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Multivarible Symbolic Regression Based on gene expression programming

Multivarible Symbolic Regression Based on Gene Expression Pr...

2011 Fourth International Symposium on Computational Interlligence and Design [第四届计算智能与设计国际会议 ISCID 2011]

作者： Guang-ping Zhu Ming-fang Zhu Yu Pan Yan-ling Ren Jian-bin Zhang School of Computer Engineering Jiangsu Teachers University of Technology Changzhou China School of Electronic Infomatuion Jiangsu Teachers University of Technology Changzhou China

This paper presents a method for multivarible symbolic regression modeling and predicting. The method based on gene expression programming, a recently proposed evolutionary computation technique. We explain in details the techniques of gene expression programming and multivarible symbolic regression with gene expression programming. Furthermore, we give an example to explain this technique, and experiment results show that the model set up by gene expression programming is better than statistiacal linear regression techniques.

关键词： gene expression programming multiable symbolic regression autimatic modeling evolutionary computation

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Multi-expression Based gene expression programming

Multi-Expression Based Gene Expression Programming

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2013年中国智能自动化学术会议

作者： Wei Deng Pei He Zhi Huang School of Computer and Communication Engineering Changsha University of Science and Technology Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology

Among the variants of GP,GEP stands out for its simplicity of encoding method and MEP catches our attention for its multi-expression *** this paper,a novel GP variant-MGEP(Multi-expression based gene expression programming) is proposed to combine these two *** new method preserves the GEP structure,however unlike the traditional GEP,its genes,like those of MEP,can be disassembled into many *** in MGEP,the traditional GEP gene can contain multiple solutions for a *** experimental result shows the MGEP is more effective than the traditional GEP and MEP in solving problems.

关键词： genetic programming gene expression programming Multi expression programming

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Students Creativity Modeling with gene expression programming

Students Creativity Modeling with Gene Expression Programmin...

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2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

作者： Jinxin Qian Jiayuan Yu Department of Psychology Nanjing Normal University Nanjing 210097 China

Williams Creativity Test B (WCTB) and Adolescent Scientific Creativity Scale (ASCS) were used to measure the creative affective and scientific creativity for 550 middle school students. In these students, 70％ of them were selected to be as training samples, and the others to be as testing samples. gene expression programming (GEP), generalized regression neural network (GRNN) and multivariable linear regression (MLR) were used for modeling and testing. The result showed the fitting error of GEP model was the lowest compared with the errors of GRNN and MLR models.

关键词： gene expression programming neural networks creativity modeling

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Automatic Rule Identification for Agent-Based Crowd Models Through gene expression programming 14

Automatic Rule Identification for Agent-Based Crowd Models T...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Jinghui Zhong Linbo Luo Wentong Cai Michael Lees School of Computer Engineering Nanyang Technological University Section Computational Science University of Amsterdam

ISBN: (纸本)9781634391313

Agent-based modelling of human crowds has now become an important and active research field, with a wide range of applications such as military training, evacuation analysis and digital game. One of the significant and challenging tasks in agent-based crowd modelling is the design of decision rules for agents, so as to reproduce desired emergent phenomena behaviors. The common approach in agent-based crowd modelling is to design decision rules empirically based on model developer's experiences and domain specific knowledge. In this paper, an evolutionary framework is proposed to automatically extract decision rules for agent-based crowd models, so as to reproduce an objective crowd behavior. To automate the rule extraction process, the problem of finding optimal decision rules from objective crowd behaviors is formulated as a symbolic regression problem. An evolutionary framework based on gene expression programming is developed to solve the problem. The proposed algorithm is tested using crowd evacuation simulations in three scenarios with differing complexity. Our results demonstrate the feasibility of the approach and shows that our algorithm is able to find decision rules for agents, which in turn can generate the prescribed macro-scale dynamics.

关键词： Algorithms Agent-Based Modelling Crowd Simulation Decision Rules Evolutionary Algorithm gene expression programming decision rule evolutionary algorithm Agent-Based Modeling algorithms Mass Behavior AUTOMATIC Agents

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The Modeling And Optimization Of Chemical Processes Using gene expression programming

The Modeling And Optimization Of Chemical Processes Using Ge...

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作者： MOHAMED HASSAN SAID OMAR 武汉理工大学

学位级别：硕士

gene expression programming (GEP) is an evolutionary algorithm that combines the characteristics of genetic algorithm and\ngenetic programming by incorporating genotype and phenotype representations in its chromosome. Although many methods exist for modeling and optimization, none can be considered universal, hence there is a never ending search for algorithms and\nmethods that can be applied in chemical process modeling and optimization.\n The main aim of this thesis is to explore the possibility of applying GEP to chemical process modeling and optimization. Having been invented in the recent past, there is a need to investigate its applicability to various fields. This thesis presents practical examples of steady-state modeling of styrene-butadiene rubber (SBR) formulation, concrete mix formulation and an ethylbenzene dehydrogenation reactor using GEP. It also presents results obtained from the global optimization of benchmark problems (both NLP and MINLP). In both cases the results obtained show that GEP can also be applied to chemical\nprocess modeling and optimization.

关键词： Chemical Processes modeling gene expression programming genetic algorithm

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Mathematical model for prediction of compressive strength of ternary blended cement concrete utilizing gene expression programming

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SCIENTIFIC AFRICAN 2023年 22卷

作者： Alabi, Stephen Adeyemi Arum, Chinwuba Adewuyi, Adekunle Philip Arum, Roland Chinwuba Afolayan, Joseph Olasehinde Mahachi, Jeffrey Univ Botswana Dept Civil Engn Gaborone Botswana Fed Univ Technol Akure Dept Civil Engn Akure Nigeria Univ Johannesburg Dept Civil Engn Technol Johannesburg South Africa

In order to encourage the utilization of rice husk ash (RHA), ceramic waste powder (CWP) and glass waste powder (GWP) in their ternary combinations in the production of concrete as well as determine the variability in the resulting compressive strength (CS), the application of softcomputing algorithms is of great interest. Therefore, this study developed a predictive model using the gene expression programming (GEP) technique. The predictive model was trained and developed using extensive and trustworthy data of the compressive strength of ternary blended cement concrete comprising four (4) input parameters, including the RHA, CWP, GWP, and curing day (CD). The model was developed based on 70 % training datasets, and the model's accuracy was checked using 15 % testing datasets;the model's outcomes were validated using 15 % experimental datasets. The performance of the developed model was further assessed by applying statistical checks, comparing regression models and sensitivity analysis. The R-values in the training, testing and validation phases of GEP models are 0.95, 0.93 and 0.89 respectively with an objective function (OF) of 0.04. Based on the optimum GEP model, a closed-form mathematical equation is presented, proving to have excellent adaptability, predictive ability and capability of accurately estimating the compressive strength of concrete made from ternary blended cement mixes. Therefore, the outcomes of this study can help the construction sector predict the properties of pozzolanic concrete and manage scarce resources.

关键词： Pozzolanic concrete Ternary binders Machine learning Waste management Prediction gene expression programming

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Research on fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming

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JOURNAL OF ALGORITHMS & COMPUTATIONAL TECHNOLOGY 2019年第13期13卷 -页

作者： Li, Wenjing Pan, Qiuxia Liang, Shiaofang Jiao, Jiang Yin Nanning Normal Univ Sch Logist Management & Engn Nanning 530299 Guangxi Peoples R China Nanning Normal Univ Coll Comp & Informat Engn Nanning Guangxi Peoples R China

Regarding the problems of insufficient image segmentation intelligence, low compression rate, slow speed for global searching to find the optimal fractal image compression encoding, and bad decoding effect, this article proposes the fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming. Firstly, according to the accurate and fast image classification of deep convolutional neural network and the fast search and matching encoding advantages of gene expression programming, it realizes theoretically the action mechanism of fractal image compression hybrid encoding by combining the convolutional neural network and the gene expression programming;then, it uses the deep convolutional neural network to train and classify the image, and uses the adaptive quadtree segmentation method to segment the classified image, thus generating the domain block and range block classification set. According to the action mechanism of gene expression programming in fractal image compression encoding, it then quickly obtains the optimal solution of fractal image compression encoding by searching and encoding the sub-blocks of range block classification set and the classification set corresponding to the domain. Finally, in the CPU/GPU environment, it conducts the comparative experiment with basic fractal image compression algorithm and fractal image compression algorithm based on convolutional neural network. The experimental results show that this proposed algorithm outperforms similar algorithms in terms of image segmentation speed and accuracy as well as fractal compression encoding speed and compression ratio. Therefore, this algorithm is a fractal image compression algorithm with intelligent segmentation, fast encoding and high compression ratio.

关键词： Fractal image convolutional neural network gene expression programming intelligent segmentation hybrid compression CPU GPU

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Short-Term Load Forecasting Based on Improved gene expression programming

Short-Term Load Forecasting Based on Improved Gene Expressio...

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4th International Conference on Circuits and Systems for Communications (ICCSC 2008), vol.2

作者： Limin Huo Jinliang Yin Lirui Guo Jie Hu Xinqiao Fan Department of Mechanical and Electronic Engineering Agricultural University of Hebei Baoding Hebei China

Short-term load forecasting is an important part of the modernized power system administration. Some methods have been applied to short-term load forecasting and obtained a certain achievement. Considering the characteristics of gene expression programming (GEP), it is possible to apply GEP to short-term load forecasting. But there still are some shortcomings of GEP. Such as the initial population that is generated randomly, mutation rate that can't be adjusted by itself and evolution result got before that can't be utilized. In order to overcome these shortcomings, GEP was improved (IGEP) in the aspects of excessive multiplication, self-adaptive mutation rate and adopting mathematical model got before is proposed. And the IGEP was applied to short-term load forecasting. Firstly, the load series of the same time but different days are chosen as the training samples. Secondly, the load samples are filtered and processed generally. And finally, the short-term load is forecasted classified by weekday and weekend after eliminating the pseudo-data. Compared with the results forecasted by means of GP and GEP, it proves that the method of IGEP in short-term load forecasting is better.

关键词： Improved gene expression programming gene expression programming Artificial intelligence Power system Short-term load forecasting

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Short-Term Load Forecasting Based on Improved gene expression programming

Short-Term Load Forecasting Based on Improved Gene Expressio...

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7th World Congress on Intelligent Control and Automation (WCICA 2008), vol.14

作者： Jinliang Yin Limin Huo Lirui Guo Jie Hu Department of Mechanical and Electronic Engineering Agricultural University of Hebei Baoding Hebei China

gene expression programming (GEP) was improved (IGEP) to overcome the shortcomings that the initial population is generated randomly, there are no standards to measure the gene, and evolution results got before can't be utilized. Quasi-law of nature that excessive multiplication, environmental factor selecting, using pheromones to measure gene, passing on from generation to generation and self-adaptive mutation rate method are proposed and that make the Improved gene expression programming (IGEP) is capable of memory and self-adaptive ability. The Improved gene expression programming (IGEP) was applied to short-term load forecasting. Firstly, the load series of the same time but different days are chosen as the training samples. Secondly, the load samples are filtered and processed generally. And finally, the short-term load is forecasted classified by weekday and weekend after eliminating the pseudo-data. Compared with the results forecasted by means of GP and GEP, it proves that the method of IGEP in short-term load forecasting is better.

关键词： gene expression programming Improved gene expression programming Power system Short-term load forecasting

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A data-driven approach to predicting multifactor-influenced flexural size effect and fracture behaviors of concrete

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ENGINEERING FRACTURE MECHANICS 2025年 315卷

作者： Ye, Junhong Uddin, Md Nasir Yu, Jie Xu, Tengfei Zhan, Yulin Zhang, Dong Weng, Yiwei Southwest Jiaotong Univ Inst Smart City & Intelligent Transportat Chengdu Peoples R China Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China Southwest Jiaotong Univ Dept Bridge Engn Chengdu Peoples R China Fuzhou Univ Coll Civil Engn Fuzhou Peoples R China Hong Kong Polytech Univ Shenzhen Res Inst Shenzhen Peoples R China

Flexural size effect, originating from the fracture characteristics of materials, is a common phenomenon in concrete. Conventionally, time-consuming and labor-intensive experiments are required to investigate the flexural size effect and fracture behaviors of concrete. To tackle the limitations, a data-driven approach was adopted to predict the multifactor-influenced flexural size effect and fracture behaviors of concrete by gene expression programming (GEP) due to its capability of addressing non-linear problems and developing empirical equations with multiple input variables. Results show that the GEP models can accurately predict nominal flexural strength (R2, 0.890) and fracture toughness (R2, 0.946). Parametric analysis reveals that the compressive strength and tensile strain capacity positively impact the nominal flexural strength and fracture toughness of concrete. Based on the GEP model, a multifactor-influenced size effect law (SEL) is proposed to predict the nominal flexural strength by incorporating both material and geometric parameters, removing the need for extensive experimental investigations. The findings provide generalized models to predict the nominal flexural strength and fracture toughness of various materials at different sizes.

关键词： Size effect Machine learning Flexural strength Fracture toughness gene expression programming

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