technol Akure Dept Min Engn dPMB 704 Akure Nigeria

The present study investigates the effects of feeding properties on rock comminution by a laboratory-scale jaw crusher. For this purpose, detailed crushability tests were carried out on four different rock types to assess their degree of rock crushability (DRC). Various feeding sizes (9.5 - 19 mm) and quantities (500 - 1500 g) were adopted to reveal the choke feeding intensity during crushing actions. The efficiency of feeding properties was investigated through the response surface methodology (RSM). The RSM results demonstrated that the characterized feeding size (F-80, mm) dominates the general size reduction, whereas the feeding quantity (m(f), g) is associated with the crushing energy consumption and product flakiness. Therefore, the choke feeding intensity has a direct relation to the m(f) and F-80. In addition, novel gene expression programming (GEP) models were employed to generate empirical formulations to predict the DRC parameters. The established GEP models have a satisfactory estimation capability. Therefore, the DRC of the investigated rocks can be optimized through the proposed GEP models based on the coupling variables of m(f) and F-80. (C) 2021 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.

关键词： Jaw crusher Crushed stone Rock comminution Response surface methodology gene expression programming

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A hybrid gene expression programming model for discharge prediction

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PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-WATER MANAGEMENT 2021年第5期176卷 223-234页

作者： Li, Shicheng Yang, James KTH Royal Inst Technol Dept Civil & Architectural Engn Stockholm Sweden Vattenfall AB R&D Hydraul Lab Alvkarleby Sweden

The head-discharge relationship of an overflow weir is a prerequisite for flow measurement. Conventionally, it is determined by regression methods. With machine learning techniques, data-driven modelling becomes an alternative. However, a standalone model may be inadequate to generate satisfactory results, particularly for a complex system. With the intention of improving the performance of standard gene expression programming (GEP), a hybrid evolutionary scheme is proposed, which is coupled with grey system theory and probabilistic technique. As a gene filter, grey relational analysis (GRA) eliminates noise and simulated annealing (SA) reduces overfitting by optimising the gene weights. The proposed GEP-based model was developed and validated using experimental data of a submerged pivot weir. Compared with standalone GEP, the GRA-GEP-SA model was found to generate more accurate results. Its coefficients of determination and correlation were improved by 3.6% and 1.7%, respectively. The root mean square error was lowered by 24.8%, which is significant. The number of datasets with an error of less than 10% and 20% was increased by 15% and 12%, respectively. The proposed approach outperforms classic genetic programming and shows a comparative error level with the empirical formula. The hybrid procedure also provides a reference for applications in other hydraulic issues.

关键词： data-driven modelling flow measurement gene expression programming grey relational analysis hydraulics & hydrodynamics mathematical modelling simulated annealing waterways & canals

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Settlement prediction of the rock-socketed piles through a new technique based on gene expression programming

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NEURAL COMPUTING & APPLICATIONS 2018年第11期29卷 1115-1125页

作者： Armaghani, Danial Jahed Faradonbeh, Roohollah Shirani Rezaei, Hossein Rashid, Ahmad Safuan A. Amnieh, Hassan Bakhshandeh Amirkabir Univ Technol Dept Civil & Environm Engn Hafez Ave Tehran 15914 Iran Tarbiat Modares Univ Dept Min Fac Engn Tehran 14115143 Iran Lorestan Univ Dept Civil Engn Fac Engn Lorestan Iran UTM Dept Geotech & Transportat Fac Civil Engn Skudai 81310 Johor Malaysia Univ Tehran Sch Min Coll Engn Tehran 111554563 Iran

The settlement design of bored piles socketed into rock has received considerable attention. Although many design methods of pile settlement are recommended in the literature, proposing new/practical technique(s) with higher performance prediction is of advantage. A new model based on gene expression programming (GEP) is presented in this paper for predicting the settlement of the rock-socketed pile. To do this, 96 piles socketed in different types of rock (mostly granite) as part of the Klang Valley Mass Rapid Transit project, Malaysia, were studied. In order to propose a predictive model with higher performance prediction, a series of GEP analyses were conducted using the most important factors on pile settlement, i.e. ratio of length in soil layer to length in rock layer, ratio of total length to diameter, uniaxial compressive strength, standard penetration test and ultimate bearing capacity. For comparison purpose, using the same dataset, linear multiple regression (LMR) technique was also performed. After developing the equations, their prediction performances were checked through several performance indices. The results demonstrated the feasibility of GEP-based predictive model of settlement. Coefficients of determination (CoD) values of 0.872 and 0.861 for training and testing datasets of GEP equation, respectively, show superiority of this model in predicting pile settlement while these values were obtained as 0.835 and 0.751 for the LMR model. Moreover, root mean square error (RMSE) values of (1.293 and 1.656 for training and testing) and (1.737 and 1.767 for training and testing) were achieved for the developed GEP and LMR models, respectively.

关键词： Rock-socketed pile Settlement gene expression programming Linear multiple regression

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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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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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