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检索条件"主题词=Gene Expression Programming"

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Edit Distance Based Crossover Operator in gene expression programming 8

Edit Distance Based Crossover Operator in Gene Expression Pr...

引用

8th International Conference on BioMedical Engineering and Informatics (BMEI)

作者： Li Qu Cheng Hongbing Lin, H. X. Zhejiang Univ Coll Comp Sci Hangzhou 310037 Zhejiang Peoples R China Delft Univ Technol Fac Elect Engn Math & Comp Sci Delft Netherlands

ISBN: (纸本)9781509000227

The population diversity greatly affects the evolutionary efficiency and solution quality of gene expression programming algorithm. Population diversity should be preserved by keeping certain distance between individuals in the population. Edit distance can describe the similarity of individuals well. Crossover is a way to create and maintain the distance of the individuals. In this paper, we propose two edit distance based crossover operators. Experimental results show that the proposed farthest edit distance based crossover operator is able to preserve the diversity of population and solve the optimization problem more efficiently.

关键词： gene expression programming population diversity Edit distance crossover operator

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Prediction of Software Quality Model Using gene expression programming

Prediction of Software Quality Model Using Gene Expression P...

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10th International Conference on Product-Focused Software Process Improvement

作者： Singh, Yogesh Kaur, Arvinder Malhotra, Ruchika GGS Indraprastha Univ Univ Sch Informat Technol Delhi 110403 India

ISBN: (纸本)9783642021510

There has been number of measurement techniques proposed in the literature. These metrics can be used in assessing quality of software products, thereby controlling costs and schedules. ne empirical validation of object-oriented (OO) metrics is essential to ensure their practical relevance in industrial settings. In this paper, we empirically validate OO metrics given by Chidamber and Kemerer for their ability to predict software quality in terms of fault proneness. In order to analyze these metrics we use gene expression programming (GEP). Here, we explore the ability of OO metrics using defect data for open source software. Further, we develop a software quality metric and suggest ways in which software professional may use this metric for process improvement. We conclude that GEP can be used in detecting fault prone classes. We also conclude that the proposed metric may be effectively used by software managers tin predicting faulty classes in earlier phases of software development.

关键词： Metrics Object-oriented Software Quality Empirical validation Fault prediction gene expression programming

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A New Method of Evolving Boolean Function Based on gene expression programming

A New Method of Evolving Boolean Function Based on Gene Expr...

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IEEE International Conference on Automation and Logistics

作者： Li, Kangshun Xie, Yang Zhang, Wensheng Wang, Feng Jiangxi Univ Sci & Technol Sch Informat Engn Nanchang 341000 Jiangxi Peoples R China Chinese Acad Sci Inst Automat Beijing 100083 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

ISBN: (纸本)9781424425020

Evolutionary Encryption is a new focus in recent research work. The new method of design encryption is to combine evolution algorithm with encryption designing. Boolean Function is very import in encryption. The traditional method of optimizing Boolean function is algebra method which is slow and difficult. We propose an optimization algorithm for the Boolean function based on GEP. Comparing to the other methods, the method is not only fast but also the Boolean function found is very well even the dimension is high.

关键词： Boolean function evolutionary encryption gene expression programming

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An Optimized Clustering Algorithm Using Improved gene expression programming 7th

An Optimized Clustering Algorithm Using Improved Gene Expres...

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7th International Symposium on Intelligence Computation and Applications (ISICA)

作者： Yang, Shuling Li, Kangshun Li, Wei Chen, Weiguang South China Agr Univ Coll Math & Informat Guangzhou 510642 Guangdong Peoples R China Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China

ISBN: (纸本)9789811003561;9789811003554

How to find the better initial center points plays an important role in many clustering applications. In our paper, we propose the novel chromosome representation according to extended traditional gene expression programming used in GEP-ADF. It is aimed at improving the performance of GEP to obtain center points more accurately. Experimental results show that our new algorithm has good performance in clustering and the three real world datasets compared with the other two algorithms.

关键词： Center points Novel chromosome representation gene expression programming GEP-ADF

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The Short-term Load Forecasting Model Based on Improved gene expression programming Arithmetic

The Short-term Load Forecasting Model Based on Improved Gene...

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International Conference on Information Science, Automation and Material System

作者： Li, Yanmei Wang, Jingmin N China Elect Power Univ Sch Business & Adm Baoding 071003 Peoples R China

ISBN: (纸本)9783037850817

Applying the improved gene expression programming arithmetic to optimization plan, the convergence rate and precision of the model can be improved, which can be used to load forecasting. Preprocessing the load sample data, and applying the flexible skills of the improved gene expression programming arithmetic,the paper forecasts the whole point load of future short-term to see the same point load sequence of different work-day as sample. Through a case analysis,the improved gene expression programming arithmetic has been proved to have more efficiency and faster convergence rate than optimization methods.

关键词： gene expression programming mutation operator load forecasting

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Mining Class Contrast Functions by gene expression programming

Mining Class Contrast Functions by Gene Expression Programmi...

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5th International Conference on Advanced Data Mining and Applications

作者： Duan, Lei Tang, Changjie Tang, Liang Zhang, Tianqing Zuo, Jie Sichuan Univ Sch Comp Sci Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9783642033476

Finding functions whose accuracies change significantly between two classes is an interesting work. In this paper, this kind of functions is defined as class contrast functions. As gene expression programming (GEP) can discover essential relations from data and express them mathematically, it is desirable to apply GEP to mining such class contrast functions from data. The main contributions of this paper include: (1) proposing a new data mining task - class contrast function mining, (2) designing a GEP based method to find class contrast functions, (3) presenting several strategies for finding multiple class contrast functions in data, (4) giving an extensive performance study on both synthetic and real world datasets. The experimental results show that the proposed methods are effective. Several class contrast functions are discovered from the real world datasets. Some potential works on class contrast function mining are discussed based on the experimental results.

关键词： gene expression programming Contrast Mining Data Mining

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Space Partition based gene expression programming for Symbolic Regression 19

Space Partition based Gene Expression Programming for Symbol...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Lu, Qiang Zhou, Shuo Tao, Fan Wang, Zhiguang China Univ Petr Beijing Peoples R China

ISBN: (纸本)9781450367486

For the problem of symbolic regression, we propose a novel space partition based gene expression programming (GEP) algorithm named SP-GEP, which helps GEP escape from local optimum and improves the search accuracy of GEP by letting individuals jump efficiently between segmented subspaces and preserving population diversity. It firstly partitions the space of mathematical expressions into k subspaces that are mutually exclusive. Then, in order for individuals to jump efficiently between these subspaces, it uses a subspace selection method, which combines multi-armed bandit and epsilon-greedy strategy. Through experiments on a set of standard SR benchmarks, the results show that the proposed SP-GEP always keeps higher population diversity, and can find more accurate results than canonical GEPs.

关键词： gene expression programming multi-armed bandit symbolic regression evolutionary computation

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Multi-expression Based gene expression programming

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Chinese Intelligent Automation Conference (CIAC)

作者： Deng, Wei He, Pei Huang, Zhi Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Hunan Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China

ISBN: (纸本)9783642384660;9783642384653

Among the variants of GP, GEP stands out for its simplicity of encoding method and MEP catches our attention for its multi-expression capability. In this paper, a novel GP variant-MGEP (Multi-expression based gene expression programming) is proposed to combine these two approaches. The new method preserves the GEP structure, however unlike the traditional GEP, its genes, like those of MEP, can be disassembled into many expressions. Therefore in MGEP, the traditional GEP gene can contain multiple solutions for a problem. The 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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An Efficient Cooperative Co-evolutionary gene expression programming 4

An Efficient Cooperative Co-evolutionary Gene Expression Pro...

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IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)

作者： Cheng, Tiantian Zhong, Jinghui South China Univ Technol Sch Comp Sci & Engn Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538693803

gene expression programming (GEP) is a popular and powerful evolutionary optimization technique for automatic generation of computer programs. In this paper, a Cooperative Co-evolutionary framework is proposed to improve the performance of GEP. The proposed framework consists of three components to find high-quality computer programs. One component focusing on searches for both structures and coefficients of computer programs, while the other two components focus on optimizing the structures and coefficients, respectively. The three components are working cooperatively during the evolution process. The proposed framework is tested on twelve symbolic regression problems and two real-world regression problems. Experimental results demonstrated that the proposed method can offer enhanced performances over two state-of-the-art algorithms in terms of solution accuracy and search efficiency.

关键词： Cooperative Co-evolution gene expression programming genetic programming Symbolic Regression

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Using gene expression programming to develop a combined runoff estimate model from conventional rainfall-runoff model outputs 18

Using gene expression programming to develop a combined runo...

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Combined IMACS World Congress/Modelling and Simulation Society-of-Australia-and-New-Zealand (MSSANZ)/18th Biennial Conference on Modelling and Simulation

作者： Fernando, D. A. K. Shamseldin, A. Y. Abrahart, R. J. Unitec New Zealand Dept Civil Engn Auckland New Zealand

ISBN: (纸本)9780975840078

In previous studies, artificial neural networks have been used to develop a model that combines simulated river flows from several individual rainfall-runoff models e. g. Shamseldin et al., 1997;Abrahart & See, 2002;Shamseldin, O'Connor, & Nasr, 2007. The combined runoff estimate model was found to perform better than the individual models in most of the cases. However, no attempts have been made to explain the inner workings of the combined models or the drivers for their success. The research presented in this study investigates the use of gene expression programming (GEP) to develop a combination rainfall-runoff model through the process of symbolic regression. One of the additional advantages of this approach over the neural combination method is the model's ability to represent itself in the form of mathematical expressions. The GEP model is developed using the daily simulated river flows of four other rainfall runoff models for the Chu catchment which is located in Vietnam. The four models are the linear perturbation model (LPM), the linearly varying gain factor model (LVGFM), the probability-distributed interacting storage capacity (PDISC) model, and the soil moisture accounting and routing (SMAR) models. In this paper, geneXproTools 4.0, a powerful soft computing software package, is used to develop the combined model. The program provides transparent modeling solutions in the sense that it provides the users with the mathematical equation describing the combined model. The results reveal that combination using symbolic regression is successful and that a superior combined model can be developed using outputs from other individual models. The structure of the combined model is also investigated in this study. The results show that the combined model is dominated by input information from the PDISC model forming the baseline estimate, to which different permutations and combinations of the remaining inputs from the other models are added. This research, limit

关键词： gene expression programming Rainfall-runoff models Combination models Over-fitting

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